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CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments

Overview of attention for article published in BMC Bioinformatics, September 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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Citations

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57 Mendeley
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1 CiteULike
Title
CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0701-2
Pubmed ID
Authors

Ashley J. Waardenberg, Samuel D. Bassett, Romaric Bouveret, Richard P. Harvey

Abstract

Gene ontology (GO) enrichment is commonly used for inferring biological meaning from systems biology experiments. However, determining differential GO and pathway enrichment between DNA-binding experiments or using the GO structure to classify experiments has received little attention. Herein, we present a bioinformatics tool, CompGO, for identifying Differentially Enriched Gene Ontologies, called DiEGOs, and pathways, through the use of a z-score derivation of log odds ratios, and visualizing these differences at GO and pathway level. Through public experimental data focused on the cardiac transcription factor NKX2-5, we illustrate the problems associated with comparing GO enrichments between experiments using a simple overlap approach. We have developed an R/Bioconductor package, CompGO, which implements a new statistic normally used in epidemiological studies for performing comparative GO analyses and visualizing comparisons from . BED data containing genomic coordinates as well as gene lists as inputs. We justify the statistic through inclusion of experimental data and compare to the commonly used overlap method. CompGO is freely available as a R/Bioconductor package enabling easy integration into existing pipelines and is available at: http://www.bioconductor.org/packages/release/bioc/html/CompGO.html packages/release/bioc/html/CompGO.html.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Australia 1 2%
Unknown 54 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 35%
Student > Ph. D. Student 14 25%
Student > Bachelor 7 12%
Student > Master 3 5%
Professor > Associate Professor 2 4%
Other 2 4%
Unknown 9 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 32%
Computer Science 12 21%
Biochemistry, Genetics and Molecular Biology 9 16%
Medicine and Dentistry 4 7%
Social Sciences 2 4%
Other 2 4%
Unknown 10 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 January 2016.
All research outputs
#5,559,144
of 25,766,791 outputs
Outputs from BMC Bioinformatics
#1,958
of 7,743 outputs
Outputs of similar age
#64,054
of 277,904 outputs
Outputs of similar age from BMC Bioinformatics
#37
of 124 outputs
Altmetric has tracked 25,766,791 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,743 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,904 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 124 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.