title: GOMA: functional enrichment analysis tool based on GO modules.
abstract: Analyzing the function of gene sets is a critical step in interpreting the results of high-throughput experiments in systems biology. A variety of enrichment analysis tools have been developed in recent years, but most output a long list of significantly enriched terms that are often redundant, making it difficult to extract the most meaningful functions. In this paper, we present GOMA, a novel enrichment analysis method based on the new concept of enriched functional Gene Ontology (GO) modules. With this method, we systematically revealed functional GO modules, i.e., groups of functionally similar GO terms, via an optimization model and then ranked them by enrichment scores. Our new method simplifies enrichment analysis results by reducing redundancy, thereby preventing inconsistent enrichment results among functionally similar terms and providing more biologically meaningful results.
authors.affiliation: Chinese Academy of Sciences, Beijing, People's Republic of China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Huang
authors.forename: Ling-Yun
authors.initials: LY
authors.lastname: Wu
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiang-Sun
authors.initials: XS
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods