Evidence map›Paper›PMID 25206364›Full record

ArticleFrontiers in genetics2014

A bioinformatics workflow for detecting signatures of selection in genomic data.

Murray Cadzow, James Boocock, Hoang T Nguyen, Phillip Wilcox, Tony R Merriman, Michael A Black

Abstract 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 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

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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

6 authors.

Murray CadzowDepartment of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand.
James BoocockDepartment of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand.
Hoang T NguyenDepartment of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand ; Department of Mathematics and Statistics, University of Otago Dunedin, New Zealand.
Phillip WilcoxDepartment of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand ; New Zealand Forest Research Institute Ltd Rotorua, New Zealand.
Tony R MerrimanDepartment of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand.
Michael A BlackDepartment of Biochemistry, University of Otago Dunedin, New Zealand ; Virtual Institute of Statistical Genetics Rotorua, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The detection of "signatures of selection" is now possible on a genome-wide scale in many plant and animal species, and can be performed in a population-specific manner due to the wealth of per-population genome-wide genotype data that is available. With genomic regions that exhibit evidence of having been under selection shown to also be enriched for genes associated with biologically important traits, detection of evidence of selective pressure is emerging as an additional approach for identifying novel gene-trait associations. While high-density genotype data is now relatively easy to obtain, for many researchers it is not immediately obvious how to go about identifying signatures of selection in these data sets. Here we describe a basic workflow, constructed from open source tools, for detecting and examining evidence of selection in genomic data. Code to install and implement the pipeline components, and instructions to run a basic analysis using the workflow described here, can be downloaded from our public GitHub repository: http://www.github.com/smilefreak/selectionTools/

Indexed as

analysis pipelinegenome-widegenomicssignatures of selection

Identifiers

PMID25206364
PMCPMC4144660

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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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.