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Best practices for evaluating single nucleotide variant calling methods for microbial genomics.

Olson ND, Lund SP, Colman RE, Foster JT, Sahl JW, Schupp JM, Keim P, Morrow JB, Salit ML, Zook JM - Front Genet (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.

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.

No MeSH data available.


Related in: MedlinePlus

SNP calling workflow diagram. Horizontal boxes represent steps in the workflow and arrows to the left indicate steps in the workflow challenged with reference genomic DNA, and sequence data.
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Figure 1: SNP calling workflow diagram. Horizontal boxes represent steps in the workflow and arrows to the left indicate steps in the workflow challenged with reference genomic DNA, and sequence data.

Mentions: In order to draw meaningful conclusions from evaluation of variant calling methods, the process used to identify the variants must first be understood. This measurement process includes sample processing, sequencing, mapping or de novo assembly, followed by variant calling (Altmann et al., 2012, Figure 1).


Best practices for evaluating single nucleotide variant calling methods for microbial genomics.

Olson ND, Lund SP, Colman RE, Foster JT, Sahl JW, Schupp JM, Keim P, Morrow JB, Salit ML, Zook JM - Front Genet (2015)

SNP calling workflow diagram. Horizontal boxes represent steps in the workflow and arrows to the left indicate steps in the workflow challenged with reference genomic DNA, and sequence data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493402&req=5

Figure 1: SNP calling workflow diagram. Horizontal boxes represent steps in the workflow and arrows to the left indicate steps in the workflow challenged with reference genomic DNA, and sequence data.
Mentions: In order to draw meaningful conclusions from evaluation of variant calling methods, the process used to identify the variants must first be understood. This measurement process includes sample processing, sequencing, mapping or de novo assembly, followed by variant calling (Altmann et al., 2012, Figure 1).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Biosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.

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.

No MeSH data available.


Related in: MedlinePlus