Title |
DE-kupl: exhaustive capture of biological variation in RNA-seq data through k-mer decomposition
|
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Published in |
Genome Biology, December 2017
|
DOI | 10.1186/s13059-017-1372-2 |
Pubmed ID | |
Authors |
Jérôme Audoux, Nicolas Philippe, Rayan Chikhi, Mikaël Salson, Mélina Gallopin, Marc Gabriel, Jérémy Le Coz, Emilie Drouineau, Thérèse Commes, Daniel Gautheret |
Abstract |
We introduce a k-mer-based computational protocol, DE-kupl, for capturing local RNA variation in a set of RNA-seq libraries, independently of a reference genome or transcriptome. DE-kupl extracts all k-mers with differential abundance directly from the raw data files. This enables the retrieval of virtually all variation present in an RNA-seq data set. This variation is subsequently assigned to biological events or entities such as differential long non-coding RNAs, splice and polyadenylation variants, introns, repeats, editing or mutation events, and exogenous RNA. Applying DE-kupl to human RNA-seq data sets identified multiple types of novel events, reproducibly across independent RNA-seq experiments. |
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Geographical breakdown
Country | Count | As % |
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United States | 34 | 25% |
France | 16 | 12% |
United Kingdom | 12 | 9% |
Germany | 5 | 4% |
Australia | 4 | 3% |
Canada | 2 | 1% |
Spain | 2 | 1% |
Finland | 2 | 1% |
India | 2 | 1% |
Other | 12 | 9% |
Unknown | 46 | 34% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 82 | 60% |
Members of the public | 53 | 39% |
Science communicators (journalists, bloggers, editors) | 2 | 1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 1% |
Unknown | 138 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 44 | 31% |
Student > Ph. D. Student | 27 | 19% |
Student > Master | 12 | 9% |
Student > Bachelor | 9 | 6% |
Other | 6 | 4% |
Other | 18 | 13% |
Unknown | 24 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 41 | 29% |
Biochemistry, Genetics and Molecular Biology | 37 | 26% |
Computer Science | 16 | 11% |
Engineering | 7 | 5% |
Veterinary Science and Veterinary Medicine | 2 | 1% |
Other | 9 | 6% |
Unknown | 28 | 20% |