ArticleBMC genomics2015
REDHORSE-REcombination and Double crossover detection in Haploid Organisms using next-geneRation SEquencing data.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii.Journal of visualized experiments : JoVE · 2017Article
- Rhoptry Proteins ROP5 and ROP18 Are Major Murine Virulence Factors in Genetically Divergent South American Strains of Toxoplasma gondii.PLoS genetics · 2015Article
- NextGen sequencing reveals short double crossovers contribute disproportionately to genetic diversity in Toxoplasma gondii.BMC genomics · 2014Article
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4 authors.
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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/ .
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