Title |
De novo assembly and analysis of RNA-seq data
|
---|---|
Published in |
Nature Methods, October 2010
|
DOI | 10.1038/nmeth.1517 |
Pubmed ID | |
Authors |
Gordon Robertson, Jacqueline Schein, Readman Chiu, Richard Corbett, Matthew Field, Shaun D Jackman, Karen Mungall, Sam Lee, Hisanaga Mark Okada, Jenny Q Qian, Malachi Griffith, Anthony Raymond, Nina Thiessen, Timothee Cezard, Yaron S Butterfield, Richard Newsome, Simon K Chan, Rong She, Richard Varhol, Baljit Kamoh, Anna-Liisa Prabhu, Angela Tam, YongJun Zhao, Richard A Moore, Martin Hirst, Marco A Marra, Steven J M Jones, Pamela A Hoodless, Inanc Birol |
Abstract |
We describe Trans-ABySS, a de novo short-read transcriptome assembly and analysis pipeline that addresses variation in local read densities by assembling read substrings with varying stringencies and then merging the resulting contigs before analysis. Analyzing 7.4 gigabases of 50-base-pair paired-end Illumina reads from an adult mouse liver poly(A) RNA library, we identified known, new and alternative structures in expressed transcripts, and achieved high sensitivity and specificity relative to reference-based assembly methods. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | 17% |
Canada | 1 | 17% |
Unknown | 4 | 67% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 50% |
Members of the public | 3 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 42 | 3% |
United Kingdom | 19 | 1% |
Germany | 13 | <1% |
France | 9 | <1% |
Canada | 9 | <1% |
Sweden | 9 | <1% |
Brazil | 9 | <1% |
Italy | 7 | <1% |
Spain | 7 | <1% |
Other | 52 | 3% |
Unknown | 1348 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 379 | 25% |
Researcher | 337 | 22% |
Student > Master | 208 | 14% |
Student > Bachelor | 106 | 7% |
Student > Doctoral Student | 91 | 6% |
Other | 239 | 16% |
Unknown | 164 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 839 | 55% |
Biochemistry, Genetics and Molecular Biology | 267 | 18% |
Computer Science | 87 | 6% |
Medicine and Dentistry | 32 | 2% |
Engineering | 18 | 1% |
Other | 93 | 6% |
Unknown | 188 | 12% |