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Algal Functional Annotation Tool: a web-based analysis suite to functionally interpret large gene lists using integrated annotation and expression data

Overview of attention for article published in BMC Bioinformatics, July 2011
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Title
Algal Functional Annotation Tool: a web-based analysis suite to functionally interpret large gene lists using integrated annotation and expression data
Published in
BMC Bioinformatics, July 2011
DOI 10.1186/1471-2105-12-282
Pubmed ID
Authors

David Lopez, David Casero, Shawn J Cokus, Sabeeha S Merchant, Matteo Pellegrini

Abstract

Progress in genome sequencing is proceeding at an exponential pace, and several new algal genomes are becoming available every year. One of the challenges facing the community is the association of protein sequences encoded in the genomes with biological function. While most genome assembly projects generate annotations for predicted protein sequences, they are usually limited and integrate functional terms from a limited number of databases. Another challenge is the use of annotations to interpret large lists of 'interesting' genes generated by genome-scale datasets. Previously, these gene lists had to be analyzed across several independent biological databases, often on a gene-by-gene basis. In contrast, several annotation databases, such as DAVID, integrate data from multiple functional databases and reveal underlying biological themes of large gene lists. While several such databases have been constructed for animals, none is currently available for the study of algae. Due to renewed interest in algae as potential sources of biofuels and the emergence of multiple algal genome sequences, a significant need has arisen for such a database to process the growing compendiums of algal genomic data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
United Kingdom 2 1%
Ireland 1 <1%
Brazil 1 <1%
Denmark 1 <1%
Canada 1 <1%
Unknown 155 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 30%
Researcher 43 26%
Student > Master 16 10%
Student > Doctoral Student 11 7%
Professor > Associate Professor 8 5%
Other 25 15%
Unknown 12 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 96 58%
Biochemistry, Genetics and Molecular Biology 27 16%
Engineering 5 3%
Computer Science 5 3%
Environmental Science 3 2%
Other 11 7%
Unknown 18 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 09 May 2013.
All research outputs
#18,338,946
of 22,710,079 outputs
Outputs from BMC Bioinformatics
#6,292
of 7,259 outputs
Outputs of similar age
#97,574
of 116,782 outputs
Outputs of similar age from BMC Bioinformatics
#81
of 91 outputs
Altmetric has tracked 22,710,079 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,259 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 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 91 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.