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Open Access Software

EnRICH: Extraction and Ranking using Integration and Criteria Heuristics

Xia Zhang13, M Heather West Greenlee234* and Jeanne M Serb13

Author Affiliations

1 Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, Iowa, USA

2 Department of Biomedical Sciences, 2008 Veterinary Medicine, Iowa State University, Ames, IA 50010, USA

3 Interdepartmental Genetics Program, Iowa State University, Ames, Iowa, USA

4 Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA

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BMC Systems Biology 2013, 7:4  doi:10.1186/1752-0509-7-4

Published: 15 January 2013

Abstract

Background

High throughput screening technologies enable biologists to generate candidate genes at a rate that, due to time and cost constraints, cannot be studied by experimental approaches in the laboratory. Thus, it has become increasingly important to prioritize candidate genes for experiments. To accomplish this, researchers need to apply selection requirements based on their knowledge, which necessitates qualitative integration of heterogeneous data sources and filtration using multiple criteria. A similar approach can also be applied to putative candidate gene relationships. While automation can assist in this routine and imperative procedure, flexibility of data sources and criteria must not be sacrificed. A tool that can optimize the trade-off between automation and flexibility to simultaneously filter and qualitatively integrate data is needed to prioritize candidate genes and generate composite networks from heterogeneous data sources.

Results

We developed the java application, EnRICH (

    E
xtractio
    n
and
    R
anking using
    I
ntegration and
    C
riteria
    H
euristics), in order to alleviate this need. Here we present a case study in which we used EnRICH to integrate and filter multiple candidate gene lists in order to identify potential retinal disease genes. As a result of this procedure, a candidate pool of several hundred genes was narrowed down to five candidate genes, of which four are confirmed retinal disease genes and one is associated with a retinal disease state.

Conclusions

We developed a platform-independent tool that is able to qualitatively integrate multiple heterogeneous datasets and use different selection criteria to filter each of them, provided the datasets are tables that have distinct identifiers (required) and attributes (optional). With the flexibility to specify data sources and filtering criteria, EnRICH automatically prioritizes candidate genes or gene relationships for biologists based on their specific requirements. Here, we also demonstrate that this tool can be effectively and easily used to apply highly specific user-defined criteria and can efficiently identify high quality candidate genes from relatively sparse datasets.

Keywords:
Qualitative integration; High-throughput data; Heterogeneous data; Network; Network visualization; Candidate prioritization