REDHORSE-REcombination and Double crossover detection in Haploid Organisms using next-geneRation SEquencing data

Abstract

BackgroundNext-generation sequencing technology provides a means to study genetic exchange at a higher resolution than was possible using earlier technologies. However, this improvement presents challenges as the alignments of next generation sequence data to a reference genome cannot be directly used as input to existing detection algorithms, which instead typically use multiple sequence alignments as input. We therefore designed a software suite called REDHORSE that uses genomic alignments, extracts genetic markers, and generates multiple sequence alignments that can be used as input to existing recombination detection algorithms. In addition, REDHORSE implements a custom recombination detection algorithm that makes use of sequence information and genomic positions to accurately detect crossovers. REDHORSE is a portable and platform independent suite that provides efficient analysis of genetic crosses based on Next-generation sequencing data.ResultsWe demonstrated the utility of REDHORSE using simulated data and real Next-generation sequencing data. The simulated dataset mimicked recombination between two known haploid parental strains and allowed comparison of detected break points against known true break points to assess performance of recombination detection algorithms. A newly generated NGS dataset from a genetic cross of Toxoplasma gondii allowed us to demonstrate our pipeline. REDHORSE successfully extracted the relevant genetic markers and was able to transform the read alignments from NGS to the genome to generate multiple sequence alignments. Recombination detection algorithm in REDHORSE was able to detect conventional crossovers and double crossovers typically associated with gene conversions whilst filtering out artifacts that might have been introduced during sequencing or alignment. REDHORSE outperformed other commonly used recombination detection algorithms in finding conventional crossovers. In addition, REDHORSE was the only algorithm that was able to detect double crossovers.ConclusionREDHORSE is an efficient analytical pipeline that serves as a bridge between genomic alignments and existing recombination detection algorithms. Moreover, REDHORSE is equipped with a recombination detection algorithm specifically designed for Next-generation sequencing data. REDHORSE is portable, platform independent Java based utility that provides efficient analysis of genetic crosses based on Next-generation sequencing data. REDHORSE is available at http://redhorse.sourceforge.net/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1309-7) contains supplementary material, which is available to authorized users.

NextGen sequencing reveals short double crossovers contribute disproportionately to genetic diversity in Toxoplasma gondii

“…To more precisely identify SNPs and estimate the recombination intervals, we used a new utility called REDHORSE, which is described in an accompanying software methods paper [ 33 ]. In brief, the REDHORSE software identifies putative recombination breakpoints by evaluating SNPs in each progeny clone and comparing them to the genotype of the parents.…”

“…The raw reads from the parental lines as well as hybrids were aligned to the ME49 genome v 8.0 by CLC genomics ( http://www.clcbio.com ) using the following parameters- Mismatch cost: 3, Insertion cost: 3, Deletion cost: 3, Length fraction: 0.9, Similarity fraction: 0.8 and by using Global alignment. Since the ME49 genome is haploid, we extracted the alleles with frequency greater than or equal to 80% and having a minimum coverage of 5 using REDHORSE [ 33 ]. Loci that did not meet these criteria were tagged as missing data.…”

“…SNPs between the parental lines were used to define the inheritance of alleles in the hybrids. We identified a total of 532,949 SNPs in VAND and 1,821 SNPs in ME49 to compare raw reads to the reference ME49 genome in ToxoDB using REDHORSE [ 33 ]. The 301 SNPs that were common across both the parental lines were filtered out as they represent loci where both parents are different from the reference genome but are not different with respect to each other.…”

Genetic Mapping of Pathogenesis Determinants inToxoplasma gondii

“…With the advent of microarray technology, crosses could be genotyped at over a thousand marker positions across the genome based on parental SNPs residing within probes on the array, as was done for the type 1 × type 2 cross (9). With NextGen sequencing, genomes can be completely sequenced to identify all the SNPs in the parents and progeny, yielding many thousands of markers (106). The type 2 × type 10 cross was genotyped using whole-genome sequencing allowing for the identification of recombination breakpoints and short double-crossover events in the progeny (60).…”

Rhoptry Proteins ROP5 and ROP18 Are Major Murine Virulence Factors in Genetically Divergent South American Strains of Toxoplasma gondii

“…We utilized a previously described genetic cross between type 2 ME49-FUDR r and type 10 VAND-SNF r strains [ 25 , 28 ]. Whole-genome sequencing of 24 progeny and mapping based on genome-wide SNP analysis was previously used to identify the molecular basis of sinefungin resistance [ 29 ].…”