Simulation with RADinitio improves RADseq experimental design and sheds light on sources of missing data
| Autor: | Angel G. Rivera-Colón, Nicolas C. Rochette, Julian M. Catchen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
0301 basic medicine Computer science Test data generation Library preparation Population Biology computer.software_genre Machine learning 010603 evolutionary biology 01 natural sciences DNA sequencing Population genomics 03 medical and health sciences Genetics Computer Simulation education Ecology Evolution Behavior and Systematics Selection (genetic algorithm) Protocol (science) education.field_of_study business.industry Genomics Sequence Analysis DNA Missing data Simulation software 030104 developmental biology Research Design Artificial intelligence Metagenomics business computer Software Biotechnology |
| Zdroj: | Molecular ecology resourcesREFERENCES. 21(2) |
| ISSN: | 1755-0998 |
| Popis: | Restriction-site Associated DNA sequencing (RADseq) has become a powerful and versatile tool in modern population genomics, enabling large-scale genomic analyses in otherwise inaccessible biological systems. With its widespread use, different variants on the protocol have been developed to suit specific experimental needs. Researchers face the challenge of choosing the optimal molecular and sequencing protocols for their experimental design, an often-complicated process. Strategic errors can lead to improper data generation that has reduced power to answer biological questions. Here we present RADinitio, simulation software for the selection and optimization of RADseq experiments via the generation of sequencing data that behaves similarly to empirical sources. RADinitio provides an evolutionary simulation of populations, implementation of various RADseq protocols with customizable parameters, and thorough assessment of missing data. Using the software, we test its efficacy using different RAD protocols across several organisms, highlighting the importance of protocol selection on the magnitude and quality of data acquired. Additionally, we test the effects of RAD library preparation and sequencing on allelic dropout, observing that library preparation and sequencing often contributes more to missing alleles than population-level variation. |
| Databáze: | OpenAIRE |
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