title: MultiGeMS: detection of SNVs from multiple samples using model selection on high-throughput sequencing data.
abstract: Single nucleotide variant (SNV) detection procedures are being utilized as never before to analyze the recent abundance of high-throughput DNA sequencing data, both on single and multiple sample datasets. Building on previously published work with the single sample SNV caller genotype model selection (GeMS), a multiple sample version of GeMS (MultiGeMS) is introduced. Unlike other popular multiple sample SNV callers, the MultiGeMS statistical model accounts for enzymatic substitution sequencing errors. It also addresses the multiple testing problem endemic to multiple sample SNV calling and utilizes high performance computing (HPC) techniques.
  
  
  
  A simulation study demonstrates that MultiGeMS ranks highest in precision among a selection of popular multiple sample SNV callers, while showing exceptional recall in calling common SNVs. Further, both simulation studies and real data analyses indicate that MultiGeMS is robust to low-quality data. We also demonstrate that accounting for enzymatic substitution sequencing errors not only improves SNV call precision at low mapping quality regions, but also improves recall at reference allele-dominated sites with high mapping quality.
  
  
  
  The MultiGeMS package can be downloaded from https://github.com/cui-lab/multigems
  
  
  
  xinping.cui@ucr.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, University of California, Riverside, CA 92521, USA.
authors.forename: Gabriel H
authors.initials: GH
authors.lastname: Murillo
authors.affiliation: Department of Statistical Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, China.
authors.forename: Na
authors.initials: N
authors.lastname: You
authors.affiliation: Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China.
authors.forename: Xiaoquan
authors.initials: X
authors.lastname: Su
authors.affiliation: Department of Statistics, University of California, Riverside, CA 92521, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Cui
authors.affiliation: Cardiovascular Institute.
authors.forename: Muredach P
authors.initials: MP
authors.lastname: Reilly
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
authors.forename: Mingyao
authors.initials: M
authors.lastname: Li
authors.affiliation: Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China and.
authors.forename: Kang
authors.initials: K
authors.lastname: Ning
authors.affiliation: Department of Statistics, University of California, Riverside, CA 92521, USA, Center for Plant Cell Biology, Institute for Integrative Genome Biology, University of California, Riverside, CA 92521, USA.
authors.forename: Xinping
authors.initials: X
authors.lastname: Cui
mesh_terms: D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D020641:Polymorphism, Single Nucleotide