Evidence map›Paper›PMID 26217378›Full record

ReviewFrontiers in genetics2015

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

Nathan D Olson, Steven P Lund, Rebecca E Colman, Jeffrey T Foster, Jason W Sahl, James M Schupp, Paul Keim, Jayne B Morrow, Marc L Salit, Justin M Zook

Abstract readReview
In one paragraph

Review in Frontiers in genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
101citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

101 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  14. Genomic and Pangenomic Insights intoPathogens (Basel, Switzerland) · 2025
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41 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Nathan D OlsonBiosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.
Steven P LundStatistical Engineering Division, Information Technology Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.
Rebecca E ColmanDivision of Pathogen Genomics, Translational Genomics Research Institute , Flagstaff, AZ, USA.
Jeffrey T FosterCenter for Microbial Genetics and Genomics, Northern Arizona University , Flagstaff, AZ, USA.
Jason W SahlDivision of Pathogen Genomics, Translational Genomics Research Institute , Flagstaff, AZ, USA ; Center for Microbial Genetics and Genomics, Northern Arizona University , Flagstaff, AZ, USA.
James M SchuppDivision of Pathogen Genomics, Translational Genomics Research Institute , Flagstaff, AZ, USA.
Paul KeimDivision of Pathogen Genomics, Translational Genomics Research Institute , Flagstaff, AZ, USA ; Center for Microbial Genetics and Genomics, Northern Arizona University , Flagstaff, AZ, USA.
Jayne B MorrowBiosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.
Marc L SalitBiosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA ; Department of Bioengineering, Stanford University , Stanford, CA, USA.
Justin M ZookBiosystems and Biomaterials Division, Material Measurement Laboratory, National Institute of Standards and Technology , Gaithersburg, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

indelnext-generation sequencingperformance metricssingle nucleotide variantsvariant calling

Identifiers

PMID26217378
PMCPMC4493402

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.