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Bai X, Chen Z, Chen K, Wu Z, Wang R, Liu J, Chang L, Wen L, Tang F. Simultaneous de novo calling and phasing of genetic variants at chromosome-scale using NanoStrand-seq. Cell Discov 2024; 10:74. [PMID: 38977679 PMCID: PMC11231365 DOI: 10.1038/s41421-024-00694-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 05/23/2024] [Indexed: 07/10/2024] Open
Abstract
The successful accomplishment of the first telomere-to-telomere human genome assembly, T2T-CHM13, marked a milestone in achieving completeness of the human reference genome. The upcoming era of genome study will focus on fully phased diploid genome assembly, with an emphasis on genetic differences between individual haplotypes. Most existing sequencing approaches only achieved localized haplotype phasing and relied on additional pedigree information for further whole-chromosome scale phasing. The short-read-based Strand-seq method is able to directly phase single nucleotide polymorphisms (SNPs) at whole-chromosome scale but falls short when it comes to phasing structural variations (SVs). To shed light on this issue, we developed a Nanopore sequencing platform-based Strand-seq approach, which we named NanoStrand-seq. This method allowed for de novo SNP calling with high precision (99.52%) and acheived a superior phasing accuracy (0.02% Hamming error rate) at whole-chromosome scale, a level of performance comparable to Strand-seq for haplotype phasing of the GM12878 genome. Importantly, we demonstrated that NanoStrand-seq can efficiently resolve the MHC locus, a highly polymorphic genomic region. Moreover, NanoStrand-seq enabled independent direct calling and phasing of deletions and insertions at whole-chromosome level; when applied to long genomic regions of SNP homozygosity, it outperformed the strategy that combined Strand-seq with bulk long-read sequencing. Finally, we showed that, like Strand-seq, NanoStrand-seq was also applicable to primary cultured cells. Together, here we provided a novel methodology that enabled interrogation of a full spectrum of haplotype-resolved SNPs and SVs at whole-chromosome scale, with broad applications for species with diploid or even potentially polypoid genomes.
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Affiliation(s)
- Xiuzhen Bai
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
- Changping Laboratory, Beijing, China
| | - Zonggui Chen
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Changping Laboratory, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Kexuan Chen
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- School of Life Sciences, Peking University, Beijing, China
| | - Zixin Wu
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Rui Wang
- Department of Medicine, Cancer Institute, Stanford University, Stanford, CA, USA
| | - Jun'e Liu
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
- Changping Laboratory, Beijing, China
- School of Life Sciences, Peking University, Beijing, China
| | - Liang Chang
- State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, China
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education Beijing, Beijing, China
- Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China
| | - Lu Wen
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
- Changping Laboratory, Beijing, China
| | - Fuchou Tang
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China.
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China.
- Changping Laboratory, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
- School of Life Sciences, Peking University, Beijing, China.
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2
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Henglin M, Ghareghani M, Harvey W, Porubsky D, Koren S, Eichler EE, Ebert P, Marschall T. Phasing Diploid Genome Assembly Graphs with Single-Cell Strand Sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580432. [PMID: 38529499 PMCID: PMC10962706 DOI: 10.1101/2024.02.15.580432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de-novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de-novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio-phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.
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Affiliation(s)
- Mir Henglin
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
| | - Maryam Ghareghani
- Department of Mathematics and Computer Science, Freie Universität Berlin, Germany
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - William Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
- Core Unit Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
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3
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Lorig-Roach R, Meredith M, Monlong J, Jain M, Olsen HE, McNulty B, Porubsky D, Montague TG, Lucas JK, Condon C, Eizenga JM, Juul S, McKenzie SK, Simmonds SE, Park J, Asri M, Koren S, Eichler EE, Axel R, Martin B, Carnevali P, Miga KH, Paten B. Phased nanopore assembly with Shasta and modular graph phasing with GFAse. Genome Res 2024; 34:454-468. [PMID: 38627094 PMCID: PMC11067879 DOI: 10.1101/gr.278268.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 03/19/2024] [Indexed: 04/30/2024]
Abstract
Reference-free genome phasing is vital for understanding allele inheritance and the impact of single-molecule DNA variation on phenotypes. To achieve thorough phasing across homozygous or repetitive regions of the genome, long-read sequencing technologies are often used to perform phased de novo assembly. As a step toward reducing the cost and complexity of this type of analysis, we describe new methods for accurately phasing Oxford Nanopore Technologies (ONT) sequence data with the Shasta genome assembler and a modular tool for extending phasing to the chromosome scale called GFAse. We test using new variants of ONT PromethION sequencing, including those using proximity ligation, and show that newer, higher accuracy ONT reads substantially improve assembly quality.
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Affiliation(s)
- Ryan Lorig-Roach
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA;
| | - Melissa Meredith
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Jean Monlong
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Miten Jain
- Department of Bioengineering, Department of Physics, Northeastern University, Boston, Massachusetts 02120, USA
| | - Hugh E Olsen
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Brandy McNulty
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA
| | - Tessa G Montague
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, New York 10027, USA
- Howard Hughes Medical Institute, Columbia University, New York, New York 10032, USA
| | - Julian K Lucas
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Chris Condon
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Jordan M Eizenga
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Sissel Juul
- Oxford Nanopore Technologies Incorporated, New York, New York 10013, USA
| | - Sean K McKenzie
- Oxford Nanopore Technologies Incorporated, New York, New York 10013, USA
| | - Sara E Simmonds
- Chan Zuckerberg Initiative Foundation, Redwood City, California 94063, USA
| | - Jimin Park
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Mobin Asri
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA
| | - Richard Axel
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, New York 10027, USA
- Howard Hughes Medical Institute, Columbia University, New York, New York 10032, USA
| | - Bruce Martin
- Chan Zuckerberg Initiative Foundation, Redwood City, California 94063, USA
| | - Paolo Carnevali
- Chan Zuckerberg Initiative Foundation, Redwood City, California 94063, USA;
| | - Karen H Miga
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California 95060, USA;
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4
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Höjer P, Frick T, Siga H, Pourbozorgi P, Aghelpasand H, Martin M, Ahmadian A. BLR: a flexible pipeline for haplotype analysis of multiple linked-read technologies. Nucleic Acids Res 2023; 51:e114. [PMID: 37941142 PMCID: PMC10711428 DOI: 10.1093/nar/gkad1010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 10/04/2023] [Accepted: 10/18/2023] [Indexed: 11/10/2023] Open
Abstract
Linked-read sequencing promises a one-method approach for genome-wide insights including single nucleotide variants (SNVs), structural variants, and haplotyping. We introduce Barcode Linked Reads (BLR), an open-source haplotyping pipeline capable of handling millions of barcodes and data from multiple linked-read technologies including DBS, 10× Genomics, TELL-seq and stLFR. Running BLR on DBS linked-reads yielded megabase-scale phasing with low (<0.2%) switch error rates. Of 13616 protein-coding genes phased in the GIAB benchmark set (v4.2.1), 98.6% matched the BLR phasing. In addition, large structural variants showed concordance with HPRC-HG002 reference assembly calls. Compared to diploid assembly with PacBio HiFi reads, BLR phasing was more continuous when considering switch errors. We further show that integrating long reads at low coverage (∼10×) can improve phasing contiguity and reduce switch errors in tandem repeats. When compared to Long Ranger on 10× Genomics data, BLR showed an increase in phase block N50 with low switch-error rates. For TELL-Seq and stLFR linked reads, BLR generated longer or similar phase block lengths and low switch error rates compared to results presented in the original publications. In conclusion, BLR provides a flexible workflow for comprehensive haplotype analysis of linked reads from multiple platforms.
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Affiliation(s)
- Pontus Höjer
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Tobias Frick
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Humam Siga
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Parham Pourbozorgi
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Hooman Aghelpasand
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Marcel Martin
- Stockholm University, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, SE-171 65, Solna, Sweden
| | - Afshin Ahmadian
- Royal Institute of Technology (KTH), School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, Science for Life Laboratory, SE-171 65, Solna, Sweden
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5
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Ashraf H, Ebler J, Marschall T. Allele detection using k-mer-based sequencing error profiles. BIOINFORMATICS ADVANCES 2023; 3:vbad149. [PMID: 37928341 PMCID: PMC10625474 DOI: 10.1093/bioadv/vbad149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 09/21/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
Motivation For genotype and haplotype inference, typically, sequencing reads aligned to a reference genome are used. The alignments identify the genomic origin of the reads and help to infer the absence or presence of sequence variants in the genome. Since long sequencing reads often come with high rates of systematic sequencing errors, single nucleotides in the reads are not always correctly aligned to the reference genome, which can thus lead to wrong conclusions about the allele carried by a sequencing read at the variant site. Thus, allele detection is not a trivial task, especially for single-nucleotide polymorphisms and indels. Results To learn the characteristics of sequencing errors, we introduce a method to create an error model in non-variant regions of the genome. This information is later used to distinguish sequencing errors from alternative alleles in variant regions. We show that our method, k-merald, improves allele detection accuracy leading to better genotyping performance as compared to the existing WhatsHap implementation using edit-distance-based allele detection, with a decrease of 18% and 24% in error rate for high-coverage Oxford Nanopore and PacBio CLR sequencing reads for sample HG002, respectively. We additionally observed a prominent improvement in genotyping performance for sequencing data with low coverage. For 3× coverage Oxford Nanopore sequencing data, the genotyping error rate reduced from 34% to 31%, corresponding to a 9% decrease. Availability and implementation https://github.com/whatshap/whatshap.
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Affiliation(s)
- Hufsah Ashraf
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Jana Ebler
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, 40225 Düsseldorf, Germany
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6
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Yi D, Nam JW, Jeong H. Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. Brief Bioinform 2023; 24:bbad297. [PMID: 37587831 PMCID: PMC10516374 DOI: 10.1093/bib/bbad297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/05/2023] [Accepted: 07/23/2023] [Indexed: 08/18/2023] Open
Abstract
Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.
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Affiliation(s)
- Dohun Yi
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Research Institute for Convergence of Basic Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyobin Jeong
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
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7
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Xie X, Sun X, Wang Y, Lehner B, Li X. Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles. Nat Commun 2023; 14:5551. [PMID: 37689712 PMCID: PMC10492795 DOI: 10.1038/s41467-023-41188-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/25/2023] [Indexed: 09/11/2023] Open
Abstract
An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a "heteroallelic combination". However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.
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Affiliation(s)
- Xuan Xie
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
| | - Xia Sun
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Yuheng Wang
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Ben Lehner
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, 08003, Spain.
- ICREA, Pg. Luis Companys 23, Barcelona, 08010, Spain.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
| | - Xianghua Li
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK.
- Biomedical and Health Translational Centre of Zhejiang Province, Haizhou East Road 718, Haining, 314400, P. R. China.
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8
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Laufer VA, Glover TW, Wilson TE. Applications of advanced technologies for detecting genomic structural variation. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 792:108475. [PMID: 37931775 PMCID: PMC10792551 DOI: 10.1016/j.mrrev.2023.108475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/07/2023] [Accepted: 11/02/2023] [Indexed: 11/08/2023]
Abstract
Chromosomal structural variation (SV) encompasses a heterogenous class of genetic variants that exerts strong influences on human health and disease. Despite their importance, many structural variants (SVs) have remained poorly characterized at even a basic level, a discrepancy predicated upon the technical limitations of prior genomic assays. However, recent advances in genomic technology can identify and localize SVs accurately, opening new questions regarding SV risk factors and their impacts in humans. Here, we first define and classify human SVs and their generative mechanisms, highlighting characteristics leveraged by various SV assays. We next examine the first-ever gapless assembly of the human genome and the technical process of assembling it, which required third-generation sequencing technologies to resolve structurally complex loci. The new portions of that "telomere-to-telomere" and subsequent pangenome assemblies highlight aspects of SV biology likely to develop in the near-term. We consider the strengths and limitations of the most promising new SV technologies and when they or longstanding approaches are best suited to meeting salient goals in the study of human SV in population-scale genomics research, clinical, and public health contexts. It is a watershed time in our understanding of human SV when new approaches are expected to fundamentally change genomic applications.
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Affiliation(s)
- Vincent A Laufer
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
| | - Thomas W Glover
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
| | - Thomas E Wilson
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
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9
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Leal JL, Milesi P, Salojärvi J, Lascoux M. Phylogenetic Analysis of Allotetraploid Species Using Polarized Genomic Sequences. Syst Biol 2023; 72:372-390. [PMID: 36932679 PMCID: PMC10275558 DOI: 10.1093/sysbio/syad009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 10/14/2022] [Accepted: 03/10/2023] [Indexed: 03/19/2023] Open
Abstract
Phylogenetic analysis of polyploid hybrid species has long posed a formidable challenge as it requires the ability to distinguish between alleles of different ancestral origins in order to disentangle their individual evolutionary history. This problem has been previously addressed by conceiving phylogenies as reticulate networks, using a two-step phasing strategy that first identifies and segregates homoeologous loci and then, during a second phasing step, assigns each gene copy to one of the subgenomes of an allopolyploid species. Here, we propose an alternative approach, one that preserves the core idea behind phasing-to produce separate nucleotide sequences that capture the reticulate evolutionary history of a polyploid-while vastly simplifying its implementation by reducing a complex multistage procedure to a single phasing step. While most current methods used for phylogenetic reconstruction of polyploid species require sequencing reads to be pre-phased using experimental or computational methods-usually an expensive, complex, and/or time-consuming endeavor-phasing executed using our algorithm is performed directly on the multiple-sequence alignment (MSA), a key change that allows for the simultaneous segregation and sorting of gene copies. We introduce the concept of genomic polarization that, when applied to an allopolyploid species, produces nucleotide sequences that capture the fraction of a polyploid genome that deviates from that of a reference sequence, usually one of the other species present in the MSA. We show that if the reference sequence is one of the parental species, the polarized polyploid sequence has a close resemblance (high pairwise sequence identity) to the second parental species. This knowledge is harnessed to build a new heuristic algorithm where, by replacing the allopolyploid genomic sequence in the MSA by its polarized version, it is possible to identify the phylogenetic position of the polyploid's ancestral parents in an iterative process. The proposed methodology can be used with long-read and short-read high-throughput sequencing data and requires only one representative individual for each species to be included in the phylogenetic analysis. In its current form, it can be used in the analysis of phylogenies containing tetraploid and diploid species. We test the newly developed method extensively using simulated data in order to evaluate its accuracy. We show empirically that the use of polarized genomic sequences allows for the correct identification of both parental species of an allotetraploid with up to 97% certainty in phylogenies with moderate levels of incomplete lineage sorting (ILS) and 87% in phylogenies containing high levels of ILS. We then apply the polarization protocol to reconstruct the reticulate histories of Arabidopsis kamchatica and Arabidopsis suecica, two allopolyploids whose ancestry has been well documented. [Allopolyploidy; Arabidopsis; genomic polarization; homoeologs; incomplete lineage sorting; phasing; polyploid phylogenetics; reticulate evolution.].
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Affiliation(s)
- J Luis Leal
- Plant Ecology and Evolution, Department of Ecology and Genetics, Uppsala University, Norbyvägen 18D, 75236 Uppsala, Sweden
| | - Pascal Milesi
- Plant Ecology and Evolution, Department of Ecology and Genetics, Uppsala University, Norbyvägen 18D, 75236 Uppsala, Sweden
- Science for Life Laboratory (SciLifeLab), Uppsala University, 75237 Uppsala, Sweden
| | - Jarkko Salojärvi
- Organismal and Evolutionary Biology Research Program, Faculty of Biological and Environmental Sciences, and Viikki Plant Science Centre, University of Helsinki, P.O. Box 65 (Viikinkaari 1), 00014 Helsinki, Finland
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore
| | - Martin Lascoux
- Plant Ecology and Evolution, Department of Ecology and Genetics, Uppsala University, Norbyvägen 18D, 75236 Uppsala, Sweden
- Science for Life Laboratory (SciLifeLab), Uppsala University, 75237 Uppsala, Sweden
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10
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Lorig-Roach R, Meredith M, Monlong J, Jain M, Olsen H, McNulty B, Porubsky D, Montague T, Lucas J, Condon C, Eizenga J, Juul S, McKenzie S, Simmonds SE, Park J, Asri M, Koren S, Eichler E, Axel R, Martin B, Carnevali P, Miga K, Paten B. Phased nanopore assembly with Shasta and modular graph phasing with GFAse. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529152. [PMID: 36865218 PMCID: PMC9980101 DOI: 10.1101/2023.02.21.529152] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
As a step towards simplifying and reducing the cost of haplotype resolved de novo assembly, we describe new methods for accurately phasing nanopore data with the Shasta genome assembler and a modular tool for extending phasing to the chromosome scale called GFAse. We test using new variants of Oxford Nanopore Technologies' (ONT) PromethION sequencing, including those using proximity ligation and show that newer, higher accuracy ONT reads substantially improve assembly quality.
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Affiliation(s)
- Ryan Lorig-Roach
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Melissa Meredith
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jean Monlong
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Miten Jain
- Department of Bioengineering, Department of Physics, Northeastern University, Boston, MA, USA
| | - Hugh Olsen
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Brandy McNulty
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Tessa Montague
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY, USA & Howard Hughes Medical Institute, Columbia University, New York, NY, USA
| | - Julian Lucas
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Chris Condon
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Jordan Eizenga
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | | | | | | | - Jimin Park
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Mobin Asri
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome & Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Evan Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA & Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Richard Axel
- The Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY, USA & Howard Hughes Medical Institute, Columbia University, New York, NY, USA
| | - Bruce Martin
- Chan Zuckerberg Initiative Foundation, Redwood City, CA, USA
| | - Paolo Carnevali
- Chan Zuckerberg Initiative Foundation, Redwood City, CA, USA
| | - Karen Miga
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
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11
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Chan AP, Choi Y, Rangan A, Zhang G, Podder A, Berens M, Sharma S, Pirrotte P, Byron S, Duggan D, Schork NJ. Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges. Methods Mol Biol 2023; 2590:1-30. [PMID: 36335489 DOI: 10.1007/978-1-0716-2819-5_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Human DNA sequencing protocols have revolutionized human biology, biomedical science, and clinical practice, but still have very important limitations. One limitation is that most protocols do not separate or assemble (i.e., "phase") the nucleotide content of each of the maternally and paternally derived chromosomal homologs making up the 22 autosomal pairs and the chromosomal pair making up the pseudo-autosomal region of the sex chromosomes. This has led to a dearth of studies and a consequent underappreciation of many phenomena of fundamental importance to basic and clinical genomic science. We discuss a few protocols for obtaining phase information as well as their limitations, including those that could be used in tumor phasing settings. We then describe a number of biological and clinical phenomena that require phase information. These include phenomena that require precise knowledge of the nucleotide sequence in a chromosomal segment from germline or somatic cells, such as DNA binding events, and insight into unique cis vs. trans-acting functionally impactful variant combinations-for example, variants implicated in a phenotype governed by compound heterozygosity. In addition, we also comment on the need for reliable and consensus-based diploid-context computational workflows for variant identification as well as the need for laboratory-based functional verification strategies for validating cis vs. trans effects of variant combinations. We also briefly describe available resources, example studies, as well as areas of further research, and ultimately argue that the science behind the study of human diploidy, referred to as "diplomics," which will be enabled by nucleotide-level resolution of phased genomes, is a logical next step in the analysis of human genome biology.
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Affiliation(s)
- Agnes P Chan
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Yongwook Choi
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Aditya Rangan
- Courant Institute of Mathematical Sciences at New York University, New York, NY, USA
| | - Guangfa Zhang
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Avijit Podder
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
| | - Michael Berens
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Sunil Sharma
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Patrick Pirrotte
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Sara Byron
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Dave Duggan
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA
- The City of Hope National Medical Center, Duarte, CA, USA
| | - Nicholas J Schork
- The Translational Genomics Research Institute (TGen), part of the City of Hope National Medical Center, Phoenix, AZ, USA.
- The City of Hope National Medical Center, Duarte, CA, USA.
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12
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Abstract
Dense local haplotypes can now readily be extracted from long-read or droplet-based sequence data. However, these methods struggle to combine subchromosomal haplotype blocks into global chromosome-length haplotypes. Strand-seq is a single cell sequencing technique that uses read orientation to capture sparse global phase information by sequencing only one of two DNA strands for each parental homolog. In combination with dense local haplotypes from other technologies, Strand-seq data can be used to obtain complete chromosome-length phase information. In this chapter, we run the R package StrandPhaseR to phase SNVs using publicly available sequence data for sample HG005 of the Genome in a Bottle project.
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Affiliation(s)
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
| | - Peter M Lansdorp
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
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13
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Duitama J. Phased Genome Assemblies. Methods Mol Biol 2023; 2590:273-286. [PMID: 36335504 DOI: 10.1007/978-1-0716-2819-5_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The ultimate goal of de novo assembly of reads sequenced from a diploid individual is the separate reconstruction of the sequences corresponding to the two copies of each chromosome. Unfortunately, the allele linkage information needed to perform phased genome assemblies has been difficult to generate. Hence, most current genome assemblies are a haploid mixture of the two underlying chromosome copies present in the sequenced individual. Sequencing technologies providing long (20 kb) and accurate reads are the basis to generate phased genome assemblies. This chapter provides a brief overview of the main milestones in traditional genome assembly, focusing on the bioinformatic techniques developed to generate haplotype information from different specialized protocols. Using these techniques as a knowledge background, the chapter reviews the current algorithms to generate phased assemblies from long reads with low error rates. Current techniques perform haplotype-aware error correction steps to increase the quality of the raw reads. In addition, variations on the traditional overlap-layout-consensus (OLC) graph have been developed in an effort to eliminate edges between reads sequenced from different chromosome copies. This allows for large presence-absence variants between the chromosome copies to be taken into account. The development of these algorithms, along with the improved sequencing technologies has been crucial to finish chromosome-level assemblies of complex genomes.
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Affiliation(s)
- Jorge Duitama
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia.
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14
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Akbari V, Hanlon VC, O’Neill K, Lefebvre L, Schrader KA, Lansdorp PM, Jones SJ. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq. CELL GENOMICS 2022; 3:100233. [PMID: 36777186 PMCID: PMC9903809 DOI: 10.1016/j.xgen.2022.100233] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/08/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Abstract
Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all human autosomes without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds. The parent of origin was correctly inferred for all autosomes with an average mismatch error rate of 0.31% for SNVs and 1.89% for insertions or deletions (indels). Because our method can determine whether an inherited disease allele originated from the mother or the father, we predict that it will improve the diagnosis and management of many genetic diseases.
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Affiliation(s)
- Vahid Akbari
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada,Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Kieran O’Neill
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Louis Lefebvre
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Kasmintan A. Schrader
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Peter M. Lansdorp
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Terry Fox Laboratory, BC Cancer, Vancouver, BC, Canada,Corresponding author
| | - Steven J.M. Jones
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada,Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Corresponding author
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15
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Bassi C, Guerriero P, Pierantoni M, Callegari E, Sabbioni S. Novel Virus Identification through Metagenomics: A Systematic Review. LIFE (BASEL, SWITZERLAND) 2022; 12:life12122048. [PMID: 36556413 PMCID: PMC9784588 DOI: 10.3390/life12122048] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/25/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Metagenomic Next Generation Sequencing (mNGS) allows the evaluation of complex microbial communities, avoiding isolation and cultivation of each microbial species, and does not require prior knowledge of the microbial sequences present in the sample. Applications of mNGS include virome characterization, new virus discovery and full-length viral genome reconstruction, either from virus preparations enriched in culture or directly from clinical and environmental specimens. Here, we systematically reviewed studies that describe novel virus identification through mNGS from samples of different origin (plant, animal and environment). Without imposing time limits to the search, 379 publications were identified that met the search parameters. Sample types, geographical origin, enrichment and nucleic acid extraction methods, sequencing platforms, bioinformatic analytical steps and identified viral families were described. The review highlights mNGS as a feasible method for novel virus discovery from samples of different origins, describes which kind of heterogeneous experimental and analytical protocols are currently used and provides useful information such as the different commercial kits used for the purification of nucleic acids and bioinformatics analytical pipelines.
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Affiliation(s)
- Cristian Bassi
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy
- Laboratorio per Le Tecnologie delle Terapie Avanzate (LTTA), University of Ferrara, 44121 Ferrara, Italy
| | - Paola Guerriero
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy
- Laboratorio per Le Tecnologie delle Terapie Avanzate (LTTA), University of Ferrara, 44121 Ferrara, Italy
| | - Marina Pierantoni
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Elisa Callegari
- Department of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy
| | - Silvia Sabbioni
- Laboratorio per Le Tecnologie delle Terapie Avanzate (LTTA), University of Ferrara, 44121 Ferrara, Italy
- Department of Life Science and Biotechnology, University of Ferrara, 44121 Ferrara, Italy
- Correspondence: ; Tel.: +39-053-245-5319
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16
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Jarvis ED, Formenti G, Rhie A, Guarracino A, Yang C, Wood J, Tracey A, Thibaud-Nissen F, Vollger MR, Porubsky D, Cheng H, Asri M, Logsdon GA, Carnevali P, Chaisson MJP, Chin CS, Cody S, Collins J, Ebert P, Escalona M, Fedrigo O, Fulton RS, Fulton LL, Garg S, Gerton JL, Ghurye J, Granat A, Green RE, Harvey W, Hasenfeld P, Hastie A, Haukness M, Jaeger EB, Jain M, Kirsche M, Kolmogorov M, Korbel JO, Koren S, Korlach J, Lee J, Li D, Lindsay T, Lucas J, Luo F, Marschall T, Mitchell MW, McDaniel J, Nie F, Olsen HE, Olson ND, Pesout T, Potapova T, Puiu D, Regier A, Ruan J, Salzberg SL, Sanders AD, Schatz MC, Schmitt A, Schneider VA, Selvaraj S, Shafin K, Shumate A, Stitziel NO, Stober C, Torrance J, Wagner J, Wang J, Wenger A, Xiao C, Zimin AV, Zhang G, Wang T, Li H, Garrison E, Haussler D, Hall I, Zook JM, Eichler EE, Phillippy AM, Paten B, Howe K, Miga KH. Semi-automated assembly of high-quality diploid human reference genomes. Nature 2022; 611:519-531. [PMID: 36261518 PMCID: PMC9668749 DOI: 10.1038/s41586-022-05325-5] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 09/06/2022] [Indexed: 01/01/2023]
Abstract
The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society1,2. However, it still has many gaps and errors, and does not represent a biological genome as it is a blend of multiple individuals3,4. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome5. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity6. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements.
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Affiliation(s)
- Erich D. Jarvis
- grid.134907.80000 0001 2166 1519Vertebrate Genome Laboratory, The Rockefeller University, New York, NY USA ,grid.413575.10000 0001 2167 1581Howard Hughes Medical Institute, Chevy Chase, MD USA
| | - Giulio Formenti
- grid.134907.80000 0001 2166 1519Vertebrate Genome Laboratory, The Rockefeller University, New York, NY USA
| | - Arang Rhie
- grid.94365.3d0000 0001 2297 5165Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Andrea Guarracino
- grid.510779.d0000 0004 9414 6915Genomics Research Centre, Human Technopole, Viale Rita Levi-Montalcini, Milan, Italy
| | - Chentao Yang
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China
| | - Jonathan Wood
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Alan Tracey
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Francoise Thibaud-Nissen
- grid.94365.3d0000 0001 2297 5165National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD USA
| | - Mitchell R. Vollger
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - David Porubsky
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Haoyu Cheng
- grid.65499.370000 0001 2106 9910Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Mobin Asri
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Glennis A. Logsdon
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Paolo Carnevali
- grid.507326.50000 0004 6090 4941Chan Zuckerberg Initiative, Redwood City, CA USA
| | - Mark J. P. Chaisson
- grid.42505.360000 0001 2156 6853Quantitative and Computational Biology, University of Southern California, Los Angeles, CA USA
| | | | - Sarah Cody
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA
| | - Joanna Collins
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Peter Ebert
- grid.411327.20000 0001 2176 9917Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Merly Escalona
- grid.205975.c0000 0001 0740 6917Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA USA
| | - Olivier Fedrigo
- grid.134907.80000 0001 2166 1519Vertebrate Genome Laboratory, The Rockefeller University, New York, NY USA
| | - Robert S. Fulton
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA
| | - Lucinda L. Fulton
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA
| | - Shilpa Garg
- grid.5254.60000 0001 0674 042XDepartment of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jennifer L. Gerton
- grid.250820.d0000 0000 9420 1591Stowers Institute for Medical Research, Kansas City, MO USA
| | - Jay Ghurye
- grid.504403.6Dovetail Genomics, Scotts Valley, CA USA
| | | | - Richard E. Green
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - William Harvey
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Patrick Hasenfeld
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Alex Hastie
- grid.470262.50000 0004 0473 1353Bionano Genomics, San Diego, CA USA
| | - Marina Haukness
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Erich B. Jaeger
- grid.185669.50000 0004 0507 3954Illumina, Inc., San Diego, CA USA
| | - Miten Jain
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Melanie Kirsche
- grid.21107.350000 0001 2171 9311Department of Computer Science, Johns Hopkins University, Baltimore, MD USA
| | - Mikhail Kolmogorov
- grid.266100.30000 0001 2107 4242Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
| | - Jan O. Korbel
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sergey Koren
- grid.94365.3d0000 0001 2297 5165Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Jonas Korlach
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Joyce Lee
- grid.470262.50000 0004 0473 1353Bionano Genomics, San Diego, CA USA
| | - Daofeng Li
- grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO USA
| | - Tina Lindsay
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA
| | - Julian Lucas
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Feng Luo
- grid.26090.3d0000 0001 0665 0280School of Computing, Clemson University, Clemson, SC USA
| | - Tobias Marschall
- grid.411327.20000 0001 2176 9917Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Matthew W. Mitchell
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ USA
| | - Jennifer McDaniel
- grid.94225.38000000012158463XMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Fan Nie
- grid.216417.70000 0001 0379 7164Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hugh E. Olsen
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Nathan D. Olson
- grid.94225.38000000012158463XMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Trevor Pesout
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Tamara Potapova
- grid.250820.d0000 0000 9420 1591Stowers Institute for Medical Research, Kansas City, MO USA
| | - Daniela Puiu
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Allison Regier
- grid.511991.40000 0004 4910 5831DNAnexus, Mountain View, CA USA
| | - Jue Ruan
- grid.410727.70000 0001 0526 1937Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Steven L. Salzberg
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Ashley D. Sanders
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michael C. Schatz
- grid.21107.350000 0001 2171 9311Department of Computer Science, Johns Hopkins University, Baltimore, MD USA
| | | | - Valerie A. Schneider
- grid.94365.3d0000 0001 2297 5165National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD USA
| | | | - Kishwar Shafin
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Alaina Shumate
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Nathan O. Stitziel
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Cardiovascular Division, John T. Milliken Department of Internal Medicine, Washington University School of Medicine, St. Louis, USA
| | - Catherine Stober
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - James Torrance
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Justin Wagner
- grid.94225.38000000012158463XMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Jianxin Wang
- grid.216417.70000 0001 0379 7164Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Aaron Wenger
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Chuanle Xiao
- grid.12981.330000 0001 2360 039XState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Aleksey V. Zimin
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Guojie Zhang
- grid.13402.340000 0004 1759 700XCenter for Evolutionary & Organismal Biology, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Wang
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO USA
| | - Heng Li
- grid.65499.370000 0001 2106 9910Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA
| | - Erik Garrison
- grid.267301.10000 0004 0386 9246Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN USA
| | - David Haussler
- grid.413575.10000 0001 2167 1581Howard Hughes Medical Institute, Chevy Chase, MD USA ,grid.205975.c0000 0001 0740 6917Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA USA
| | - Ira Hall
- grid.47100.320000000419368710Yale School of Medicine, New Haven, CT USA
| | - Justin M. Zook
- grid.94225.38000000012158463XMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Evan E. Eichler
- grid.413575.10000 0001 2167 1581Howard Hughes Medical Institute, Chevy Chase, MD USA ,grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Adam M. Phillippy
- grid.94365.3d0000 0001 2297 5165Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Benedict Paten
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Kerstin Howe
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Karen H. Miga
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
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17
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Porubsky D, Höps W, Ashraf H, Hsieh P, Rodriguez-Martin B, Yilmaz F, Ebler J, Hallast P, Maria Maggiolini FA, Harvey WT, Henning B, Audano PA, Gordon DS, Ebert P, Hasenfeld P, Benito E, Zhu Q, Lee C, Antonacci F, Steinrücken M, Beck CR, Sanders AD, Marschall T, Eichler EE, Korbel JO. Recurrent inversion polymorphisms in humans associate with genetic instability and genomic disorders. Cell 2022; 185:1986-2005.e26. [PMID: 35525246 PMCID: PMC9563103 DOI: 10.1016/j.cell.2022.04.017] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/14/2022] [Accepted: 04/08/2022] [Indexed: 12/13/2022]
Abstract
Unlike copy number variants (CNVs), inversions remain an underexplored genetic variation class. By integrating multiple genomic technologies, we discover 729 inversions in 41 human genomes. Approximately 85% of inversions <2 kbp form by twin-priming during L1 retrotransposition; 80% of the larger inversions are balanced and affect twice as many nucleotides as CNVs. Balanced inversions show an excess of common variants, and 72% are flanked by segmental duplications (SDs) or retrotransposons. Since flanking repeats promote non-allelic homologous recombination, we developed complementary approaches to identify recurrent inversion formation. We describe 40 recurrent inversions encompassing 0.6% of the genome, showing inversion rates up to 2.7 × 10-4 per locus per generation. Recurrent inversions exhibit a sex-chromosomal bias and co-localize with genomic disorder critical regions. We propose that inversion recurrence results in an elevated number of heterozygous carriers and structural SD diversity, which increases mutability in the population and predisposes specific haplotypes to disease-causing CNVs.
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18
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Wagner J, Olson ND, Harris L, Khan Z, Farek J, Mahmoud M, Stankovic A, Kovacevic V, Yoo B, Miller N, Rosenfeld JA, Ni B, Zarate S, Kirsche M, Aganezov S, Schatz MC, Narzisi G, Byrska-Bishop M, Clarke W, Evani US, Markello C, Shafin K, Zhou X, Sidow A, Bansal V, Ebert P, Marschall T, Lansdorp P, Hanlon V, Mattsson CA, Barrio AM, Fiddes IT, Xiao C, Fungtammasan A, Chin CS, Wenger AM, Rowell WJ, Sedlazeck FJ, Carroll A, Salit M, Zook JM. Benchmarking challenging small variants with linked and long reads. CELL GENOMICS 2022; 2:100128. [PMID: 36452119 PMCID: PMC9706577 DOI: 10.1016/j.xgen.2022.100128] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Genome in a Bottle benchmarks are widely used to help validate clinical sequencing pipelines and develop variant calling and sequencing methods. Here we use accurate linked and long reads to expand benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are challenging for short reads. These benchmarks add more than 300,000 SNVs and 50,000 insertions or deletions (indels) and include 16% more exonic variants, many in challenging, clinically relevant genes not covered previously, such as PMS2. For HG002, we include 92% of the autosomal GRCh38 assembly while excluding regions problematic for benchmarking small variants, such as copy number variants, that should not have been in the previous version, which included 85% of GRCh38. It identifies eight times more false negatives in a short read variant call set relative to our previous benchmark. We demonstrate that this benchmark reliably identifies false positives and false negatives across technologies, enabling ongoing methods development.
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Affiliation(s)
- Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8312, Gaithersburg, MD 20899, USA
- Corresponding author
| | - Nathan D. Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8312, Gaithersburg, MD 20899, USA
| | - Lindsay Harris
- Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8312, Gaithersburg, MD 20899, USA
| | - Ziad Khan
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Jesse Farek
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Ana Stankovic
- Seven Bridges, Omladinskih brigada 90g, 11070 Belgrade, Republic of Serbia
| | - Vladimir Kovacevic
- Seven Bridges, Omladinskih brigada 90g, 11070 Belgrade, Republic of Serbia
| | - Byunggil Yoo
- Children’s Mercy Kansas City, Kansas City, MO, USA
| | - Neil Miller
- Children’s Mercy Kansas City, Kansas City, MO, USA
| | | | - Bohan Ni
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Samantha Zarate
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Kirsche
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Aganezov
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Giuseppe Narzisi
- New York Genome Center, 101 Avenue of the Americas, New York, NY, USA
| | | | - Wayne Clarke
- New York Genome Center, 101 Avenue of the Americas, New York, NY, USA
| | - Uday S. Evani
- New York Genome Center, 101 Avenue of the Americas, New York, NY, USA
| | - Charles Markello
- University of California at Santa Cruz Genomics Institute, 1156 High Street, Santa Cruz, CA, USA
| | - Kishwar Shafin
- University of California at Santa Cruz Genomics Institute, 1156 High Street, Santa Cruz, CA, USA
| | - Xin Zhou
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Arend Sidow
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Vikas Bansal
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Peter Ebert
- Institute of Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Tobias Marschall
- Institute of Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Peter Lansdorp
- Institute of Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Vincent Hanlon
- Terry Fox Laboratory, BC Cancer Research Institute and Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Carl-Adam Mattsson
- Terry Fox Laboratory, BC Cancer Research Institute and Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | | | | | - Chunlin Xiao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | | | | | | | | | - Fritz J. Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Andrew Carroll
- Google Inc., 1600 Amphitheatre Pkwy., Mountain View, CA 94040, USA
| | - Marc Salit
- Joint Initiative for Metrology in Biology, SLAC National Laboratory, Stanford, CA, USA
| | - Justin M. Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8312, Gaithersburg, MD 20899, USA
- Corresponding author
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19
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Noyes MD, Harvey WT, Porubsky D, Sulovari A, Li R, Rose NR, Audano PA, Munson KM, Lewis AP, Hoekzema K, Mantere T, Graves-Lindsay TA, Sanders AD, Goodwin S, Kramer M, Mokrab Y, Zody MC, Hoischen A, Korbel JO, McCombie WR, Eichler EE. Familial long-read sequencing increases yield of de novo mutations. Am J Hum Genet 2022; 109:631-646. [PMID: 35290762 DOI: 10.1016/j.ajhg.2022.02.014] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/16/2022] [Indexed: 12/11/2022] Open
Abstract
Studies of de novo mutation (DNM) have typically excluded some of the most repetitive and complex regions of the genome because these regions cannot be unambiguously mapped with short-read sequencing data. To better understand the genome-wide pattern of DNM, we generated long-read sequence data from an autism parent-child quad with an affected female where no pathogenic variant had been discovered in short-read Illumina sequence data. We deeply sequenced all four individuals by using three sequencing platforms (Illumina, Oxford Nanopore, and Pacific Biosciences) and three complementary technologies (Strand-seq, optical mapping, and 10X Genomics). Using long-read sequencing, we initially discovered and validated 171 DNMs across two children-a 20% increase in the number of de novo single-nucleotide variants (SNVs) and indels when compared to short-read callsets. The number of DNMs further increased by 5% when considering a more complete human reference (T2T-CHM13) because of the recovery of events in regions absent from GRCh38 (e.g., three DNMs in heterochromatic satellites). In total, we validated 195 de novo germline mutations and 23 potential post-zygotic mosaic mutations across both children; the overall true substitution rate based on this integrated callset is at least 1.41 × 10-8 substitutions per nucleotide per generation. We also identified six de novo insertions and deletions in tandem repeats, two of which represent structural variants. We demonstrate that long-read sequencing and assembly, especially when combined with a more complete reference genome, increases the number of DNMs by >25% compared to previous studies, providing a more complete catalog of DNM compared to short-read data alone.
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Affiliation(s)
- Michelle D Noyes
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Ruiyang Li
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Nicholas R Rose
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Tuomo Mantere
- Department of Human Genetics, Radboud University Medical Center, 6500 Nijmegen, the Netherlands; Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit and Biocenter Oulu, University of Oulu, 90220 Oulu, Finland
| | | | - Ashley D Sanders
- European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany
| | - Sara Goodwin
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Melissa Kramer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Younes Mokrab
- Department of Human Genetics, Sidra Medicine, PO Box 26999, Doha, Qatar; Weill Cornell Medicine, PO Box 24144, Doha, Qatar; College of Health and Life Sciences, Hamad Bin Khalifa University, PO Box 34110, Doha, Qatar
| | | | - Alexander Hoischen
- Department of Human Genetics, Radboud University Medical Center, 6500 Nijmegen, the Netherlands; Radboud Institute of Medical Life Sciences and Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6500 Nijmegen, the Netherlands
| | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany
| | - W Richard McCombie
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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20
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Hanlon VC, Chan DD, Hamadeh Z, Wang Y, Mattsson CA, Spierings DC, Coope RJ, Lansdorp PM. Construction of Strand-seq libraries in open nanoliter arrays. CELL REPORTS METHODS 2022; 2:100150. [PMID: 35474869 PMCID: PMC9017222 DOI: 10.1016/j.crmeth.2021.100150] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/22/2021] [Accepted: 12/17/2021] [Indexed: 12/22/2022]
Abstract
Single-cell Strand-seq generates directional genomic information to study DNA repair, assemble genomes, and map structural variation onto chromosome-length haplotypes. We report a nanoliter-volume, one-pot (OP) Strand-seq library preparation protocol in which reagents are added cumulatively, DNA purification steps are avoided, and enzymes are inactivated with a thermolabile protease. OP-Strand-seq libraries capture 10%-25% of the genome from a single-cell with reduced costs and increased throughput.
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Affiliation(s)
| | - Daniel D. Chan
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
| | - Zeid Hamadeh
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
| | - Yanni Wang
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
| | | | - Diana C.J. Spierings
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, the Netherlands
| | - Robin J.N. Coope
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
| | - Peter M. Lansdorp
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV Groningen, the Netherlands
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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21
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Carioscia SA, Weaver KJ, Bortvin AN, Pan H, Ariad D, Bell AD, McCoy RC. A method for low-coverage single-gamete sequence analysis demonstrates adherence to Mendel's first law across a large sample of human sperm. eLife 2022; 11:76383. [PMID: 36475543 PMCID: PMC9844984 DOI: 10.7554/elife.76383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/05/2022] [Indexed: 12/12/2022] Open
Abstract
Recently published single-cell sequencing data from individual human sperm (n=41,189; 969-3377 cells from each of 25 donors) offer an opportunity to investigate questions of inheritance with improved statistical power, but require new methods tailored to these extremely low-coverage data (∼0.01× per cell). To this end, we developed a method, named rhapsodi, that leverages sparse gamete genotype data to phase the diploid genomes of the donor individuals, impute missing gamete genotypes, and discover meiotic recombination breakpoints, benchmarking its performance across a wide range of study designs. We then applied rhapsodi to the sperm sequencing data to investigate adherence to Mendel's Law of Segregation, which states that the offspring of a diploid, heterozygous parent will inherit either allele with equal probability. While the vast majority of loci adhere to this rule, research in model and non-model organisms has uncovered numerous exceptions whereby 'selfish' alleles are disproportionately transmitted to the next generation. Evidence of such 'transmission distortion' (TD) in humans remains equivocal in part because scans of human pedigrees have been under-powered to detect small effects. After applying rhapsodi to the sperm data and scanning for evidence of TD, our results exhibited close concordance with binomial expectations under balanced transmission. Together, our work demonstrates that rhapsodi can facilitate novel uses of inferred genotype data and meiotic recombination events, while offering a powerful quantitative framework for testing for TD in other cohorts and study systems.
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Affiliation(s)
- Sara A Carioscia
- Department of Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Kathryn J Weaver
- Department of Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Andrew N Bortvin
- Department of Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Hao Pan
- Department of Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Daniel Ariad
- Department of Biology, Johns Hopkins UniversityBaltimoreUnited States
| | - Avery Davis Bell
- School of Biological Sciences, Georgia Institute of TechnologyAtlantaUnited States
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins UniversityBaltimoreUnited States
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22
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Zverinova S, Guryev V. Variant calling: Considerations, practices, and developments. Hum Mutat 2021; 43:976-985. [PMID: 34882898 PMCID: PMC9545713 DOI: 10.1002/humu.24311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 11/02/2021] [Accepted: 12/03/2021] [Indexed: 11/10/2022]
Abstract
The success of many clinical, association, or population genetics studies critically relies on properly performed variant calling step. The variety of modern genomics protocols, techniques, and platforms makes our choices of methods and algorithms difficult and there is no "one size fits all" solution for study design and data analysis. In this review, we discuss considerations that need to be taken into account while designing the study and preparing for the experiments. We outline the variety of variant types that can be detected using sequencing approaches and highlight some specific requirements and basic principles of their detection. Finally, we cover interesting developments that enable variant calling for a broad range of applications in the genomics field. We conclude by discussing technological and algorithmic advances that have the potential to change the ways of calling DNA variants in the nearest future.
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Affiliation(s)
- Stepanka Zverinova
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
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23
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Long-read technologies identify a hidden inverted duplication in a family with choroideremia. HGG ADVANCES 2021; 2:100046. [PMID: 35047838 PMCID: PMC8756506 DOI: 10.1016/j.xhgg.2021.100046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/01/2021] [Indexed: 12/03/2022] Open
Abstract
The lack of molecular diagnoses in rare genetic diseases can be explained by limitations of current standard genomic technologies. Upcoming long-read techniques have complementary strengths to overcome these limitations, with a particular strength in identifying structural variants. By using optical genome mapping and long-read sequencing, we aimed to identify the pathogenic variant in a large family with X-linked choroideremia. In this family, aberrant splicing of exon 12 of the choroideremia gene CHM was detected in 2003, but the underlying genomic defect remained elusive. Optical genome mapping and long-read sequencing approaches now revealed an intragenic 1,752 bp inverted duplication including exon 12 and surrounding regions, located downstream of the wild-type copy of exon 12. Both breakpoint junctions were confirmed with Sanger sequencing and segregate with the X-linked inheritance in the family. The breakpoint junctions displayed sequence microhomology suggestive for an erroneous replication mechanism as the origin of the structural variant. The inverted duplication is predicted to result in a hairpin formation of the pre-mRNA with the wild-type exon 12, leading to exon skipping in the mature mRNA. The identified inverted duplication is deemed the hidden pathogenic cause of disease in this family. Our study shows that optical genome mapping and long-read sequencing have significant potential for the identification of (hidden) structural variants in rare genetic diseases.
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24
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Using de novo assembly to identify structural variation of eight complex immune system gene regions. PLoS Comput Biol 2021; 17:e1009254. [PMID: 34343164 PMCID: PMC8363018 DOI: 10.1371/journal.pcbi.1009254] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 08/13/2021] [Accepted: 07/06/2021] [Indexed: 12/11/2022] Open
Abstract
Driven by the necessity to survive environmental pathogens, the human immune system has evolved exceptional diversity and plasticity, to which several factors contribute including inheritable structural polymorphism of the underlying genes. Characterizing this variation is challenging due to the complexity of these loci, which contain extensive regions of paralogy, segmental duplication and high copy-number repeats, but recent progress in long-read sequencing and optical mapping techniques suggests this problem may now be tractable. Here we assess this by using long-read sequencing platforms from PacBio and Oxford Nanopore, supplemented with short-read sequencing and Bionano optical mapping, to sequence DNA extracted from CD14+ monocytes and peripheral blood mononuclear cells from a single European individual identified as HV31. We use this data to build a de novo assembly of eight genomic regions encoding four key components of the immune system, namely the human leukocyte antigen, immunoglobulins, T cell receptors, and killer-cell immunoglobulin-like receptors. Validation of our assembly using k-mer based and alignment approaches suggests that it has high accuracy, with estimated base-level error rates below 1 in 10 kb, although we identify a small number of remaining structural errors. We use the assembly to identify heterozygous and homozygous structural variation in comparison to GRCh38. Despite analyzing only a single individual, we find multiple large structural variants affecting core genes at all three immunoglobulin regions and at two of the three T cell receptor regions. Several of these variants are not accurately callable using current algorithms, implying that further methodological improvements are needed. Our results demonstrate that assessing haplotype variation in these regions is possible given sufficiently accurate long-read and associated data. Continued reductions in the cost of these technologies will enable application of these methods to larger samples and provide a broader catalogue of germline structural variation at these loci, an important step toward making these regions accessible to large-scale genetic association studies. The human immune system is incredibly versatile underlying its capacity to defend the body against thousands of pathogens. At a molecular level, it recognizes pathogens using large libraries of antibodies and related protein receptors. These molecules are encoded by gene families that are particularly difficult to analyze due to their unusually complex patterns of similarities and differences between genes and individuals. To overcome this, we applied several sequencing methods to DNA from a single individual and developed methods to reconstruct the underlying sequence at eight of the immune-associated regions. Importantly, we used DNA extracted from monocytes to avoid capturing the further rearrangements that occur in active immune cells. We generated accurate assemblies by integrating multiple complementary data types, although we noted a small subset of locations that remain challenging. Moreover, we found that this individual contains multiple structural differences between the two inherited chromosomes and compared to previously analyzed genomes, affecting the copy number of immune system genes. Application of these methods in larger numbers of individuals will clearly uncover much more variation than is currently known, and might lead to new understanding of the effect of genetic variation on the broad range of human diseases determined by the immune response.
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25
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Hanlon VCT, Mattsson CA, Spierings DCJ, Guryev V, Lansdorp PM. InvertypeR: Bayesian inversion genotyping with Strand-seq data. BMC Genomics 2021; 22:582. [PMID: 34332539 PMCID: PMC8325862 DOI: 10.1186/s12864-021-07892-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 07/15/2021] [Indexed: 11/23/2022] Open
Abstract
Background Single cell Strand-seq is a unique tool for the discovery and phasing of genomic inversions. Conventional methods to discover inversions with Strand-seq data are blind to known inversion locations, limiting their statistical power for the detection of inversions smaller than 10 Kb. Moreover, the methods rely on manual inspection to separate false and true positives. Results Here we describe “InvertypeR”, a method based on a Bayesian binomial model that genotypes inversions using fixed genomic coordinates. We validated InvertypeR by re-genotyping inversions reported for three trios by the Human Genome Structural Variation Consortium. Although 6.3% of the family inversion genotypes in the original study showed Mendelian discordance, this was reduced to 0.5% using InvertypeR. By applying InvertypeR to published inversion coordinates and predicted inversion hotspots (n = 3701), as well as coordinates from conventional inversion discovery, we furthermore genotyped 66 inversions not previously reported for the three trios. Conclusions InvertypeR discovers, genotypes, and phases inversions without relying on manual inspection. For greater accessibility, results are presented as phased chromosome ideograms with inversions linked to Strand-seq data in the genome browser. InvertypeR increases the power of Strand-seq for studies on the role of inversions in phenotypic variation, genome instability, and human disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07892-9.
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Affiliation(s)
- Vincent C T Hanlon
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada.
| | - Carl-Adam Mattsson
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada
| | - Diana C J Spierings
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands
| | - Peter M Lansdorp
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada.,European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands.,Departments of Medical Genetics and Hematology, University of British Columbia, Vancouver, British Columbia, V6T 1Z4, Canada
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26
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Lu TY, Chaisson MJP. Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs. Nat Commun 2021; 12:4250. [PMID: 34253730 PMCID: PMC8275641 DOI: 10.1038/s41467-021-24378-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 06/10/2021] [Indexed: 12/11/2022] Open
Abstract
Variable number tandem repeats (VNTRs) are composed of consecutive repetitive DNA with hypervariable repeat count and composition. They include protein coding sequences and associations with clinical disorders. It has been difficult to incorporate VNTR analysis in disease studies that use short-read sequencing because the traditional approach of mapping to the human reference is less effective for repetitive and divergent sequences. In this work, we solve VNTR mapping for short reads with a repeat-pangenome graph (RPGG), a data structure that encodes both the population diversity and repeat structure of VNTR loci from multiple haplotype-resolved assemblies. We develop software to build a RPGG, and use the RPGG to estimate VNTR composition with short reads. We use this to discover VNTRs with length stratified by continental population, and expression quantitative trait loci, indicating that RPGG analysis of VNTRs will be critical for future studies of diversity and disease.
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Affiliation(s)
- Tsung-Yu Lu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
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27
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Li R, Qu H, Chen J, Wang S, Chater JM, Zhang L, Wei J, Zhang YM, Xu C, Zhong WD, Zhu J, Lu J, Feng Y, Chen W, Ma R, Ferrante SP, Roose ML, Jia Z. Inference of Chromosome-Length Haplotypes Using Genomic Data of Three or a Few More Single Gametes. Mol Biol Evol 2021; 37:3684-3698. [PMID: 32668004 PMCID: PMC7743722 DOI: 10.1093/molbev/msaa176] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Compared with genomic data of individual markers, haplotype data provide higher resolution for DNA variants, advancing our knowledge in genetics and evolution. Although many computational and experimental phasing methods have been developed for analyzing diploid genomes, it remains challenging to reconstruct chromosome-scale haplotypes at low cost, which constrains the utility of this valuable genetic resource. Gamete cells, the natural packaging of haploid complements, are ideal materials for phasing entire chromosomes because the majority of the haplotypic allele combinations has been preserved. Therefore, compared with the current diploid-based phasing methods, using haploid genomic data of single gametes may substantially reduce the complexity in inferring the donor’s chromosomal haplotypes. In this study, we developed the first easy-to-use R package, Hapi, for inferring chromosome-length haplotypes of individual diploid genomes with only a few gametes. Hapi outperformed other phasing methods when analyzing both simulated and real single gamete cell sequencing data sets. The results also suggested that chromosome-scale haplotypes may be inferred by using as few as three gametes, which has pushed the boundary to its possible limit. The single gamete cell sequencing technology allied with the cost-effective Hapi method will make large-scale haplotype-based genetic studies feasible and affordable, promoting the use of haplotype data in a wide range of research.
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Affiliation(s)
- Ruidong Li
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA.,Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, Riverside, CA
| | - Han Qu
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA
| | - Jinfeng Chen
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA
| | - Shibo Wang
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA
| | - John M Chater
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA
| | - Le Zhang
- Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, Riverside, CA
| | - Julong Wei
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA.,Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI
| | - Yuan-Ming Zhang
- Statistical Genomics Lab, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Chenwu Xu
- Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou, China
| | - Wei-De Zhong
- Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Jianguo Zhu
- Department of Urology, Guizhou Provincial People's Hospital, Guizhou, China
| | - Jianming Lu
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA.,Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yuanfa Feng
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA.,Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Weiming Chen
- Department of Urology, Guizhou Provincial People's Hospital, Guizhou, China
| | - Renyuan Ma
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA.,Department of Mathematics, Bowdoin College, Brunswick, ME
| | - Sergio Pietro Ferrante
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA
| | - Mikeal L Roose
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA.,Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, Riverside, CA
| | - Zhenyu Jia
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA.,Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, Riverside, CA
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28
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Determination of complete chromosomal haplotypes by bulk DNA sequencing. Genome Biol 2021; 22:139. [PMID: 33957932 PMCID: PMC8101039 DOI: 10.1186/s13059-021-02330-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 03/25/2021] [Indexed: 01/02/2023] Open
Abstract
Haplotype phase represents the collective genetic variation between homologous chromosomes and is an essential feature of non-haploid genomes. Here we describe a computational strategy to reliably determine complete whole-chromosome haplotypes using a combination of bulk long-range sequencing and Hi-C sequencing. We demonstrate that this strategy can resolve the haplotypes of parental chromosomes in diploid human genomes with high precision (>99%) and completeness (>98%) and assemble the syntenic structure of rearranged chromosomes in aneuploid cancer genomes at base pair level resolution. Our work enables direct interrogation of chromosome-specific alterations and chromatin reorganization using bulk DNA sequencing.
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29
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Kronenberg ZN, Rhie A, Koren S, Concepcion GT, Peluso P, Munson KM, Porubsky D, Kuhn K, Mueller KA, Low WY, Hiendleder S, Fedrigo O, Liachko I, Hall RJ, Phillippy AM, Eichler EE, Williams JL, Smith TPL, Jarvis ED, Sullivan ST, Kingan SB. Extended haplotype-phasing of long-read de novo genome assemblies using Hi-C. Nat Commun 2021; 12:1935. [PMID: 33911078 PMCID: PMC8081726 DOI: 10.1038/s41467-020-20536-y] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 11/12/2020] [Indexed: 01/27/2023] Open
Abstract
Haplotype-resolved genome assemblies are important for understanding how combinations of variants impact phenotypes. To date, these assemblies have been best created with complex protocols, such as cultured cells that contain a single-haplotype (haploid) genome, single cells where haplotypes are separated, or co-sequencing of parental genomes in a trio-based approach. These approaches are impractical in most situations. To address this issue, we present FALCON-Phase, a phasing tool that uses ultra-long-range Hi-C chromatin interaction data to extend phase blocks of partially-phased diploid assembles to chromosome or scaffold scale. FALCON-Phase uses the inherent phasing information in Hi-C reads, skipping variant calling, and reduces the computational complexity of phasing. Our method is validated on three benchmark datasets generated as part of the Vertebrate Genomes Project (VGP), including human, cow, and zebra finch, for which high-quality, fully haplotype-resolved assemblies are available using the trio-based approach. FALCON-Phase is accurate without having parental data and performance is better in samples with higher heterozygosity. For cow and zebra finch the accuracy is 97% compared to 80-91% for human. FALCON-Phase is applicable to any draft assembly that contains long primary contigs and phased associate contigs.
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Affiliation(s)
- Zev N Kronenberg
- Phase Genomics, Seattle, WA, USA.
- Pacific Biosciences, Menlo Park, CA, USA.
| | - Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, Bethesda, MD, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, Bethesda, MD, USA
| | | | | | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Kristen Kuhn
- US Meat Animal Research Center, ARS USDA, Clay Center, NE, USA
| | | | - Wai Yee Low
- Davies Research Centre, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - Stefan Hiendleder
- Davies Research Centre, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - Olivier Fedrigo
- Vertebrate Genomes Laboratory, The Rockefeller University, New York, NY, USA
| | | | | | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, Bethesda, MD, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - John L Williams
- Davies Research Centre, School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
- Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti, Università Cattolica del Sacro Cuore, 29122, Piacenza, Italy
| | | | - Erich D Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
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30
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Garg S. Computational methods for chromosome-scale haplotype reconstruction. Genome Biol 2021; 22:101. [PMID: 33845884 PMCID: PMC8040228 DOI: 10.1186/s13059-021-02328-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 03/25/2021] [Indexed: 12/13/2022] Open
Abstract
High-quality chromosome-scale haplotype sequences of diploid genomes, polyploid genomes, and metagenomes provide important insights into genetic variation associated with disease and biodiversity. However, whole-genome short read sequencing does not yield haplotype information spanning whole chromosomes directly. Computational assembly of shorter haplotype fragments is required for haplotype reconstruction, which can be challenging owing to limited fragment lengths and high haplotype and repeat variability across genomes. Recent advancements in long-read and chromosome-scale sequencing technologies, alongside computational innovations, are improving the reconstruction of haplotypes at the level of whole chromosomes. Here, we review recent and discuss methodological progress and perspectives in these areas.
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Affiliation(s)
- Shilpa Garg
- Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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31
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Ebert P, Audano PA, Zhu Q, Rodriguez-Martin B, Porubsky D, Bonder MJ, Sulovari A, Ebler J, Zhou W, Serra Mari R, Yilmaz F, Zhao X, Hsieh P, Lee J, Kumar S, Lin J, Rausch T, Chen Y, Ren J, Santamarina M, Höps W, Ashraf H, Chuang NT, Yang X, Munson KM, Lewis AP, Fairley S, Tallon LJ, Clarke WE, Basile AO, Byrska-Bishop M, Corvelo A, Evani US, Lu TY, Chaisson MJP, Chen J, Li C, Brand H, Wenger AM, Ghareghani M, Harvey WT, Raeder B, Hasenfeld P, Regier AA, Abel HJ, Hall IM, Flicek P, Stegle O, Gerstein MB, Tubio JMC, Mu Z, Li YI, Shi X, Hastie AR, Ye K, Chong Z, Sanders AD, Zody MC, Talkowski ME, Mills RE, Devine SE, Lee C, Korbel JO, Marschall T, Eichler EE. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. Science 2021; 372:eabf7117. [PMID: 33632895 PMCID: PMC8026704 DOI: 10.1126/science.abf7117] [Citation(s) in RCA: 307] [Impact Index Per Article: 102.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent-child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average minimum contig length needed to cover 50% of the genome: 26 million base pairs) integrate all forms of genetic variation, even across complex loci. We identified 107,590 structural variants (SVs), of which 68% were not discovered with short-read sequencing, and 278 SV hotspots (spanning megabases of gene-rich sequence). We characterized 130 of the most active mobile element source elements and found that 63% of all SVs arise through homology-mediated mechanisms. This resource enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1526 expression quantitative trait loci as well as SV candidates for adaptive selection within the human population.
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Affiliation(s)
- Peter Ebert
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Qihui Zhu
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Bernardo Rodriguez-Martin
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Marc Jan Bonder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Jana Ebler
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Weichen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Rebecca Serra Mari
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Feyza Yilmaz
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Xuefang Zhao
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - PingHsun Hsieh
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Joyce Lee
- Bionano Genomics, San Diego, CA 92121, USA
| | - Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, BASS 432 and 437, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Jiadong Lin
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Tobias Rausch
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Yu Chen
- Department of Genetics and Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Jingwen Ren
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Martin Santamarina
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics, and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Wolfram Höps
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Hufsah Ashraf
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - Nelson T Chuang
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | - Xiaofei Yang
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Susan Fairley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Luke J Tallon
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | | | | | | | | | | | - Tsung-Yu Lu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Junjie Chen
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Chong Li
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aaron M Wenger
- Pacific Biosciences of California, Menlo Park, CA 94025, USA
| | - Maryam Ghareghani
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, 66123 Saarbrücken, Germany
- Saarbrücken Graduate School of Computer Science, Saarland University, Saarland Informatics Campus E1.3, 66123 Saarbrücken, Germany
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA
| | - Benjamin Raeder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Patrick Hasenfeld
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Allison A Regier
- Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Haley J Abel
- Department of Medicine, Washington University, St. Louis, MO 63108, USA
| | - Ira M Hall
- Department of Genetics, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Oliver Stegle
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Mark B Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, BASS 432 and 437, 266 Whitney Avenue, New Haven, CT 06520, USA
| | - Jose M C Tubio
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Zoology, Genetics, and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
| | - Zepeng Mu
- Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Yang I Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
| | | | - Kai Ye
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
- Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109, USA
| | - Zechen Chong
- Department of Genetics and Informatics Institute, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ashley D Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | | | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA 02114, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ryan E Mills
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
- Department of Human Genetics, University of Michigan, 1241 E. Catherine Street, Ann Arbor, MI 48109, USA
| | - Scott E Devine
- Institute for Genome Sciences, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD 21201, USA
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, 277 West Yanta Road, Xi'an, 710061, Shaanxi, China
- Department of Graduate Studies-Life Sciences, Ewha Womans University, Ewhayeodae-gil, Seodaemun-gu, Seoul 120-750, South Korea
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tobias Marschall
- Heinrich Heine University, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Moorenstraße 20, 40225 Düsseldorf, Germany.
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Avenue NE, Seattle, WA 98195-5065, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
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32
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Eimer C, Sanders AD, Korbel JO, Marschall T, Ebert P. ASHLEYS: automated quality control for single-cell Strand-seq data. Bioinformatics 2021; 37:3356-3357. [PMID: 33792647 PMCID: PMC8504637 DOI: 10.1093/bioinformatics/btab221] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/15/2021] [Accepted: 03/31/2021] [Indexed: 11/18/2022] Open
Abstract
Summary Single-cell DNA template strand sequencing (Strand-seq) enables chromosome length haplotype phasing, construction of phased assemblies, mapping sister-chromatid exchange events and structural variant discovery. The initial quality control of potentially thousands of single-cell libraries is still done manually by domain experts. ASHLEYS automates this tedious task, delivers near-expert performance and labels even large datasets in seconds. Availability and implementation github.com/friendsofstrandseq/ashleys-qc, MIT license. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christina Eimer
- Center for Bioinformatics Saar, Saarland University, 66123 Saarbrücken, Germany
| | - Ashley D Sanders
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Heinrich Heine University, 40225 Düsseldorf, Germany
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33
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Construction of Whole Genomes from Scaffolds Using Single Cell Strand-Seq Data. Int J Mol Sci 2021; 22:ijms22073617. [PMID: 33807210 PMCID: PMC8037727 DOI: 10.3390/ijms22073617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/23/2021] [Accepted: 03/27/2021] [Indexed: 12/26/2022] Open
Abstract
Accurate reference genome sequences provide the foundation for modern molecular biology and genomics as the interpretation of sequence data to study evolution, gene expression, and epigenetics depends heavily on the quality of the genome assembly used for its alignment. Correctly organising sequenced fragments such as contigs and scaffolds in relation to each other is a critical and often challenging step in the construction of robust genome references. We previously identified misoriented regions in the mouse and human reference assemblies using Strand-seq, a single cell sequencing technique that preserves DNA directionality Here we demonstrate the ability of Strand-seq to build and correct full-length chromosomes by identifying which scaffolds belong to the same chromosome and determining their correct order and orientation, without the need for overlapping sequences. We demonstrate that Strand-seq exquisitely maps assembly fragments into large related groups and chromosome-sized clusters without using new assembly data. Using template strand inheritance as a bi-allelic marker, we employ genetic mapping principles to cluster scaffolds that are derived from the same chromosome and order them within the chromosome based solely on directionality of DNA strand inheritance. We prove the utility of our approach by generating improved genome assemblies for several model organisms including the ferret, pig, Xenopus, zebrafish, Tasmanian devil and the Guinea pig.
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34
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Ura H, Togi S, Niida Y. Targeted Double-Stranded cDNA Sequencing-Based Phase Analysis to Identify Compound Heterozygous Mutations and Differential Allelic Expression. BIOLOGY 2021; 10:biology10040256. [PMID: 33804940 PMCID: PMC8063809 DOI: 10.3390/biology10040256] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 11/16/2022]
Abstract
Simple Summary Phase analysis to distinguish between in cis and in trans heterozygous mutations is important for clinical diagnosis because in trans compound heterozygous mutations cause autosomal recessive diseases. However, conventional phase analysis is limited because of the large target size of genomic DNA. Here, we performed a targeted double-stranded cDNA sequencing-based phase analysis to resolve the limitation of distance using direct adapter ligation library preparation and paired-end sequencing; we elucidated that two heterozygous mutations on a patient with Wilson disease are in trans compound heterozygous mutations. Furthermore, we detected the differential allelic expression. Our results indicate that a targeted double-stranded cDNA sequencing-based phase analysis is useful for determining compound heterozygous mutations and confers information on allelic expression. Abstract There are two combinations of heterozygous mutation, i.e., in trans, which carries mutations on different alleles, and in cis, which carries mutations on the same allele. Because only in trans compound heterozygous mutations have been implicated in autosomal recessive diseases, it is important to distinguish them for clinical diagnosis. However, conventional phase analysis is limited because of the large target size of genomic DNA. Here, we performed a genetic analysis on a patient with Wilson disease, and we detected two heterozygous mutations chr13:51958362;G>GG (NM_000053.4:c.2304dup r.2304dup p.Met769HisfsTer26) and chr13:51964900;C>T (NM_000053.4:c.1841G>A r.1841g>a p.Gly614Asp) in the causative gene ATP7B. The distance between the two mutations was 6.5 kb in genomic DNA but 464 bp in mRNA. Targeted double-stranded cDNA sequencing-based phase analysis was performed using direct adapter ligation library preparation and paired-end sequencing, and we elucidated they are in trans compound heterozygous mutations. Trio analysis showed that the mutation (chr13:51964900;C>T) derived from the father and the other mutation from the mother, validating that the mutations are in trans composition. Furthermore, targeted double-stranded cDNA sequencing-based phase analysis detected the differential allelic expression, suggesting that the mutation (chr13:51958362;G>GG) caused downregulation of expression by nonsense-mediated mRNA decay. Our results indicate that targeted double-stranded cDNA sequencing-based phase analysis is useful for determining compound heterozygous mutations and confers information on allelic expression.
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Affiliation(s)
- Hiroki Ura
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa 920-0923, Japan; (S.T.); (Y.N.)
- Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa 920-0923, Japan
- Correspondence: ; Tel.: +81-076-286-2211 (ext. 8353)
| | - Sumihito Togi
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa 920-0923, Japan; (S.T.); (Y.N.)
- Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa 920-0923, Japan
| | - Yo Niida
- Center for Clinical Genomics, Kanazawa Medical University Hospital, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa 920-0923, Japan; (S.T.); (Y.N.)
- Division of Genomic Medicine, Department of Advanced Medicine, Medical Research Institute, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa 920-0923, Japan
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35
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Porubsky D, Ebert P, Audano PA, Vollger MR, Harvey WT, Marijon P, Ebler J, Munson KM, Sorensen M, Sulovari A, Haukness M, Ghareghani M, Lansdorp PM, Paten B, Devine SE, Sanders AD, Lee C, Chaisson MJP, Korbel JO, Eichler EE, Marschall T. Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads. Nat Biotechnol 2021; 39:302-308. [PMID: 33288906 PMCID: PMC7954704 DOI: 10.1038/s41587-020-0719-5] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 09/16/2020] [Indexed: 12/18/2022]
Abstract
Human genomes are typically assembled as consensus sequences that lack information on parental haplotypes. Here we describe a reference-free workflow for diploid de novo genome assembly that combines the chromosome-wide phasing and scaffolding capabilities of single-cell strand sequencing1,2 with continuous long-read or high-fidelity3 sequencing data. Employing this strategy, we produced a completely phased de novo genome assembly for each haplotype of an individual of Puerto Rican descent (HG00733) in the absence of parental data. The assemblies are accurate (quality value > 40) and highly contiguous (contig N50 > 23 Mbp) with low switch error rates (0.17%), providing fully phased single-nucleotide variants, indels and structural variants. A comparison of Oxford Nanopore Technologies and Pacific Biosciences phased assemblies identified 154 regions that are preferential sites of contig breaks, irrespective of sequencing technology or phasing algorithms.
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Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Peter Ebert
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | - Peter A Audano
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Pierre Marijon
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | - Jana Ebler
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Melanie Sorensen
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Arvis Sulovari
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Marina Haukness
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Maryam Ghareghani
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany
- Center for Bioinformatics, Saarland University, and Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Peter M Lansdorp
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Scott E Devine
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ashley D Sanders
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Department of Life Science, Ewha Womans University, Seoul, Republic of Korea
| | - Mark J P Chaisson
- Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
| | - Tobias Marschall
- Heinrich Heine University Düsseldorf, Medical Faculty, Institute for Medical Biometry and Bioinformatics, Düsseldorf, Germany.
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36
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Maestri S, Maturo MG, Cosentino E, Marcolungo L, Iadarola B, Fortunati E, Rossato M, Delledonne M. A Long-Read Sequencing Approach for Direct Haplotype Phasing in Clinical Settings. Int J Mol Sci 2020; 21:E9177. [PMID: 33271988 PMCID: PMC7731377 DOI: 10.3390/ijms21239177] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/20/2020] [Accepted: 11/27/2020] [Indexed: 11/17/2022] Open
Abstract
The reconstruction of individual haplotypes can facilitate the interpretation of disease risks; however, high costs and technical challenges still hinder their assessment in clinical settings. Second-generation sequencing is the gold standard for variant discovery but, due to the production of short reads covering small genomic regions, allows only indirect haplotyping based on statistical methods. In contrast, third-generation methods such as the nanopore sequencing platform developed by Oxford Nanopore Technologies (ONT) generate long reads that can be used for direct haplotyping, with fewer drawbacks. However, robust standards for variant phasing in ONT-based target resequencing efforts are not yet available. In this study, we presented a streamlined proof-of-concept workflow for variant calling and phasing based on ONT data in a clinically relevant 12-kb region of the APOE locus, a hotspot for variants and haplotypes associated with aging-related diseases and longevity. Starting with sequencing data from simple amplicons of the target locus, we demonstrated that ONT data allow for reliable single-nucleotide variant (SNV) calling and phasing from as little as 60 reads, although the recognition of indels is less efficient. Even so, we identified the best combination of ONT read sets (600) and software (BWA/Minimap2 and HapCUT2) that enables full haplotype reconstruction when both SNVs and indels have been identified previously using a highly-accurate sequencing platform. In conclusion, we established a rapid and inexpensive workflow for variant phasing based on ONT long reads. This allowed for the analysis of multiple samples in parallel and can easily be implemented in routine clinical practice, including diagnostic testing.
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Affiliation(s)
| | | | | | | | | | | | - Marzia Rossato
- Department of Biotechnology, University of Verona, 37134 Verona, Italy; (S.M.); (M.G.M.); (E.C.); (L.M.); (B.I.); (E.F.); (M.D.)
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37
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Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet 2020; 21:597-614. [PMID: 32504078 PMCID: PMC7877196 DOI: 10.1038/s41576-020-0236-x] [Citation(s) in RCA: 437] [Impact Index Per Article: 109.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/27/2022]
Abstract
Over the past decade, long-read, single-molecule DNA sequencing technologies have emerged as powerful players in genomics. With the ability to generate reads tens to thousands of kilobases in length with an accuracy approaching that of short-read sequencing technologies, these platforms have proven their ability to resolve some of the most challenging regions of the human genome, detect previously inaccessible structural variants and generate some of the first telomere-to-telomere assemblies of whole chromosomes. Long-read sequencing technologies will soon permit the routine assembly of diploid genomes, which will revolutionize genomics by revealing the full spectrum of human genetic variation, resolving some of the missing heritability and leading to the discovery of novel mechanisms of disease.
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Affiliation(s)
- Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA.
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38
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Detecting chromatin interactions between and along sister chromatids with SisterC. Nat Methods 2020; 17:1002-1009. [PMID: 32968250 PMCID: PMC7541687 DOI: 10.1038/s41592-020-0930-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 07/04/2020] [Accepted: 07/21/2020] [Indexed: 11/28/2022]
Abstract
Chromosome segregation requires both compaction and disentanglement of sister chromatids. We describe SisterC, a chromosome conformation capture assay that distinguishes interactions between and along identical sister chromatids. SisterC employs BrdU incorporation during S-phase to label newly replicated strands, followed by Hi-C and then the destruction of BrdU-containing strands by UV/Hoechst treatment. After sequencing of the remaining intact strands, this allows for assignment of Hi-C products as inter- and intra-sister interactions based on the strands that reads are mapped to. We performed SisterC on mitotic S. cerevisiae cells. We find precise alignment of sister chromatids at centromeres. Along arms, sister chromatids are less precisely aligned with inter-sister connections every ~35kb. Inter-sister interactions occur between cohesin binding sites that often are offset by 5 to 25kb. Along sister chromatids, cohesin forms loops of up to 50kb. SisterC allows study of the complex interplay between sister chromatid compaction and their segregation during mitosis.
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39
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Chin CS, Wagner J, Zeng Q, Garrison E, Garg S, Fungtammasan A, Rautiainen M, Aganezov S, Kirsche M, Zarate S, Schatz MC, Xiao C, Rowell WJ, Markello C, Farek J, Sedlazeck FJ, Bansal V, Yoo B, Miller N, Zhou X, Carroll A, Barrio AM, Salit M, Marschall T, Dilthey AT, Zook JM. A diploid assembly-based benchmark for variants in the major histocompatibility complex. Nat Commun 2020; 11:4794. [PMID: 32963235 PMCID: PMC7508831 DOI: 10.1038/s41467-020-18564-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 08/27/2020] [Indexed: 01/20/2023] Open
Abstract
Most human genomes are characterized by aligning individual reads to the reference genome, but accurate long reads and linked reads now enable us to construct accurate, phased de novo assemblies. We focus on a medically important, highly variable, 5 million base-pair (bp) region where diploid assembly is particularly useful - the Major Histocompatibility Complex (MHC). Here, we develop a human genome benchmark derived from a diploid assembly for the openly-consented Genome in a Bottle sample HG002. We assemble a single contig for each haplotype, align them to the reference, call phased small and structural variants, and define a small variant benchmark for the MHC, covering 94% of the MHC and 22368 variants smaller than 50 bp, 49% more variants than a mapping-based benchmark. This benchmark reliably identifies errors in mapping-based callsets, and enables performance assessment in regions with much denser, complex variation than regions covered by previous benchmarks.
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Affiliation(s)
- Chen-Shan Chin
- DNAnexus, Inc, 1975 W El Camino Real, Suite 204, Mountain View, CA, 94040, USA
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8312, Gaithersburg, MD, 20899, USA
| | - Qiandong Zeng
- Laboratory Corporation of America Holdings, 3400 Computer Drive, Westborough, MA, 01581, USA
| | - Erik Garrison
- University of California, Santa Cruz, 1156 High St, Santa Cruz, CA, 95064, USA
| | - Shilpa Garg
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Mikko Rautiainen
- Center for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, 66123, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, 66123, Saarbrücken, Germany
- Saarland Graduate School for Computer Science, Saarland Informatics Campus E1.3, 66123, Saarbrücken, Germany
| | - Sergey Aganezov
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Melanie Kirsche
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Samantha Zarate
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Michael C Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, 11724, USA
| | - Chunlin Xiao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, 20894, USA
| | | | - Charles Markello
- University of California, Santa Cruz, 1156 High St, Santa Cruz, CA, 95064, USA
| | - Jesse Farek
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Vikas Bansal
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Byunggil Yoo
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, 64108, USA
| | - Neil Miller
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO, 64108, USA
| | - Xin Zhou
- Department of Computer Science, Stanford University, Stanford, CA, 94305, USA
| | - Andrew Carroll
- Google Inc, 1600 Amphitheatre Pkwy, Mountain View, CA, 94043, USA
| | | | - Marc Salit
- Joint Initiative for Metrology in Biology, Stanford, CA, 94305, USA
| | - Tobias Marschall
- Institute of Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Alexander T Dilthey
- Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8312, Gaithersburg, MD, 20899, USA.
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40
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Porubsky D, Sanders AD, Taudt A, Colomé-Tatché M, Lansdorp PM, Guryev V. breakpointR: an R/Bioconductor package to localize strand state changes in Strand-seq data. Bioinformatics 2020; 36:1260-1261. [PMID: 31504176 DOI: 10.1093/bioinformatics/btz681] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 08/12/2019] [Accepted: 08/27/2019] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Strand-seq is a specialized single-cell DNA sequencing technique centered around the directionality of single-stranded DNA. Computational tools for Strand-seq analyses must capture the strand-specific information embedded in these data. RESULTS Here we introduce breakpointR, an R/Bioconductor package specifically tailored to process and interpret single-cell strand-specific sequencing data obtained from Strand-seq. We developed breakpointR to detect local changes in strand directionality of aligned Strand-seq data, to enable fine-mapping of sister chromatid exchanges, germline inversion and to support global haplotype assembly. Given the broad spectrum of Strand-seq applications we expect breakpointR to be an important addition to currently available tools and extend the accessibility of this novel sequencing technique. AVAILABILITY AND IMPLEMENTATION R/Bioconductor package https://bioconductor.org/packages/breakpointR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David Porubsky
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Ashley D Sanders
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada.,European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Aaron Taudt
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Maria Colomé-Tatché
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Peter M Lansdorp
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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41
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Soifer L, Fong NL, Yi N, Ireland AT, Lam I, Sooknah M, Paw JS, Peluso P, Concepcion GT, Rank D, Hastie AR, Jojic V, Ruby JG, Botstein D, Roy MA. Fully Phased Sequence of a Diploid Human Genome Determined de Novo from the DNA of a Single Individual. G3 (BETHESDA, MD.) 2020; 10:2911-2925. [PMID: 32631951 PMCID: PMC7466960 DOI: 10.1534/g3.119.400995] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 06/26/2020] [Indexed: 12/17/2022]
Abstract
In recent years, improved sequencing technology and computational tools have made de novo genome assembly more accessible. Many approaches, however, generate either an unphased or only partially resolved representation of a diploid genome, in which polymorphisms are detected but not assigned to one or the other of the homologous chromosomes. Yet chromosomal phase information is invaluable for the understanding of phenotypic trait inheritance in the cases of compound heterozygosity, allele-specific expression or cis-acting variants. Here we use a combination of tools and sequencing technologies to generate a de novo diploid assembly of the human primary cell line WI-38. First, data from PacBio single molecule sequencing and Bionano Genomics optical mapping were combined to generate an unphased assembly. Next, 10x Genomics linked reads were combined with the hybrid assembly to generate a partially phased assembly. Lastly, we developed and optimized methods to use short-read (Illumina) sequencing of flow cytometry-sorted metaphase chromosomes to provide phase information. The final genome assembly was almost fully (94%) phased with the addition of approximately 2.5-fold coverage of Illumina data from the sequenced metaphase chromosomes. The diploid nature of the final de novo genome assembly improved the resolution of structural variants between the WI-38 genome and the human reference genome. The phased WI-38 sequence data are available for browsing and download at wi38.research.calicolabs.com. Our work shows that assembling a completely phased diploid genome de novo from the DNA of a single individual is now readily achievable.
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Affiliation(s)
- Llya Soifer
- Calico Life Sciences LLC, South San Francisco, CA 94080
| | - Nicole L Fong
- Calico Life Sciences LLC, South San Francisco, CA 94080
| | - Nelda Yi
- Calico Life Sciences LLC, South San Francisco, CA 94080
| | | | - Irene Lam
- Calico Life Sciences LLC, South San Francisco, CA 94080
| | | | | | | | | | - David Rank
- Pacific Biosciences, Menlo Park, CA 94025
| | | | | | - J Graham Ruby
- Calico Life Sciences LLC, South San Francisco, CA 94080
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42
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Xu P, Kennell T, Gao M, Kimberly RP, Chong Z. MRLR: unraveling high-resolution meiotic recombination by linked reads. Bioinformatics 2020; 36:10-16. [PMID: 31214684 PMCID: PMC6956785 DOI: 10.1093/bioinformatics/btz503] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 04/30/2019] [Accepted: 06/12/2019] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION Meiotic recombination facilitates the transmission of exchanged genetic material between homologous chromosomes and plays a crucial role in increasing the genetic variations in eukaryotic organisms. In humans, thousands of crossover events have been identified by genotyping related family members. However, most of these crossover regions span tens to hundreds of kb, which is not sufficient resolution to accurately identify the crossover breakpoints in a typical trio family. RESULTS We have developed MRLR, a software using 10X linked reads to identify crossover events at a high resolution. By reconstructing the gamete genome, MRLR only requires a trio family dataset and can efficiently discover the crossover events. Using MRLR, we revealed a fine-scale pattern of crossover regions in six human families. From the two closest heterozygous alleles around the crossovers, we determined that MRLR achieved a median resolution 4.5 kb. This method can delineate a genome-wide landscape of crossover events at a precise scale, which is important for both functional and genomic features analysis of meiotic recombination. AVAILABILITY AND IMPLEMENTATION MRLR is freely available at https://github.com/ChongLab/MRLR, implemented in Perl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Peng Xu
- Department of Genetics, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.,Informatics Institute, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Timothy Kennell
- Informatics Institute, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Min Gao
- Informatics Institute, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | | | - Robert P Kimberly
- Department of Medicine, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Zechen Chong
- Department of Genetics, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.,Informatics Institute, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
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43
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Recurrent inversion toggling and great ape genome evolution. Nat Genet 2020; 52:849-858. [PMID: 32541924 PMCID: PMC7415573 DOI: 10.1038/s41588-020-0646-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 05/15/2020] [Indexed: 01/14/2023]
Abstract
Inversions play an important role in disease and evolution but are difficult to characterize because their breakpoints map to large repeats. We increased by sixfold the number (n = 1,069) of previously reported great ape inversions by using single-cell DNA template strand and long-read sequencing. We find that the X chromosome is most enriched (2.5-fold) for inversions, on the basis of its size and duplication content. There is an excess of differentially expressed primate genes near the breakpoints of large (>100 kilobases (kb)) inversions but not smaller events. We show that when great ape lineage-specific duplications emerge, they preferentially (approximately 75%) occur in an inverted orientation compared to that at their ancestral locus. We construct megabase-pair scale haplotypes for individual chromosomes and identify 23 genomic regions that have recurrently toggled between a direct and an inverted state over 15 million years. The direct orientation is most frequently the derived state for human polymorphisms that predispose to recurrent copy number variants associated with neurodevelopmental disease.
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44
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Majidian S, Sedlazeck FJ. PhaseME: Automatic rapid assessment of phasing quality and phasing improvement. Gigascience 2020; 9:giaa078. [PMID: 32706368 PMCID: PMC7379178 DOI: 10.1093/gigascience/giaa078] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 05/28/2020] [Accepted: 07/01/2020] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND The detection of which mutations are occurring on the same DNA molecule is essential to predict their consequences. This can be achieved by phasing the genomic variations. Nevertheless, state-of-the-art haplotype phasing is currently a black box in which the accuracy and quality of the reconstructed haplotypes are hard to assess. FINDINGS Here we present PhaseME, a versatile method to provide insights into and improvement of sample phasing results based on linkage data. We showcase the performance and the importance of PhaseME by comparing phasing information obtained from Pacific Biosciences including both continuous long reads and high-quality consensus reads, Oxford Nanopore Technologies, 10x Genomics, and Illumina sequencing technologies. We found that 10x Genomics and Oxford Nanopore phasing can be significantly improved while retaining a high N50 and completeness of phase blocks. PhaseME generates reports and summary plots to provide insights into phasing performance and correctness. We observed unique phasing issues for each of the sequencing technologies, highlighting the necessity of quality assessments. PhaseME is able to decrease the Hamming error rate significantly by 22.4% on average across all 5 technologies. Additionally, a significant improvement is obtained in the reduction of long switch errors. Especially for high-quality consensus reads, the improvement is 54.6% in return for only a 5% decrease in phase block N50 length. CONCLUSIONS PhaseME is a universal method to assess the phasing quality and accuracy and improves the quality of phasing using linkage information. The package is freely available at https://github.com/smajidian/phaseme.
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Affiliation(s)
- Sina Majidian
- School of Electrical Engineering, Iran University of Science & Technology, Narmak, Tehran 1684613114, Iran
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
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Baaijens JA, Schönhuth A. Overlap graph-based generation of haplotigs for diploids and polyploids. Bioinformatics 2020; 35:4281-4289. [PMID: 30994902 DOI: 10.1093/bioinformatics/btz255] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 03/18/2019] [Accepted: 04/11/2019] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Haplotype-aware genome assembly plays an important role in genetics, medicine and various other disciplines, yet generation of haplotype-resolved de novo assemblies remains a major challenge. Beyond distinguishing between errors and true sequential variants, one needs to assign the true variants to the different genome copies. Recent work has pointed out that the enormous quantities of traditional NGS read data have been greatly underexploited in terms of haplotig computation so far, which reflects that methodology for reference independent haplotig computation has not yet reached maturity. RESULTS We present POLYploid genome fitTEr (POLYTE) as a new approach to de novo generation of haplotigs for diploid and polyploid genomes of known ploidy. Our method follows an iterative scheme where in each iteration reads or contigs are joined, based on their interplay in terms of an underlying haplotype-aware overlap graph. Along the iterations, contigs grow while preserving their haplotype identity. Benchmarking experiments on both real and simulated data demonstrate that POLYTE establishes new standards in terms of error-free reconstruction of haplotype-specific sequence. As a consequence, POLYTE outperforms state-of-the-art approaches in various relevant aspects, where advantages become particularly distinct in polyploid settings. AVAILABILITY AND IMPLEMENTATION POLYTE is freely available as part of the HaploConduct package at https://github.com/HaploConduct/HaploConduct, implemented in Python and C++. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Alexander Schönhuth
- Centrum Wiskunde & Informatica, XG Amsterdam, The Netherlands.,Theoretical Biology and Bioinformatics, Utrecht University, CH Utrecht, The Netherlands
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Lutgen D, Ritter R, Olsen R, Schielzeth H, Gruselius J, Ewels P, García JT, Shirihai H, Schweizer M, Suh A, Burri R. Linked‐read sequencing enables haplotype‐resolved resequencing at population scale. Mol Ecol Resour 2020; 20:1311-1322. [DOI: 10.1111/1755-0998.13192] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/25/2020] [Accepted: 05/06/2020] [Indexed: 11/28/2022]
Affiliation(s)
- Dave Lutgen
- Department of Population Ecology Institute of Ecology and Evolution Friedrich Schiller University Jena Jena Germany
| | - Raphael Ritter
- Department of Population Ecology Institute of Ecology and Evolution Friedrich Schiller University Jena Jena Germany
| | - Remi‐André Olsen
- Science for Life Laboratory Department of Biochemistry and Biophysics Stockholm University Solna Sweden
| | - Holger Schielzeth
- Department of Population Ecology Institute of Ecology and Evolution Friedrich Schiller University Jena Jena Germany
| | - Joel Gruselius
- Science for Life Laboratory Department of Biosciences and Nutrition Karolinska Institutet Stockholm Sweden
| | - Philip Ewels
- Science for Life Laboratory Department of Biochemistry and Biophysics Stockholm University Solna Sweden
| | - Jesús T. García
- Instituto de Investigación en Recursos Cinegéticos (IREC) CSIC‐UCLM‐JCCM Ciudad Real Spain
| | | | - Manuel Schweizer
- Natural History Museum Bern Bern Switzerland
- Institute of Ecology and Evolution University of Bern Bern Switzerland
| | - Alexander Suh
- Department of Organismal Biology – Systematic Biology Evolutionary Biology Centre (EBC) Uppsala University Uppsala Sweden
| | - Reto Burri
- Department of Population Ecology Institute of Ecology and Evolution Friedrich Schiller University Jena Jena Germany
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Majidian S, Kahaei MH, de Ridder D. Hap10: reconstructing accurate and long polyploid haplotypes using linked reads. BMC Bioinformatics 2020; 21:253. [PMID: 32552661 PMCID: PMC7302376 DOI: 10.1186/s12859-020-03584-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 06/05/2020] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Haplotype information is essential for many genetic and genomic analyses, including genotype-phenotype associations in human, animals and plants. Haplotype assembly is a method for reconstructing haplotypes from DNA sequencing reads. By the advent of new sequencing technologies, new algorithms are needed to ensure long and accurate haplotypes. While a few linked-read haplotype assembly algorithms are available for diploid genomes, to the best of our knowledge, no algorithms have yet been proposed for polyploids specifically exploiting linked reads. RESULTS The first haplotyping algorithm designed for linked reads generated from a polyploid genome is presented, built on a typical short-read haplotyping method, SDhaP. Using the input aligned reads and called variants, the haplotype-relevant information is extracted. Next, reads with the same barcodes are combined to produce molecule-specific fragments. Then, these fragments are clustered into strongly connected components which are then used as input of a haplotype assembly core in order to estimate accurate and long haplotypes. CONCLUSIONS Hap10 is a novel algorithm for haplotype assembly of polyploid genomes using linked reads. The performance of the algorithms is evaluated in a number of simulation scenarios and its applicability is demonstrated on a real dataset of sweet potato.
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Affiliation(s)
- Sina Majidian
- School of Electrical Engineering, Iran University of Science & Technology, Narmak, Tehran, 16846-13114, Iran
| | - Mohammad Hossein Kahaei
- School of Electrical Engineering, Iran University of Science & Technology, Narmak, Tehran, 16846-13114, Iran.
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands
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Majidian S, Kahaei MH, de Ridder D. Minimum error correction-based haplotype assembly: Considerations for long read data. PLoS One 2020; 15:e0234470. [PMID: 32530974 PMCID: PMC7292361 DOI: 10.1371/journal.pone.0234470] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/27/2020] [Indexed: 11/23/2022] Open
Abstract
The single nucleotide polymorphism (SNP) is the most widely studied type of genetic variation. A haplotype is defined as the sequence of alleles at SNP sites on each haploid chromosome. Haplotype information is essential in unravelling the genome-phenotype association. Haplotype assembly is a well-known approach for reconstructing haplotypes, exploiting reads generated by DNA sequencing devices. The Minimum Error Correction (MEC) metric is often used for reconstruction of haplotypes from reads. However, problems with the MEC metric have been reported. Here, we investigate the MEC approach to demonstrate that it may result in incorrectly reconstructed haplotypes for devices that produce error-prone long reads. Specifically, we evaluate this approach for devices developed by Illumina, Pacific BioSciences and Oxford Nanopore Technologies. We show that imprecise haplotypes may be reconstructed with a lower MEC than that of the exact haplotype. The performance of MEC is explored for different coverage levels and error rates of data. Our simulation results reveal that in order to avoid incorrect MEC-based haplotypes, a coverage of 25 is needed for reads generated by Pacific BioSciences RS systems.
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Affiliation(s)
- Sina Majidian
- School of Electrical Engineering, Iran University of Science & Technology, Narmak, Tehran, Iran
| | - Mohammad Hossein Kahaei
- School of Electrical Engineering, Iran University of Science & Technology, Narmak, Tehran, Iran
- * E-mail:
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
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Rausch T, Hsi-Yang Fritz M, Korbel JO, Benes V. Alfred: interactive multi-sample BAM alignment statistics, feature counting and feature annotation for long- and short-read sequencing. Bioinformatics 2020; 35:2489-2491. [PMID: 30520945 PMCID: PMC6612896 DOI: 10.1093/bioinformatics/bty1007] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Revised: 11/20/2018] [Accepted: 12/05/2018] [Indexed: 11/30/2022] Open
Abstract
Summary Harmonizing quality control (QC) of large-scale second and third-generation sequencing datasets is key for enabling downstream computational and biological analyses. We present Alfred, an efficient and versatile command-line application that computes multi-sample QC metrics in a read-group aware manner, across a wide variety of sequencing assays and technologies. In addition to standard QC metrics such as GC bias, base composition, insert size and sequencing coverage distributions it supports haplotype-aware and allele-specific feature counting and feature annotation. The versatility of Alfred allows for easy pipeline integration in high-throughput settings, including DNA sequencing facilities and large-scale research initiatives, enabling continuous monitoring of sequence data quality and characteristics across samples. Alfred supports haplo-tagging of BAM/CRAM files to conduct haplotype-resolved analyses in conjunction with a variety of next-generation sequencing based assays. Alfred’s companion web application enables interactive exploration of results and comparison to public datasets. Availability and implementation Alfred is open-source and freely available at https://tobiasrausch.com/alfred/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tobias Rausch
- Genomics Core Facility, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany.,Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany
| | - Markus Hsi-Yang Fritz
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany
| | - Jan O Korbel
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany
| | - Vladimir Benes
- Genomics Core Facility, European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, Heidelberg, Germany
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50
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Sanders AD, Meiers S, Ghareghani M, Porubsky D, Jeong H, van Vliet MACC, Rausch T, Richter-Pechańska P, Kunz JB, Jenni S, Bolognini D, Longo GMC, Raeder B, Kinanen V, Zimmermann J, Benes V, Schrappe M, Mardin BR, Kulozik AE, Bornhauser B, Bourquin JP, Marschall T, Korbel JO. Single-cell analysis of structural variations and complex rearrangements with tri-channel processing. Nat Biotechnol 2020; 38:343-354. [PMID: 31873213 PMCID: PMC7612647 DOI: 10.1038/s41587-019-0366-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 11/20/2019] [Indexed: 02/07/2023]
Abstract
Structural variation (SV), involving deletions, duplications, inversions and translocations of DNA segments, is a major source of genetic variability in somatic cells and can dysregulate cancer-related pathways. However, discovering somatic SVs in single cells has been challenging, with copy-number-neutral and complex variants typically escaping detection. Here we describe single-cell tri-channel processing (scTRIP), a computational framework that integrates read depth, template strand and haplotype phase to comprehensively discover SVs in individual cells. We surveyed SV landscapes of 565 single cells, including transformed epithelial cells and patient-derived leukemic samples, to discover abundant SV classes, including inversions, translocations and complex DNA rearrangements. Analysis of the leukemic samples revealed four times more somatic SVs than cytogenetic karyotyping, submicroscopic copy-number alterations, oncogenic copy-neutral rearrangements and a subclonal chromothripsis event. Advancing current methods, single-cell tri-channel processing can directly measure SV mutational processes in individual cells, such as breakage-fusion-bridge cycles, facilitating studies of clonal evolution, genetic mosaicism and SV formation mechanisms, which could improve disease classification for precision medicine.
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Affiliation(s)
- Ashley D Sanders
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sascha Meiers
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maryam Ghareghani
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Saarbrücken, Germany
| | - David Porubsky
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Hyobin Jeong
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Tobias Rausch
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
| | - Paulina Richter-Pechańska
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Joachim B Kunz
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Silvia Jenni
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Davide Bolognini
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Gabriel M C Longo
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Benjamin Raeder
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Venla Kinanen
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Jürgen Zimmermann
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Vladimir Benes
- European Molecular Biology Laboratory, Genomics Core Facility, Heidelberg, Germany
| | - Martin Schrappe
- Department of Pediatrics, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Balca R Mardin
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- BioMed X Innovation Center, Heidelberg, Germany
| | - Andreas E Kulozik
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany
- Department of Pediatric Oncology, Hematology, and Immunology, University of Heidelberg and Hopp Children's Cancer Center, Heidelberg, Germany
| | - Beat Bornhauser
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Jean-Pierre Bourquin
- Division of Pediatric Oncology, University Children's Hospital, Zürich, Switzerland
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany.
- Max Planck Institute for Informatics, Saarbrücken, Germany.
| | - Jan O Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, University of Heidelberg, Heidelberg, Germany.
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