6533b82bfe1ef96bd128d6f6

RESEARCH PRODUCT

REGGAE : a novel approach for the identification of key transcriptional regulators

Lara SchneiderNico GerstnerNicole LudwigJörn WalterTim KehlNorbert GrafStefan TenzerDaniel StöckelKathrin KattlerEckart MeeseMarkus SchickUlrich KellerUlrich KellerJenny WegertManfred GesslerAndreas KellerUte DistlerHans-peter Lenhof

subject

0301 basic medicineStatistics and ProbabilityTranscription Genetic610Computational biologyBiologyBiochemistry03 medical and health sciencesNeoplasmsHumansTwo sampleMolecular BiologyGeneProbabilitySupplementary dataRegulation of gene expressionSystems Biology500Original PapersComputer Science Applications004Computational Mathematics030104 developmental biologyComputational Theory and MathematicsGene Expression RegulationKey (cryptography)Identification (biology)FemaleSoftware

description

Abstract Motivation Transcriptional regulators play a major role in most biological processes. Alterations in their activities are associated with a variety of diseases and in particular with tumor development and progression. Hence, it is important to assess the effects of deregulated regulators on pathological processes. Results Here, we present REGulator-Gene Association Enrichment (REGGAE), a novel method for the identification of key transcriptional regulators that have a significant effect on the expression of a given set of genes, e.g. genes that are differentially expressed between two sample groups. REGGAE uses a Kolmogorov–Smirnov-like test statistic that implicitly combines associations between regulators and their target genes with an enrichment approach to prioritize the influence of transcriptional regulators. We evaluated our method in two different application scenarios, which demonstrate that REGGAE is well suited for uncovering the influence of transcriptional regulators and is a valuable tool for the elucidation of complex regulatory mechanisms. Availability and implementation REGGAE is freely available at https://regulatortrail.bioinf.uni-sb.de. Supplementary information Supplementary data are available at Bioinformatics online.

https://dx.doi.org/10.22028/d291-29530