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Mamm Genome. 2015 Oct;26(9-10):556-66. doi: 10.1007/s00335-015-9575-x. Epub 2015 Jun 20.

GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data.

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The Jackson Laboratory, Bar Harbor, ME, 04609, USA.
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.
Computer Science Department, Baylor University, Waco, TX, 76798, USA.
The Jackson Laboratory, Bar Harbor, ME, 04609, USA.


A persistent challenge lies in the interpretation of consensus and discord from functional genomics experimentation. Harmonizing and analyzing this data will enable investigators to discover relations of many genes to many diseases, and from many phenotypes and experimental paradigms to many diseases through their genomic substrates. The system provides a platform for cross-species integration and interrogation of heterogeneous curated and experimentally derived functional genomics data. GeneWeaver enables researchers to store, share, analyze, and compare results of their own genome-wide functional genomics experiments in an environment containing rich companion data obtained from major curated repositories, including the Mouse Genome Database and other model organism databases, along with derived data from highly specialized resources, publications, and user submissions. The data, largely consisting of gene sets and putative biological networks, are mapped onto one another through gene identifiers and homology across species. A versatile suite of interactive tools enables investigators to perform a variety of set analysis operations to find consilience among these often noisy experimental results. Fast algorithms enable real-time analysis of large queries. Specific applications include prioritizing candidate genes for quantitative trait loci, identifying biologically valid mouse models and phenotypic assays for human disease, finding the common biological substrates of related diseases, classifying experiments and the biological concepts they represent from empirical data, and applying patterns of genomic evidence to implicate novel genes in disease. These results illustrate an alternative to strict emphasis on replicability, whereby researchers classify experimental results to identify the conditions that lead to their similarity.

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