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Methods Mol Biol. 2017;1488:131-152.

Integrative Functional Genomics for Systems Genetics in GeneWeaver.org.

Author information

1
The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA.
2
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, 37996, USA.
3
Institute for Biomedical Studies, Baylor University, Waco, TN, 76798, USA.
4
The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, 04609, USA. Elissa.chesler@jax.org.

Abstract

The abundance of existing functional genomics studies permits an integrative approach to interpreting and resolving the results of diverse systems genetics studies. However, a major challenge lies in assembling and harmonizing heterogeneous data sets across species for facile comparison to the positional candidate genes and coexpression networks that come from systems genetic studies. GeneWeaver is an online database and suite of tools at www.geneweaver.org that allows for fast aggregation and analysis of gene set-centric data. GeneWeaver contains curated experimental data together with resource-level data such as GO annotations, MP annotations, and KEGG pathways, along with persistent stores of user entered data sets. These can be entered directly into GeneWeaver or transferred from widely used resources such as GeneNetwork.org. Data are analyzed using statistical tools and advanced graph algorithms to discover new relations, prioritize candidate genes, and generate function hypotheses. Here we use GeneWeaver to find genes common to multiple gene sets, prioritize candidate genes from a quantitative trait locus, and characterize a set of differentially expressed genes. Coupling a large multispecies repository curated and empirical functional genomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous data for interpreting and extrapolating systems genetics results.

KEYWORDS:

Data mining; GeneWeaver data base; IT-tools for systems genetics; QTL candidate gene

PMID:
27933523
DOI:
10.1007/978-1-4939-6427-7_6
[Indexed for MEDLINE]

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