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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

Cause-effect diagram indicating the sources of error associated with different steps in the variant calling measurement process. Note that the SNP calling is performed using one of two methods, either read mapping or de novo assembly.
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Figure 2: Cause-effect diagram indicating the sources of error associated with different steps in the variant calling measurement process. Note that the SNP calling is performed using one of two methods, either read mapping or de novo assembly.

Mentions: Understanding the types and sources of error associated with a SNP and indel calling procedure will not only facilitate evaluation of results but also enable the user to optimize method performance. Several types of errors can impact the accuracy of SNP and indel variant identification. These errors occur during sample processing, the chemical and electronic processes that occur during sequencing, as well as the bioinformatic processing of sequence data: base calling, read mapping or de novo assembly, and identification of SNP and indel variants (Nielsen et al., 2011). The sources of error associated with different steps in the measurement process are depicted in Figure 2.


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)

Cause-effect diagram indicating the sources of error associated with different steps in the variant calling measurement process. Note that the SNP calling is performed using one of two methods, either read mapping or de novo assembly.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Cause-effect diagram indicating the sources of error associated with different steps in the variant calling measurement process. Note that the SNP calling is performed using one of two methods, either read mapping or de novo assembly.
Mentions: Understanding the types and sources of error associated with a SNP and indel calling procedure will not only facilitate evaluation of results but also enable the user to optimize method performance. Several types of errors can impact the accuracy of SNP and indel variant identification. These errors occur during sample processing, the chemical and electronic processes that occur during sequencing, as well as the bioinformatic processing of sequence data: base calling, read mapping or de novo assembly, and identification of SNP and indel variants (Nielsen et al., 2011). The sources of error associated with different steps in the measurement process are depicted in Figure 2.

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