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Tissue enrichment analysis for C. elegans genomics

Overview of attention for article published in BMC Bioinformatics, September 2016
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  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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7 tweeters

Citations

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80 Dimensions

Readers on

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74 Mendeley
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Title
Tissue enrichment analysis for C. elegans genomics
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1229-9
Pubmed ID
Authors

David Angeles-Albores, Raymond Y. N. Lee, Juancarlos Chan, Paul W. Sternberg

Abstract

Over the last ten years, there has been explosive development in methods for measuring gene expression. These methods can identify thousands of genes altered between conditions, but understanding these datasets and forming hypotheses based on them remains challenging. One way to analyze these datasets is to associate ontologies (hierarchical, descriptive vocabularies with controlled relations between terms) with genes and to look for enrichment of specific terms. Although Gene Ontology (GO) is available for Caenorhabditis elegans, it does not include anatomical information. We have developed a tool for identifying enrichment of C. elegans tissues among gene sets and generated a website GUI where users can access this tool. Since a common drawback to ontology enrichment analyses is its verbosity, we developed a very simple filtering algorithm to reduce the ontology size by an order of magnitude. We adjusted these filters and validated our tool using a set of 30 gold standards from Expression Cluster data in WormBase. We show our tool can even discriminate between embryonic and larval tissues and can even identify tissues down to the single-cell level. We used our tool to identify multiple neuronal tissues that are down-regulated due to pathogen infection in C. elegans. Our Tissue Enrichment Analysis (TEA) can be found within WormBase, and can be downloaded using Python's standard pip installer. It tests a slimmed-down C. elegans tissue ontology for enrichment of specific terms and provides users with a text and graphic representation of the results.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 74 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 35%
Researcher 13 18%
Student > Master 10 14%
Student > Bachelor 6 8%
Student > Doctoral Student 3 4%
Other 7 9%
Unknown 9 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 31 42%
Agricultural and Biological Sciences 19 26%
Computer Science 3 4%
Neuroscience 2 3%
Physics and Astronomy 2 3%
Other 6 8%
Unknown 11 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 April 2020.
All research outputs
#9,590,642
of 17,397,008 outputs
Outputs from BMC Bioinformatics
#3,275
of 6,156 outputs
Outputs of similar age
#118,381
of 272,083 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 28 outputs
Altmetric has tracked 17,397,008 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,156 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
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 272,083 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.