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Performance evaluation of six popular short-read simulators. Heredity (Edinb) 2023; 130:55-63. [PMID: 36496447 PMCID: PMC9905089 DOI: 10.1038/s41437-022-00577-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/10/2022] [Accepted: 11/11/2022] [Indexed: 12/14/2022] Open
Abstract
High-throughput sequencing data enables the comprehensive study of genomes and the variation therein. Essential for the interpretation of this genomic data is a thorough understanding of the computational methods used for processing and analysis. Whereas "gold-standard" empirical datasets exist for this purpose in humans, synthetic (i.e., simulated) sequencing data can offer important insights into the capabilities and limitations of computational pipelines for any arbitrary species and/or study design-yet, the ability of read simulator software to emulate genomic characteristics of empirical datasets remains poorly understood. We here compare the performance of six popular short-read simulators-ART, DWGSIM, InSilicoSeq, Mason, NEAT, and wgsim-and discuss important considerations for selecting suitable models for benchmarking.
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Johri P, Aquadro CF, Beaumont M, Charlesworth B, Excoffier L, Eyre-Walker A, Keightley PD, Lynch M, McVean G, Payseur BA, Pfeifer SP, Stephan W, Jensen JD. Recommendations for improving statistical inference in population genomics. PLoS Biol 2022; 20:e3001669. [PMID: 35639797 PMCID: PMC9154105 DOI: 10.1371/journal.pbio.3001669] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.
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Affiliation(s)
- Parul Johri
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Charles F. Aquadro
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, United States of America
| | - Mark Beaumont
- School of Biological Sciences, University of Bristol, Bristol, United Kingdom
| | - Brian Charlesworth
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Laurent Excoffier
- Institute of Ecology and Evolution, University of Berne, Berne, Switzerland
| | - Adam Eyre-Walker
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Peter D. Keightley
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael Lynch
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Gil McVean
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Bret A. Payseur
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Susanne P. Pfeifer
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | | | - Jeffrey D. Jensen
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
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Bergeron LA, Besenbacher S, Schierup MH, Zhang G. Studying mutation rate evolution in primates-a need for systematic comparison of computational pipelines. Gigascience 2021; 10:giab072. [PMID: 34673928 PMCID: PMC8529962 DOI: 10.1093/gigascience/giab072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 11/14/2022] Open
Abstract
The lack of consensus methods to estimate germline mutation rates from pedigrees has led to substantial differences in computational pipelines in the published literature. Here, we answer Susanne Pfeifer's opinion piece discussing the pipeline choices of our recent article estimating the germline mutation rate of rhesus macaques (Macaca mulatta). We acknowledge the differences between the method that we applied and the one preferred by Pfeifer. Yet, we advocate for full transparency and justification of choices as long as rigorous comparison of pipelines remains absent because it is the only way to conclude on best practices for the field.
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Affiliation(s)
- Lucie A Bergeron
- Section for Ecology and Evolution, Department of Biology, University of
Copenhagen, 2100, Copenhagen, Denmark
| | - Søren Besenbacher
- Department of Molecular Medicine, Aarhus University, 8200,
Aarhus, Denmark
| | - Mikkel H Schierup
- Bioinformatics Research Centre, Aarhus University, 8000,
Aarhus, Denmark
| | - Guojie Zhang
- Section for Ecology and Evolution, Department of Biology, University of
Copenhagen, 2100, Copenhagen, Denmark
- BGI-Shenzhen, Shenzhen, Guangdong 518083, China
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute
of Zoology, Chinese Academy of Sciences, Kunming 650223, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of
Sciences, Kunming 650223, China
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