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

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

doi:10.1093/bioinformatics/btx074

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

Bioinformatics. 2017 Jun 15;33(12):1895-1897. doi: 10.1093/bioinformatics/btx074.

Authors

Alexandre Fort¹, Nikolaos I Panousis^{1

2

3}, Marco Garieri^{1

2

3}, Stylianos E Antonarakis^{1

3}, Tuuli Lappalainen^{4

5}, Emmanouil T Dermitzakis^{1

2

3}, Olivier Delaneau^{1

2

3}

Affiliations

¹ Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.
² Swiss Institute of Bioinformatics, Lausanne, Switzerland.
³ Institute of Genetics and Genomics in Geneva, Geneva, Switzerland.
⁴ New York Genome Center, New York, USA.
⁵ Department of Systems Biology, Columbia University, New York, USA.

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/ .

Contact: olivier.delaneau@unige.ch or emmanouil.dermitzakis@unige.ch.

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

Bias
Genomics / methods
Genomics / standards
Genotyping Techniques / methods*
Genotyping Techniques / standards
Humans
Quantitative Trait Loci*
Sequence Analysis, DNA / methods*
Sequence Analysis, DNA / standards
Software*

Grants and funding

R01 MH101814/MH/NIMH NIH HHS/United States