Evidence map›Paper›PMID 28224054›Full record

ArticlePeerJ2017

Variant profiling of evolving prokaryotic populations.

Markus Zojer, Lisa N Schuster, Frederik Schulz, Alexander Pfundner, Matthias Horn, Thomas Rattei

Abstract read
In one paragraph

Article in PeerJ, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Understanding the Genetic Diversity ofFrontiers in genetics · 2022
    Article
  9. Metagenome SNP calling via read-colored de Bruijn graphs.Bioinformatics (Oxford, England) · 2021
    Article
  10. Molecular causes of an evolutionary shift along the parasitism-mutualism continuum in a bacterial symbiont.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  11. Microorganisms · 2020
    Review
  12. Review
  13. Article
  14. Article
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

6 authors.

Markus ZojerDepartment of Microbiology and Ecosystems Science, Division of Computational Systems Biology, University of Vienna , Vienna , Austria.
Lisa N SchusterDepartment of Microbiology and Ecosystems Science, Division of Microbial Ecology, University of Vienna , Vienna , Austria.
Frederik SchulzDOE Joint Genome Institute, Lawrence Berkeley National Lab , Walnut Creek , CA , United States.
Alexander PfundnerDepartment of Microbiology and Ecosystems Science, Division of Computational Systems Biology, University of Vienna , Vienna , Austria.
Matthias HornDepartment of Microbiology and Ecosystems Science, Division of Microbial Ecology, University of Vienna , Vienna , Austria.
Thomas RatteiDepartment of Microbiology and Ecosystems Science, Division of Computational Systems Biology, University of Vienna , Vienna , Austria.

Funding

European Research Council 281633
6 · The paper itself

Abstract

Genomic heterogeneity of bacterial species is observed and studied in experimental evolution experiments and clinical diagnostics, and occurs as micro-diversity of natural habitats. The challenge for genome research is to accurately capture this heterogeneity with the currently used short sequencing reads. Recent advances in NGS technologies improved the speed and coverage and thus allowed for deep sequencing of bacterial populations. This facilitates the quantitative assessment of genomic heterogeneity, including low frequency alleles or haplotypes. However, false positive variant predictions due to sequencing errors and mapping artifacts of short reads need to be prevented. We therefore created VarCap, a workflow for the reliable prediction of different types of variants even at low frequencies. In order to predict SNPs, InDels and structural variations, we evaluated the sensitivity and accuracy of different software tools using synthetic read data. The results suggested that the best sensitivity could be reached by a union of different tools, however at the price of increased false positives. We identified possible reasons for false predictions and used this knowledge to improve the accuracy by post-filtering the predicted variants according to properties such as frequency, coverage, genomic environment/localization and co-localization with other variants. We observed that best precision was achieved by using an intersection of at least two tools per variant. This resulted in the reliable prediction of variants above a minimum relative abundance of 2%. VarCap is designed for being routinely used within experimental evolution experiments or for clinical diagnostics. The detected variants are reported as frequencies within a VCF file and as a graphical overview of the distribution of the different variant/allele/haplotype frequencies. The source code of VarCap is available at https://github.com/ma2o/VarCap. In order to provide this workflow to a broad community, we implemeted VarCap on a Galaxy webserver, which is accessible at http://galaxy.csb.univie.ac.at.

Indexed as

ChlamydiaeExperimental evolutionGalaxyInDelsMicrobial populationsNext Generation SequencingSNPsStructural variationsVariant callingVariant frequencies

Identifiers

PMID28224054
PMCPMC5316281

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.