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iPEAP: integrating multiple omics and genetic data for pathway enrichment analysis
Author(s) -
Haoqi Sun,
Haiping Wang,
Ruixin Zhu,
Kailin Tang,
Qin Gong,
Juan Cui,
Zhiwei Cao,
Qi Liu
Publication year - 2013
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btt576
Subject(s) - computer science , computational biology , ranking (information retrieval) , pathway analysis , systems biology , benchmark (surveying) , data mining , biology , information retrieval , genetics , gene expression , gene , geodesy , geography
A challenge in biodata analysis is to understand the underlying phenomena among many interactions in signaling pathways. Such study is formulated as the pathway enrichment analysis, which identifies relevant pathways functional enriched in high-throughput data. The question faced here is how to analyze different data types in a unified and integrative way by characterizing pathways that these data simultaneously reveal. To this end, we developed integrative Pathway Enrichment Analysis Platform, iPEAP, which handles transcriptomics, proteomics, metabolomics and GWAS data under a unified aggregation schema. iPEAP emphasizes on the ability to aggregate various pathway enrichment results generated in different high-throughput experiments, as well as the quantitative measurements of different ranking results, thus providing the first benchmark platform for integration, comparison and evaluation of multiple types of data and enrichment methods.

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