CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets

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CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets

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Title: CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets
Author: Li, Yang; Jourdain, Alexis A.; Calvo, Sarah E.; Liu, Jun S.; Mootha, Vamsi K.

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Citation: Li, Yang, Alexis A. Jourdain, Sarah E. Calvo, Jun S. Liu, and Vamsi K. Mootha. 2017. “CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets.” PLoS Computational Biology 13 (7): e1005653. doi:10.1371/journal.pcbi.1005653. http://dx.doi.org/10.1371/journal.pcbi.1005653.
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Abstract: In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active.
Published Version: doi:10.1371/journal.pcbi.1005653
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5546725/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:34375307
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