Introduction¶
mrbait is a software pipeline for identifying regions of interest in DNA sequence data and designing probes to enrich them.
A variety of genome reduction methods have been implemented to reduce costs of applying next-generation sequencing methods to non-model organisms, or projects with large numbers of samples (e.g. those focusing on the population scale). These can broadly be classified into those which use restriction enzymes and size selection for subsampling genomic complexity [e.g. RADseq methods (Baird et al., 2008; Peterson et al., 2012)], and those which enrich for fragments selected a priori using biotinylated RNA ‘baits’ (Lemmon et al., 2012; McCormack et al., 2012). The latter benefit from increased specificity, yet require some genomic information for marker development. To mitigate, some take a hybrid approach by using baits to enrich RAD loci which are most consistently recovered, or to maximize capture of parsimony-informative variation (Ali et al., 2016; Hoffberg et al., 2016).
Applying these targeted-enrichment methods (either via RAD-capture methods, ultra-conserved elements, or anchored-enrichment) requires first bioinformatic processing to parse large alignments, identify candidate regions for bait-design, and design of complementary oligonucleotide sequences for synthesis. Although some developers are transparent in providing computational resources and workflows to design such probe sets (e.g. see Faircloth 2017), a generalized and flexible pipeline does not yet exist. The motivation behind MrBait was to provide such a resource, which could be universally applied to differing bait enrichment strategies (e.g. targeting ultra-conserved regions vs. functional elements), and facilitate diverse quality control methods to mitigate non-target capture (contamination, etc), target-target hybridization, ambiguous mapping, and enrichment of repetitive DNA.
mrbait code is open-source and freely available at on GitHub
Official releases can be found here