Title |
A survey of software for genome-wide discovery of differential splicing in RNA-Seq data
|
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Published in |
Human Genomics, January 2014
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DOI | 10.1186/1479-7364-8-3 |
Pubmed ID | |
Authors |
Joan E Hooper |
Abstract |
Alternative splicing is a major contributor to cellular diversity. Therefore the identification and quantification of differentially spliced transcripts in genome-wide transcript analysis is an important consideration. Here, I review the software available for analysis of RNA-Seq data for differential splicing and discuss intrinsic challenges for differential splicing analyses. Three approaches to differential splicing analysis are described, along with their associated software implementations, their strengths, limitations, and caveats. Suggestions for future work include more extensive experimental validation to assess accuracy of the software predictions and consensus formats for outputs that would facilitate visualizations, data exchange, and downstream analyses. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 15 | 31% |
United Kingdom | 7 | 15% |
France | 3 | 6% |
Spain | 3 | 6% |
Australia | 2 | 4% |
Montenegro | 1 | 2% |
Israel | 1 | 2% |
Kenya | 1 | 2% |
Germany | 1 | 2% |
Other | 3 | 6% |
Unknown | 11 | 23% |
Demographic breakdown
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---|---|---|
Scientists | 27 | 56% |
Members of the public | 20 | 42% |
Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 3% |
United Kingdom | 5 | 2% |
Germany | 2 | <1% |
France | 1 | <1% |
Italy | 1 | <1% |
Switzerland | 1 | <1% |
Netherlands | 1 | <1% |
Czechia | 1 | <1% |
Canada | 1 | <1% |
Other | 5 | 2% |
Unknown | 245 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 82 | 30% |
Researcher | 76 | 28% |
Student > Master | 25 | 9% |
Professor > Associate Professor | 13 | 5% |
Student > Doctoral Student | 12 | 4% |
Other | 44 | 16% |
Unknown | 18 | 7% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 57 | 21% |
Medicine and Dentistry | 12 | 4% |
Computer Science | 10 | 4% |
Mathematics | 6 | 2% |
Other | 24 | 9% |
Unknown | 26 | 10% |