Evidence map›Paper›PMID 21726447›Full record

ArticleBMC bioinformatics2011

Detection of lineage-specific evolutionary changes among primate species.

Mihaela Pertea, Geo M Pertea, Steven L Salzberg

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.8field-weighted citation impact, top 28% 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

12 citing papers in PubMed, 20 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Mihaela PerteaCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA. mpertea@umiacs.umd.edu
Geo M Pertea
Steven L Salzberg
University of Maryland, College Park · USJohns Hopkins University · US

Funding

Computational Methods for Genome Assembly, Transcript Assembly, and Variant DiscoveryR01HG006677 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2011 to 2025
$10.7M
COMPUTATIONAL MODELING OF GENES AND GENE STRUCTURER01LM006845 · NLM · UNIV OF MARYLAND, COLLEGE PARK · PI SALZBERG, STEVEN L. · 1999 to 2010
$5.9M
Alignment Software for Second-Generation SequencingR01HG006102 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2011 to 2014
$2.2M
Bioinformatics Software for Analyzing Microbial GenomesR01GM083873 · NIGMS · UNIV OF MARYLAND, COLLEGE PARK · PI SALZBERG, STEVEN L. · 2008 to 2017
$2.1M
NHGRI NIH HHS R01 HG006102NHGRI NIH HHS R01 HG006677NIGMS NIH HHS R01 GM083873NLM NIH HHS R01 LM006845NLM NIH HHS R01-LM006845
6 · The paper itself

Abstract

backgroundComparison of the human genome with other primates offers the opportunity to detect evolutionary events that created the diverse phenotypes among the primate species. Because the primate genomes are highly similar to one another, methods developed for analysis of more divergent species do not always detect signs of evolutionary selection.

resultsWe have developed a new method, called DivE, specifically designed to find regions that have evolved either more or less rapidly than expected, for any clade within a set of very closely related species. Unlike some previous methods, DivE does not rely on rates of synonymous and nonsynonymous substitution, which enables it to detect evolutionary events in noncoding regions. We demonstrate using simulated data that DivE compares favorably to alternative methods, and we then apply DivE to the ENCODE regions in 14 primate species. We identify thousands of regions in these primates, ranging from 50 to >10000 bp in length, that appear to have experienced either constrained or accelerated rates of evolution. In particular, we detected 4942 regions that have potentially undergone positive selection in one or more primate species. Most of these regions occur outside of protein-coding genes, although we identified 20 proteins that have experienced positive selection.

conclusionsDivE provides an easy-to-use method to predict both positive and negative selection in noncoding DNA, that is particularly well-suited to detecting lineage-specific selection in large genomes.

Indexed as

PhylogenySoftwareAnimalsBiological EvolutionGenomeGenome, HumanHumansPrimates

Identifiers

PMID21726447
PMCPMC3143108
OpenAlexW2156868954

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.