Evidence map›Paper›PMID 25653667›Full record

ArticleFrontiers in genetics2014

Detecting rare structural variation in evolving microbial populations from new sequence junctions using breseq.

Daniel E Deatherage, Charles C Traverse, Lindsey N Wolf, Jeffrey E Barrick

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
5.7field-weighted citation impact, top 4% of its field
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

51 citing papers in PubMed, 107 citations in OpenAlex.

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  15. Adaptive Laboratory Evolution ofAntibiotics (Basel, Switzerland) · 2023
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  19. Host translesion polymerases are required for viral genome integrity.Proceedings of the National Academy of Sciences of the United States of America · 2022
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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

4 authors at 1 institution in 1 country.

Daniel E DeatherageDepartment of Molecular Biosciences, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, Institute for Cellular and Molecular Biology, The University of Texas at Austin Austin, TX, USA.
Charles C TraverseDepartment of Molecular Biosciences, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, Institute for Cellular and Molecular Biology, The University of Texas at Austin Austin, TX, USA.
Lindsey N WolfDepartment of Molecular Biosciences, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, Institute for Cellular and Molecular Biology, The University of Texas at Austin Austin, TX, USA.
Jeffrey E BarrickDepartment of Molecular Biosciences, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, Institute for Cellular and Molecular Biology, The University of Texas at Austin Austin, TX, USA.
The University of Texas at Austin · US

Funding

Identifying Mutations that Promote Microbial EvolvabilityR00GM087550 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI BARRICK, JEFFREY EVAN · 2011 to 2013
$724k
NIGMS NIH HHS R00 GM087550
6 · The paper itself

Abstract

New mutations leading to structural variation (SV) in genomes-in the form of mobile element insertions, large deletions, gene duplications, and other chromosomal rearrangements-can play a key role in microbial evolution. Yet, SV is considerably more difficult to predict from short-read genome resequencing data than single-nucleotide substitutions and indels (SN), so it is not yet routinely identified in studies that profile population-level genetic diversity over time in evolution experiments. We implemented an algorithm for detecting polymorphic SV as part of the breseq computational pipeline. This procedure examines split-read alignments, in which the two ends of a single sequencing read match disjoint locations in the reference genome, in order to detect structural variants and estimate their frequencies within a sample. We tested our algorithm using simulated Escherichia coli data and then applied it to 500- and 1000-generation population samples from the Lenski E. coli long-term evolution experiment (LTEE). Knowledge of genes that are targets of selection in the LTEE and mutations present in previously analyzed clonal isolates allowed us to evaluate the accuracy of our procedure. Overall, SV accounted for ~25% of the genetic diversity found in these samples. By profiling rare SV, we were able to identify many cases where alternative mutations in key genes transiently competed within a single population. We also found, unexpectedly, that mutations in two genes that rose to prominence at these early time points always went extinct in the long term. Because it is not limited by the base-calling error rate of the sequencing technology, our approach for identifying rare SV in whole-population samples may have a lower detection limit than similar predictions of SNs in these data sets. We anticipate that this functionality of breseq will be useful for providing a more complete picture of genome dynamics during evolution experiments with haploid microorganisms.

Indexed as

evolutionary dead endexperimental evolutiongenetic parallelismgenome resequencinginsertion sequence

Identifiers

PMID25653667
PMCPMC4301190
OpenAlexW2013647724

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.