Special features of RAD Sequencing data: implications for genotyping

By J. Davey, Timothee Cezard, P. Fuentes-Utrilla, Cathlene Eland, Karim Gharbi, Mark L. Blaxter. Molecular Ecology, 2012

365 citations recorded by the source corpus.

Restriction site‐associated DNA Sequencing (RAD‐Seq) is an economical and efficient method for SNP discovery and genotyping. As with other sequencing‐by‐synthesis methods, RAD‐Seq produces stochastic count data and requires sensitive analysis to develop or genotype markers accurately. We show that there are several sources of bias specific to RAD‐Seq that are not explicitly addressed by current genotyping tools, namely restriction fragment bias, restriction site heterozygosity and PCR GC content bias. We explore the performance of existing analysis tools given these biases and discuss approaches to limiting or handling biases in RAD‐Seq data. While these biases need to be taken seriously, we believe RAD loci affected by them can be excluded or processed with relative ease in most cases and that most RAD loci will be accurately genotyped by existing tools.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5bb06f7186251a13802cce39574979861b9ba0d9

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Citations in the corpus

Genome Editing of Plants (2017, methodology): “For example, two herbicide tolerance genes were stacked into a previously integrated insect control locus in cotton using homology-directed repair of a meganuclease-mediated DSB (D’Halluin et al., 2013).”
High-Throughput Genomic Data in Systematics and Phylogenetics (2013, methodology): “Other limitations of RAD-seq are reviewed elsewhere (Davey et al. 2012, Arnold et al. 2013).”
Stacks: an analysis tool set for population genomics (2013, background): “However, the causes of sequencing depth variation, and their effects on distributional assumptions, are poorly understood for short-read sequencing in general and GBS approaches in particular (Davey et al. 2012; Gautier et al. 2012).”
Lost in parameter space: a road map for stacks (2017, background): “Diﬀerences in both natural polymorphism and read depths can vary across individuals (Davey et al. 2013), and so exploring discrepancies between the individuals across a dataset are important.”
Restriction site‐associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference (2015, methodology): “…sequences are assembled into loci and alleles, often using distance-based criteria, genotyping results will vary according to the algorithm used (Davey et al. 2013; note that we refer to a locus as a short DNA sequence produced by clustering together unique RAD alleles ; in turn, alleles differ…”
A framework for incorporating evolutionary genomics into biodiversity conservation and management (2015, methodology): “These approaches can be used to answer a wide variety of questions on population structure and phylogeography; see [51,53,57].”