Title |
OrthoClust: an orthology-based network framework for clustering data across multiple species
|
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Published in |
Genome Biology, August 2014
|
DOI | 10.1186/gb-2014-15-8-r100 |
Pubmed ID | |
Authors |
Koon-Kiu Yan, Daifeng Wang, Joel Rozowsky, Henry Zheng, Chao Cheng, Mark Gerstein |
Abstract |
Increasingly, high-dimensional genomics data are becoming available for many organisms.Here, we develop OrthoClust for simultaneously clustering data across multiple species. OrthoClust is a computational framework that integrates the co-association networks of individual species by utilizing the orthology relationships of genes between species. It outputs optimized modules that are fundamentally cross-species, which can either be conserved or species-specific. We demonstrate the application of OrthoClust using the RNA-Seq expression profiles of Caenorhabditis elegans and Drosophila melanogaster from the modENCODE consortium. A potential application of cross-species modules is to infer putative analogous functions of uncharacterized elements like non-coding RNAs based on guilt-by-association. |
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Geographical breakdown
Country | Count | As % |
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United States | 7 | 29% |
United Kingdom | 3 | 13% |
Spain | 1 | 4% |
Cameroon | 1 | 4% |
Germany | 1 | 4% |
Australia | 1 | 4% |
France | 1 | 4% |
Unknown | 9 | 38% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 12 | 50% |
Members of the public | 11 | 46% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 5% |
Germany | 2 | 1% |
Japan | 2 | 1% |
Brazil | 2 | 1% |
Norway | 1 | <1% |
Korea, Republic of | 1 | <1% |
Turkey | 1 | <1% |
Netherlands | 1 | <1% |
Australia | 1 | <1% |
Other | 6 | 4% |
Unknown | 128 | 84% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 44 | 29% |
Student > Ph. D. Student | 38 | 25% |
Student > Master | 14 | 9% |
Professor > Associate Professor | 11 | 7% |
Student > Bachelor | 8 | 5% |
Other | 24 | 16% |
Unknown | 13 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 84 | 55% |
Biochemistry, Genetics and Molecular Biology | 24 | 16% |
Computer Science | 12 | 8% |
Mathematics | 3 | 2% |
Engineering | 3 | 2% |
Other | 9 | 6% |
Unknown | 17 | 11% |