ReCombine: A Suite of Programs for Detection and Analysis of Meiotic Recombination in Whole-Genome Datasets

Abstract

In meiosis, the exchange of DNA between chromosomes by homologous recombination is a critical step that ensures proper chromosome segregation and increases genetic diversity. Products of recombination include reciprocal exchanges, known as crossovers, and non-reciprocal gene conversions or non-crossovers. The mechanisms underlying meiotic recombination remain elusive, largely because of the difficulty of analyzing large numbers of recombination events by traditional genetic methods. These traditional methods are increasingly being superseded by high-throughput techniques capable of surveying meiotic recombination on a genome-wide basis. Next-generation sequencing or microarray hybridization is used to genotype thousands of polymorphic markers in the progeny of hybrid yeast strains. New computational tools are needed to perform this genotyping and to find and analyze recombination events. We have developed a suite of programs, ReCombine, for using short sequence reads from next-generation sequencing experiments to genotype yeast meiotic progeny. Upon genotyping, the program CrossOver, a component of ReCombine, then detects recombination products and classifies them into categories based on the features found at each location and their distribution among the various chromatids. CrossOver is also capable of analyzing segregation data from microarray experiments or other sources. This package of programs is designed to allow even researchers without computational expertise to use high-throughput, whole-genome methods to study the molecular mechanisms of meiotic recombination.

Introgression browser: high‐throughput whole‐genome SNP visualization

“…Normally, whenever lack of coverage occurs, the reference sequence is assumed. Because such cases hamper the detection of introgressions (Canady et al, 2006;Anderson et al, 2011), we included a filter specifically designed to handle ILs with low coverage sequencing. It assumes that the genotype of the offspring is identical to either the recipient or donor parent (see Experimental Procedures).…”

“…SNPs are becoming increasingly important in establishing source identity of introgressions (Paraskevis et al, 2005;Huang et al, 2009;Anderson et al, 2011;Austin et al, 2011;Prasad et al, 2013;V ıquez-Zamora et al, 2013;Chen et al, 2014). Plant genomes, also those of crop species, contain massive amounts of SNPs in their gene pools.…”

“…Coordinates are excluded if any of the following constraints are not satisfied: (i) at least two individuals show polymorphism; (ii) any individual shows heterozygosity; or (iii) any individual contains an InDel, a protocol modified from Lee et al (2014). We filter out InDels because they are difficult to align and score (Anderson et al, 2011). The reason is that they are generally not fixed in the population and are usually of little value for marker-assisted breeding.…”

“…However, the power of marker-assisted technologies to detect and delineate introgressions and chromosome rearrangements is limited due to low marker placement accuracy and even lack of markers (Anderson et al, 2011;Kumar et al, 2012;V ıquez-Zamora et al, 2013). In addition, sequence duplications, heterozygosity, and discrepancies between genetic and physical maps can seriously hamper data interpretation.…”

“…Several methods to visualize such whole-genome SNP (wgSNP) data have been presented (Posada, 2002;Martin et al, 2011;Lechat et al, 2013;Kim et al, 2014) including software packages such as VISRD (Strimmer et al, 2003), SHOREMAP (Schneeberger et al, 2009), RECOMBINE (Anderson et al, 2011), NGM (Austin et al, 2011), PARTFINDER (Prasad et al, 2013) and PHYLONET-HMM . SHOREMAP and NGM focus on identifying causal SNPs for phenotypes in segregating populations.…”