Title |
Algal Functional Annotation Tool: a web-based analysis suite to functionally interpret large gene lists using integrated annotation and expression data
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Published in |
BMC Bioinformatics, July 2011
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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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
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% |