Evidence map›Paper›PMID 25766039›Full record

ArticleBMC genomics2015

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

Jahangheer S Shaik, Asis Khan, Stephen M Beverley, L David Sibley

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jahangheer S ShaikDepartment of Molecular Biology, Washington University School of Medicine, Box 8230, , 600 S. Euclid Ave., St. Louis, MO, 63110, USA. shaik@wusm.wustl.edu.
Asis KhanDepartment of Molecular Biology, Washington University School of Medicine, Box 8230, , 600 S. Euclid Ave., St. Louis, MO, 63110, USA. asis.khan@nih.gov.
Stephen M BeverleyDepartment of Molecular Biology, Washington University School of Medicine, Box 8230, , 600 S. Euclid Ave., St. Louis, MO, 63110, USA. beverley@wusm.wustl.edu.
L David SibleyDepartment of Molecular Biology, Washington University School of Medicine, Box 8230, , 600 S. Euclid Ave., St. Louis, MO, 63110, USA. sibley@wusm.wustl.edu.

Funding

MOLECULAR GENETICS OF LEISHMANIA USING DNA TRANSFECTIONR01AI029646 · NIAID · WASHINGTON UNIVERSITY · PI BEVERLEY, STEPHEN M · 1990 to 2015
$9.3M
Molecular Basis of Human ToxoplasmosisR01AI118426 · NIAID · WASHINGTON UNIVERSITY · PI SIBLEY, L. DAVID · 2015 to 2024
$6.0M
MOLECULAR BASIS OF TOXOPLASMOSIS IN AIDS PATIENTSR01AI036629 · NIAID · WASHINGTON UNIVERSITY · PI SIBLEY, L. DAVID · 1994 to 2013
$5.3M
Infectivity and Transmission of ToxoplasmosisR01AI059176 · NIAID · WASHINGTON UNIVERSITY · PI SIBLEY, L. DAVID · 2004 to 2013
$3.9M
NIAID NIH HHS AI036629NIAID NIH HHS AI059176NIAID NIH HHS AI29646NIAID NIH HHS R01 AI029646NIAID NIH HHS R01 AI036629NIAID NIH HHS R01 AI059176NIAID NIH HHS R01 AI118426
6 · The paper itself

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/ .

Indexed as

High-Throughput Nucleotide SequencingRecombination, GeneticSequence Analysis, DNASoftwareAlgorithmsGenomicsHaploidySequence Alignment

Identifiers

PMID25766039
PMCPMC4348101

What OpenQuestion holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.