title: Best practices for evaluating single nucleotide variant calling methods for microbial genomics.
abstract: Innovations in sequencing technologies have allowed biologists to make incredible advances in understanding biological systems. As experience grows, researchers increasingly recognize that analyzing the wealth of data provided by these new sequencing platforms requires careful attention to detail for robust results. Thus far, much of the scientific Communit's focus for use in bacterial genomics has been on evaluating genome assembly algorithms and rigorously validating assembly program performance. Missing, however, is a focus on critical evaluation of variant callers for these genomes. Variant calling is essential for comparative genomics as it yields insights into nucleotide-level organismal differences. Variant calling is a multistep process with a host of potential error sources that may lead to incorrect variant calls. Identifying and resolving these incorrect calls is critical for bacterial genomics to advance. The goal of this review is to provide guidance on validating algorithms and pipelines used in variant calling for bacterial genomics. First, we will provide an overview of the variant calling procedures and the potential sources of error associated with the methods. We will then identify appropriate datasets for use in evaluating algorithms and describe statistical methods for evaluating algorithm performance. As variant calling moves from basic research to the applied setting, standardized methods for performance evaluation and reporting are required; it is our hope that this review provides the groundwork for the development of these standards.
authors.affiliation: Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.
authors.forename: Nathan D
authors.initials: ND
authors.lastname: Olson
authors.affiliation: Statistical Engineering Division, Information Technology Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.
authors.forename: Steven P
authors.initials: SP
authors.lastname: Lund
authors.affiliation: Division of Pathogen Genomics, Translational Genomics Research Institute , Flagstaff, AZ, USA.
authors.forename: Rebecca E
authors.initials: RE
authors.lastname: Colman
authors.affiliation: Center for Microbial Genetics and Genomics, Northern Arizona University , Flagstaff, AZ, USA.
authors.forename: Jeffrey T
authors.initials: JT
authors.lastname: Foster
authors.affiliation: Division of Pathogen Genomics, Translational Genomics Research Institute , Flagstaff, AZ, USA ; Center for Microbial Genetics and Genomics, Northern Arizona University , Flagstaff, AZ, USA.
authors.forename: Jason W
authors.initials: JW
authors.lastname: Sahl
authors.affiliation: Division of Pathogen Genomics, Translational Genomics Research Institute , Flagstaff, AZ, USA.
authors.forename: James M
authors.initials: JM
authors.lastname: Schupp
authors.affiliation: Division of Pathogen Genomics, Translational Genomics Research Institute , Flagstaff, AZ, USA ; Center for Microbial Genetics and Genomics, Northern Arizona University , Flagstaff, AZ, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Keim
authors.affiliation: Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.
authors.forename: Jayne B
authors.initials: JB
authors.lastname: Morrow
authors.affiliation: Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA ; Department of Bioengineering, Stanford University , Stanford, CA, USA.
authors.forename: Marc L
authors.initials: ML
authors.lastname: Salit
authors.affiliation: Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.
authors.forename: Justin M
authors.initials: JM
authors.lastname: Zook
keywords: indel; next-generation sequencing; performance metrics; single nucleotide variants; variant calling