1
|
Porubsky D, Dashnow H, Sasani TA, Logsdon GA, Hallast P, Noyes MD, Kronenberg ZN, Mokveld T, Koundinya N, Nolan C, Steely CJ, Guarracino A, Dolzhenko E, Harvey WT, Rowell WJ, Grigorev K, Nicholas TJ, Oshima KK, Lin J, Ebert P, Watkins WS, Leung TY, Hanlon VCT, McGee S, Pedersen BS, Goldberg ME, Happ HC, Jeong H, Munson KM, Hoekzema K, Chan DD, Wang Y, Knuth J, Garcia GH, Fanslow C, Lambert C, Lee C, Smith JD, Levy S, Mason CE, Garrison E, Lansdorp PM, Neklason DW, Jorde LB, Quinlan AR, Eberle MA, Eichler EE. A familial, telomere-to-telomere reference for human de novo mutation and recombination from a four-generation pedigree. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.05.606142. [PMID: 39149261 PMCID: PMC11326147 DOI: 10.1101/2024.08.05.606142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Using five complementary short- and long-read sequencing technologies, we phased and assembled >95% of each diploid human genome in a four-generation, 28-member family (CEPH 1463) allowing us to systematically assess de novo mutations (DNMs) and recombination. From this family, we estimate an average of 192 DNMs per generation, including 75.5 de novo single-nucleotide variants (SNVs), 7.4 non-tandem repeat indels, 79.6 de novo indels or structural variants (SVs) originating from tandem repeats, 7.7 centromeric de novo SVs and SNVs, and 12.4 de novo Y chromosome events per generation. STRs and VNTRs are the most mutable with 32 loci exhibiting recurrent mutation through the generations. We accurately assemble 288 centromeres and six Y chromosomes across the generations, documenting de novo SVs, and demonstrate that the DNM rate varies by an order of magnitude depending on repeat content, length, and sequence identity. We show a strong paternal bias (75-81%) for all forms of germline DNM, yet we estimate that 17% of de novo SNVs are postzygotic in origin with no paternal bias. We place all this variation in the context of a high-resolution recombination map (~3.5 kbp breakpoint resolution). We observe a strong maternal recombination bias (1.36 maternal:paternal ratio) with a consistent reduction in the number of crossovers with increasing paternal (r=0.85) and maternal (r=0.65) age. However, we observe no correlation between meiotic crossover locations and de novo SVs, arguing against non-allelic homologous recombination as a predominant mechanism. The use of multiple orthogonal technologies, near-telomere-to-telomere phased genome assemblies, and a multi-generation family to assess transmission has created the most comprehensive, publicly available "truth set" of all classes of genomic variants. The resource can be used to test and benchmark new algorithms and technologies to understand the most fundamental processes underlying human genetic variation.
Collapse
Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Harriet Dashnow
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Thomas A Sasani
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Present address: Department of Genetics, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pille Hallast
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Michelle D Noyes
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | | | - Nidhi Koundinya
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Cody J Steely
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Andrea Guarracino
- Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - William J Rowell
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Kirill Grigorev
- Blue Marble Space Institute of Science, Seattle, WA, USA
- Core Unit Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Thomas J Nicholas
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Keisuke K Oshima
- Present address: Department of Genetics, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiadong Lin
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Peter Ebert
- Core Unit Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - W Scott Watkins
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Tiffany Y Leung
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
| | | | - Sean McGee
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Brent S Pedersen
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Michael E Goldberg
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Hannah C Happ
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Hyeonsoo Jeong
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Present address: Altos Labs, San Diego, CA, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Daniel D Chan
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
| | - Yanni Wang
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, Canada
| | - Jordan Knuth
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Gage H Garcia
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | | | - Charles Lee
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Joshua D Smith
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Shawn Levy
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA
| | - Erik Garrison
- Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | | | - Deborah W Neklason
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Lynn B Jorde
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - Aaron R Quinlan
- Department of Human Genetics, University of Utah, Salt Lake City, UT, 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
| |
Collapse
|
2
|
Grochowski CM, Bengtsson JD, Du H, Gandhi M, Lun MY, Mehaffey MG, Park K, Höps W, Benito E, Hasenfeld P, Korbel JO, Mahmoud M, Paulin LF, Jhangiani SN, Hwang JP, Bhamidipati SV, Muzny DM, Fatih JM, Gibbs RA, Pendleton M, Harrington E, Juul S, Lindstrand A, Sedlazeck FJ, Pehlivan D, Lupski JR, Carvalho CMB. Inverted triplications formed by iterative template switches generate structural variant diversity at genomic disorder loci. CELL GENOMICS 2024; 4:100590. [PMID: 38908378 PMCID: PMC11293582 DOI: 10.1016/j.xgen.2024.100590] [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: 11/13/2023] [Revised: 12/27/2023] [Accepted: 05/31/2024] [Indexed: 06/24/2024]
Abstract
The duplication-triplication/inverted-duplication (DUP-TRP/INV-DUP) structure is a complex genomic rearrangement (CGR). Although it has been identified as an important pathogenic DNA mutation signature in genomic disorders and cancer genomes, its architecture remains unresolved. Here, we studied the genomic architecture of DUP-TRP/INV-DUP by investigating the DNA of 24 patients identified by array comparative genomic hybridization (aCGH) on whom we found evidence for the existence of 4 out of 4 predicted structural variant (SV) haplotypes. Using a combination of short-read genome sequencing (GS), long-read GS, optical genome mapping, and single-cell DNA template strand sequencing (strand-seq), the haplotype structure was resolved in 18 samples. The point of template switching in 4 samples was shown to be a segment of ∼2.2-5.5 kb of 100% nucleotide similarity within inverted repeat pairs. These data provide experimental evidence that inverted low-copy repeats act as recombinant substrates. This type of CGR can result in multiple conformers generating diverse SV haplotypes in susceptible dosage-sensitive loci.
Collapse
Affiliation(s)
| | | | - Haowei Du
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Mira Gandhi
- Pacific Northwest Research Institute, Seattle, WA 98122, USA
| | - Ming Yin Lun
- Pacific Northwest Research Institute, Seattle, WA 98122, USA
| | | | - KyungHee Park
- Pacific Northwest Research Institute, Seattle, WA 98122, USA
| | - Wolfram Höps
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Eva Benito
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Patrick Hasenfeld
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Medhat Mahmoud
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Luis F Paulin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shalini N Jhangiani
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - James Paul Hwang
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sravya V Bhamidipati
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Donna M Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jawid M Fatih
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Richard A Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | | | | | - Sissel Juul
- Oxford Nanopore Technologies, New York, NY 10013, USA
| | - Anna Lindstrand
- Department of Molecular Medicine and Surgery, Karolinska Institutet, 171 76 Stockholm, Sweden; Department of Clinical Genetics and Genomics, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Fritz J Sedlazeck
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Computer Science, Rice University, Houston TX 77030, USA
| | - Davut Pehlivan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Section of Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Texas Children's Hospital, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Texas Children's Hospital, Houston, TX 77030, USA
| | | |
Collapse
|
3
|
Weber T, Cosenza MR, Korbel J. MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq. Bioinformatics 2023; 39:btad633. [PMID: 37851409 PMCID: PMC10628386 DOI: 10.1093/bioinformatics/btad633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/23/2023] [Accepted: 10/17/2023] [Indexed: 10/19/2023] Open
Abstract
SUMMARY Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardized workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalization module, integration of the single-cell nucleosome occupancy and genetic variation analysis SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enable reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single-cell genomics, toward production environments. MosaiCatcher v2 is compatible with both container and conda environments, ensuring reproducibility and robustness and positioning the framework as a cornerstone in computational processing of Strand-seq data. AVAILABILITY AND IMPLEMENTATION MosaiCatcher v2 is a standardized workflow, implemented using the Snakemake workflow management system. The pipeline is available on GitHub: https://github.com/friendsofstrandseq/mosaicatcher-pipeline/ and on the snakemake-workflow-catalog: https://snakemake.github.io/snakemake-workflow-catalog/?usage=friendsofstrandseq/mosaicatcher-pipeline. Strand-seq example input data used in the publication can be found in the Data availability statement. Additionally, a lightweight dataset for test purposes can be found on the GitHub repository.
Collapse
Affiliation(s)
- Thomas Weber
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Jan Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Bridging Research Division on Mechanisms of Genomic Variation and Data Science, German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|
4
|
Grochowski CM, Bengtsson JD, Du H, Gandhi M, Lun MY, Mehaffey MG, Park K, Höps W, Benito-Garagorri E, Hasenfeld P, Korbel JO, Mahmoud M, Paulin LF, Jhangiani SN, Muzny DM, Fatih JM, Gibbs RA, Pendleton M, Harrington E, Juul S, Lindstrand A, Sedlazeck FJ, Pehlivan D, Lupski JR, Carvalho CMB. Break-induced replication underlies formation of inverted triplications and generates unexpected diversity in haplotype structures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560172. [PMID: 37873367 PMCID: PMC10592851 DOI: 10.1101/2023.10.02.560172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background The duplication-triplication/inverted-duplication (DUP-TRP/INV-DUP) structure is a type of complex genomic rearrangement (CGR) hypothesized to result from replicative repair of DNA due to replication fork collapse. It is often mediated by a pair of inverted low-copy repeats (LCR) followed by iterative template switches resulting in at least two breakpoint junctions in cis . Although it has been identified as an important mutation signature of pathogenicity for genomic disorders and cancer genomes, its architecture remains unresolved and is predicted to display at least four structural variation (SV) haplotypes. Results Here we studied the genomic architecture of DUP-TRP/INV-DUP by investigating the genomic DNA of 24 patients with neurodevelopmental disorders identified by array comparative genomic hybridization (aCGH) on whom we found evidence for the existence of 4 out of 4 predicted SV haplotypes. Using a combination of short-read genome sequencing (GS), long- read GS, optical genome mapping and StrandSeq the haplotype structure was resolved in 18 samples. This approach refined the point of template switching between inverted LCRs in 4 samples revealing a DNA segment of ∼2.2-5.5 kb of 100% nucleotide similarity. A prediction model was developed to infer the LCR used to mediate the non-allelic homology repair. Conclusions These data provide experimental evidence supporting the hypothesis that inverted LCRs act as a recombinant substrate in replication-based repair mechanisms. Such inverted repeats are particularly relevant for formation of copy-number associated inversions, including the DUP-TRP/INV-DUP structures. Moreover, this type of CGR can result in multiple conformers which contributes to generate diverse SV haplotypes in susceptible loci .
Collapse
|
5
|
Weber T, Cosenza MR, Korbel J. MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548805. [PMID: 37503087 PMCID: PMC10370012 DOI: 10.1101/2023.07.13.548805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardised workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalisation module, integration of the scNOVA SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enables reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single cell genomics, towards production environments.
Collapse
Affiliation(s)
- Thomas Weber
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Jan Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Bridging Research Division on Mechanisms of Genomic Variation and Data Science, German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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: 11] [Impact Index Per Article: 5.5] [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.
Collapse
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
| |
Collapse
|
8
|
Hanlon VCT, Lansdorp PM, Guryev V. A survey of current methods to detect and genotype inversions. Hum Mutat 2022; 43:1576-1589. [PMID: 36047337 DOI: 10.1002/humu.24458] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/11/2022]
Abstract
Polymorphic inversions are ubiquitous in humans, and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
| | - Peter M Lansdorp
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC, V5Z 1L3, Canada.,Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, 9713 AV, Groningen, The Netherlands
| |
Collapse
|
9
|
Hamadeh Z, Hanlon V, Lansdorp PM. Mapping of sister chromatid exchange events and genome alterations in single cells. Methods 2022; 204:64-72. [PMID: 35483548 DOI: 10.1016/j.ymeth.2022.04.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 04/08/2022] [Accepted: 04/22/2022] [Indexed: 12/14/2022] Open
Abstract
Mammalian genomes encode over a hundred different helicases, many of which are implicated in the repair of DNA lesions by acting on DNA structures arising during DNA replication, recombination or transcription. Defining the in vivo substrates of such DNA helicases is a major challenge given the large number of helicases in the genome, the breadth of potential substrates in the genome and the degree of genetic pleiotropy among DNA helicases in resolving diverse substrates. Helicases such as WRN, BLM and RECQL5 are implicated in the resolution of error-free recombination events known as sister chromatid exchange events (SCEs). Single cell Strand-seq can be used to map the genomic location of individual SCEs at a resolution that exceeds that of classical cytogenetic techniques by several orders of magnitude. By mapping the genomic locations of SCEs in the absence of different helicases, it should in principle be possible to infer the substrate specificity of specific helicases. Here we describe how the genome can be interrogated for such DNA repair events using single-cell template strand sequencing (Strand-seq) and bioinformatic tools. SCEs and copy-number alterations were mapped to genomic locations at kilobase resolution in haploid KBM7 cells. Strategies, possibilities, and limitations of Strand-seq to study helicase function are illustrated using these cells before and after CRISPR/Cas9 knock out of WRN, BLM and/or RECQL5.
Collapse
Affiliation(s)
- Zeid Hamadeh
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada; Department of Genome Science and Technology, University of British Columbia, Vancouver, BC, Canada
| | - Vincent Hanlon
- Terry Fox Laboratory, BC Cancer Agency, Vancouver, BC V5Z 1L3, Canada
| | - Peter M Lansdorp
- Departments of Medical Genetics and Hematology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|