4
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Shand M, Soto J, Lichtenstein L, Benjamin D, Farjoun Y, Brody Y, Maruvka Y, Blainey PC, Banks E. A validated lineage-derived somatic truth data set enables benchmarking in cancer genome analysis. Commun Biol 2020; 3:744. [PMID: 33293579 PMCID: PMC7722876 DOI: 10.1038/s42003-020-01460-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 11/04/2020] [Indexed: 11/30/2022] Open
Abstract
Existing cancer benchmark data sets for human sequencing data use germline variants, synthetic methods, or expensive validations, none of which are satisfactory for providing a large collection of true somatic variation across a whole genome. Here we propose a data set, Lineage derived Somatic Truth (LinST), of short somatic mutations in the HT115 colon cancer cell-line, that are validated using a known cell lineage that includes thousands of mutations and a high confidence region covering 2.7 gigabases per sample. Megan Shand et al. present Lineage derived Somatic Truth (LinST), a validated data set of somatic mutations from a colon cancer cell line with a known lineage tree structure. They show that LinST can be used to benchmark true-positive and false-positive rates in somatic variant-calling pipelines applied to cancer genomic data.
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Affiliation(s)
- Megan Shand
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Jose Soto
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | | | - Yossi Farjoun
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yehuda Brody
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.,Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yosef Maruvka
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.,MGH Cancer Center and Department of Pathology, Boston, MA, USA
| | - Paul C Blainey
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.,MIT Department of Biological Engineering, Cambridge, MA, USA.,Koch Institute for Integrative Cancer Research at MIT, Cambridge, MA, USA
| | - Eric Banks
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
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6
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Magi A, Bolognini D, Bartalucci N, Mingrino A, Semeraro R, Giovannini L, Bonifacio S, Parrini D, Pelo E, Mannelli F, Guglielmelli P, Maria Vannucchi A. Nano-GLADIATOR: real-time detection of copy number alterations from nanopore sequencing data. Bioinformatics 2020; 35:4213-4221. [PMID: 30949684 DOI: 10.1093/bioinformatics/btz241] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 03/05/2019] [Accepted: 04/03/2019] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION The past few years have seen the emergence of nanopore-based sequencing technologies which interrogate single molecule of DNA and generate reads sequentially. RESULTS In this paper, we demonstrate that, thanks to the sequentiality of the nanopore process, the data generated in the first tens of minutes of a typical MinION/GridION run can be exploited to resolve the alterations of a human genome at a karyotype level with a resolution in the order of tens of Mb, while the data produced in the first 6-12 h allow to obtain a resolution comparable to currently available array-based technologies, and thanks to a novel probabilistic approach are capable to predict the allelic fraction of genomic alteration with high accuracy. To exploit the unique characteristics of nanopore sequencing data we developed a novel software tool, Nano-GLADIATOR, that is capable to perform copy number variants/alterations detection and allelic fraction prediction during the sequencing run ('On-line' mode) and after experiment completion ('Off-line' mode). We tested Nano-GLADIATOR on publicly available ('Off-line' mode) and on novel whole genome sequencing dataset generated with MinION device ('On-line' mode) showing that our tool is capable to perform real-time copy number alterations detection obtaining good results with respect to other state-of-the-art tools. AVAILABILITY AND IMPLEMENTATION Nano-GLADIATOR is freely available at https://sourceforge.net/projects/nanogladiator/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alberto Magi
- Department of Information Engineering, University of Florence, Florence, Italy
| | - Davide Bolognini
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Niccoló Bartalucci
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy
| | - Alessandra Mingrino
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Roberto Semeraro
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Luna Giovannini
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Stefania Bonifacio
- Department of Laboratory Diagnosis, Genetic Diagnosis Service, Careggi Teaching Hospital, Florence, Italy
| | - Daniela Parrini
- Department of Laboratory Diagnosis, Genetic Diagnosis Service, Careggi Teaching Hospital, Florence, Italy
| | - Elisabetta Pelo
- Department of Laboratory Diagnosis, Genetic Diagnosis Service, Careggi Teaching Hospital, Florence, Italy
| | - Francesco Mannelli
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy
| | - Paola Guglielmelli
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy
| | - Alessandro Maria Vannucchi
- Department of Experimental and Clinical Medicine, CRIMM, Center Research and Innovation of Myeloproliferative Neoplasms, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence, Italy
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7
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Tattini L, Tellini N, Mozzachiodi S, D'Angiolo M, Loeillet S, Nicolas A, Liti G. Accurate Tracking of the Mutational Landscape of Diploid Hybrid Genomes. Mol Biol Evol 2020; 36:2861-2877. [PMID: 31397846 PMCID: PMC6878955 DOI: 10.1093/molbev/msz177] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Mutations, recombinations, and genome duplications may promote genetic diversity and trigger evolutionary processes. However, quantifying these events in diploid hybrid genomes is challenging. Here, we present an integrated experimental and computational workflow to accurately track the mutational landscape of yeast diploid hybrids (MuLoYDH) in terms of single-nucleotide variants, small insertions/deletions, copy-number variants, aneuploidies, and loss-of-heterozygosity. Pairs of haploid Saccharomyces parents were combined to generate ancestor hybrids with phased genomes and varying levels of heterozygosity. These diploids were evolved under different laboratory protocols, in particular mutation accumulation experiments. Variant simulations enabled the efficient integration of competitive and standard mapping of short reads, depending on local levels of heterozygosity. Experimental validations proved the high accuracy and resolution of our computational approach. Finally, applying MuLoYDH to four different diploids revealed striking genetic background effects. Homozygous Saccharomyces cerevisiae showed a ∼4-fold higher mutation rate compared with its closely related species S. paradoxus. Intraspecies hybrids unveiled that a substantial fraction of the genome (∼250 bp per generation) was shaped by loss-of-heterozygosity, a process strongly inhibited in interspecies hybrids by high levels of sequence divergence between homologous chromosomes. In contrast, interspecies hybrids exhibited higher single-nucleotide mutation rates compared with intraspecies hybrids. MuLoYDH provided an unprecedented quantitative insight into the evolutionary processes that mold diploid yeast genomes and can be generalized to other genetic systems.
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Affiliation(s)
- Lorenzo Tattini
- CNRS UMR7284, INSERM, IRCAN, Université Côte d'Azur, Nice, France
| | - Nicolò Tellini
- CNRS UMR7284, INSERM, IRCAN, Université Côte d'Azur, Nice, France
| | | | | | - Sophie Loeillet
- CNRS UMR3244, Institut Curie, PSL Research University, Paris, France
| | - Alain Nicolas
- CNRS UMR3244, Institut Curie, PSL Research University, Paris, France
| | - Gianni Liti
- CNRS UMR7284, INSERM, IRCAN, Université Côte d'Azur, Nice, France
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8
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Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A, Pinello L, Skums P, Stamatakis A, Attolini CSO, Aparicio S, Baaijens J, Balvert M, Barbanson BD, Cappuccio A, Corleone G, Dutilh BE, Florescu M, Guryev V, Holmer R, Jahn K, Lobo TJ, Keizer EM, Khatri I, Kielbasa SM, Korbel JO, Kozlov AM, Kuo TH, Lelieveldt BP, Mandoiu II, Marioni JC, Marschall T, Mölder F, Niknejad A, Rączkowska A, Reinders M, Ridder JD, Saliba AE, Somarakis A, Stegle O, Theis FJ, Yang H, Zelikovsky A, McHardy AC, Raphael BJ, Shah SP, Schönhuth A. Eleven grand challenges in single-cell data science. Genome Biol 2020; 21:31. [PMID: 32033589 PMCID: PMC7007675 DOI: 10.1186/s13059-020-1926-6] [Citation(s) in RCA: 575] [Impact Index Per Article: 143.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/02/2020] [Indexed: 02/08/2023] Open
Abstract
The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
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Affiliation(s)
- David Lähnemann
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Department of Paediatric Oncology, Haematology and Immunology, Medical Faculty, Heinrich Heine University, University Hospital, Düsseldorf, Germany
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Johannes Köster
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - Ewa Szczurek
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland
| | - Davis J. McCarthy
- Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Australia
- Melbourne Integrative Genomics, School of BioSciences–School of Mathematics & Statistics, Faculty of Science, University of Melbourne, Melbourne, Australia
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD USA
| | - Mark D. Robinson
- Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zürich, Zürich, Switzerland
| | - Catalina A. Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- The Alan Turing Institute, British Library, London, UK
| | - Kieran R. Campbell
- Department of Statistics, University of British Columbia, Vancouver, Canada
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada
- Data Science Institute, University of British Columbia, Vancouver, Canada
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ahmed Mahfouz
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Luca Pinello
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Alexandros Stamatakis
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
- Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Jasmijn Baaijens
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - Marleen Balvert
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
| | - Buys de Barbanson
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
- Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands
| | - Antonio Cappuccio
- Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands
| | - Giacomo Corleone
- Department of Surgery and Cancer, The Imperial Centre for Translational and Experimental Medicine, Imperial College London, London, UK
| | - Bas E. Dutilh
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Maria Florescu
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
- Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rens Holmer
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Thamar Jessurun Lobo
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Emma M. Keizer
- Biometris, Wageningen University & Research, Wageningen, The Netherlands
| | - Indu Khatri
- Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands
| | - Szymon M. Kielbasa
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan O. Korbel
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Alexey M. Kozlov
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Tzu-Hao Kuo
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Boudewijn P.F. Lelieveldt
- PRB lab, Delft University of Technology, Delft, The Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ion I. Mandoiu
- Computer Science & Engineering Department, University of Connecticut, Storrs, USA
| | - John C. Marioni
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Felix Mölder
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Amir Niknejad
- Computation molecular design, Zuse Institute Berlin, Berlin, Germany
- Mathematics Department, Mount Saint Vincent, New York, USA
| | - Alicja Rączkowska
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland
| | - Marcel Reinders
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Jeroen de Ridder
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Antoine-Emmanuel Saliba
- Helmholtz Institute for RNA-based Infection Research, Helmholtz-Center for Infection Research, Würzburg, Germany
| | - Antonios Somarakis
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center–DKFZ, Heidelberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Huan Yang
- Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research–LACDR–Leiden University, Leiden, The Netherlands
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, USA
- The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alice C. McHardy
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | | | - Sohrab P. Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Alexander Schönhuth
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
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9
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Juan L, Wang Y, Jiang J, Yang Q, Jiang Q, Wang Y. PGsim: A Comprehensive and Highly Customizable Personal Genome Simulator. Front Bioeng Biotechnol 2020; 8:28. [PMID: 32047747 PMCID: PMC6997238 DOI: 10.3389/fbioe.2020.00028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 01/13/2020] [Indexed: 11/26/2022] Open
Abstract
Although genome sequencing has become increasingly popular, the simulation of individual genomes is still important. This is because sequencing a large number of individual genomes is costly and genome data with extreme and boundary conditions, such as fatal genetic defects, are difficult to obtain. Privacy and legal barriers also prevent many applications of real data. Large sequencing projects in recent years have provided a deeper understanding of the human genome. However, there is a lack of tools to leverage known data to simulate personal genomes as real as possible. Here, we designed and developed PGsim, a comprehensive and highly customizable individual genome simulator, that fully uses existing knowledge, such as variant allele frequencies in global or world main populations, mutation probability differences between protein-coding regions and non-coding regions, transition/transversion (Ti/Tv) ratios, Indel incidence, Indel length distribution, structural variation sites, and pathogenic mutation sites. Users can flexibly control the proportion and quantity of known variants, common variants, novel variants in both coding and non-coding regions, and special variants through detailed parameter settings. To ensure that the simulated personal genome has sufficient randomness, PGsim makes the generated variants more real and reliable in terms of variant distribution, proportion, and population characteristics. PGsim is able to employ a huge volume database as background data to simulate personal genomes and does not require SQL database support. Users can easily change the variant databases used as needed. As a Perl script, there is no obstacle to running PGsim on any version of the MAC OS or Linux systems, and no libraries, packages, interpreters, compilers, or other dependencies need to be installed in advance. The PGsim tool is publicly available at https://github.com/lrjuan/PGsim.
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Affiliation(s)
- Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yongtian Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jingyi Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qi Yang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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