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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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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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 104 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 32%
Researcher 18 17%
Student > Master 12 12%
Student > Bachelor 11 11%
Student > Doctoral Student 5 5%
Other 8 8%
Unknown 17 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 45 43%
Agricultural and Biological Sciences 22 21%
Neuroscience 4 4%
Computer Science 3 3%
Engineering 3 3%
Other 7 7%
Unknown 20 19%
Attention Score in Context

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
#13,243,556
of 22,888,307 outputs
Outputs from BMC Bioinformatics
#4,011
of 7,298 outputs
Outputs of similar age
#165,186
of 322,146 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 121 outputs
Altmetric has tracked 22,888,307 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,298 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 42nd percentile – i.e., 42% 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 322,146 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 121 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 53% of its contemporaries.