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MBV: a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets

Fort, Alexandre ; Panousis, Nikolaos I. ; Garieri, Marco ; Antonarakis, Stylianos E. ; Lappalainen, Tuuli ; Dermitzakis, Emmanouil T. ; Delaneau, Olivier ; Stegle, Oliver ; Stegle, Oliver

In: Bioinformatics, 2017, vol. 33, no. 12, p. 1895-1897

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    Titre
    • MBV: a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets
    Auteur
    • Fort, Alexandre. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
    • Panousis, Nikolaos I.. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
    • Garieri, Marco. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
    • Antonarakis, Stylianos E.. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
    • Lappalainen, Tuuli. New York Genome Center, New York, USA
    • Dermitzakis, Emmanouil T.. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
    • Delaneau, Olivier. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
    • Stegle, Oliver. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
    • Stegle, Oliver. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
    Type de document
    • Postprint
    Identifiant OAI-PMH
    • oai:doc.rero.ch:331110
    Summary
    Abstract Motivation Large genomic datasets combining genotype and sequence data, such as for expression quantitative trait loci (eQTL) detection, require perfect matching between both data types. Results We described here MBV (Match BAM to VCF); a method to quickly solve sample mislabeling and detect cross-sample contamination and PCR amplification bias. Availability and Implementation MBV is implemented in C ++ as an independent component of the QTLtools software package, the binary and source codes are freely available at https://qtltools.github.io/qtltools/. Supplementary information Supplementary data are available at Bioinformatics online.