Scientific ReportsBioinformaticsPeer-reviewed journal article
AnnoMiner: a new web-tool to integrate epigenetics, transcription factor occupancy, and transcriptomics data to predict transcriptional regulators
Arno MeileriD, Fabio MarchianòiD, Margaux Haering, Manuela Weitkunat, Frank SchnorreriD, Bianca Hermine HabermanniD
Abstract
Gene expression regulation requires precise transcriptional programs, led by transcription factors in combination with epigenetic events. Recent advances in epigenomic and transcriptomic techniques provided insight into different gene regulation mechanisms. However, to date it remains challenging to understand how combinations of transcription factors together with epigenetic events control cell-type specific gene expression. We developed the AnnoMiner web-server and introduce an innovative and flexible way to annotate and integrate epigenetic, and transcription factor occupancy data. First, AnnoMiner annotates user-provided peaks with gene features. Second, AnnoMiner can integrate genome binding data from two different transcriptional regulators together with gene features. Third, AnnoMiner offers to explore the transcriptional deregulation of 10 genes nearby a user-provided peak. AnnoMiners fourth function performs transcription factor or histone mark enrichment analysis for user-provided gene lists by utilizing hundreds of public, high-quality datasets from ENCODE for the model organisms human, mouse, Drosophila and C. elegans. Thus, AnnoMiner can predict transcriptional regulators for a studied process without the strict need for chromatin data from the same process. We compared AnnoMiner to existing tools and experimentally validated several transcriptional regulators predicted by AnnoMiner to indeed contribute to muscle morphogenesis in Drosophila. AnnoMiner is freely available at http://chimborazo.ibdm.univ-mrs.fr/AnnoMiner/.
- organism: human
- organism: mouse
- organism: Drosophila
- assay: ChIP-seq
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 8
- FWCI
- 0.39
- Field percentile
- top 49%
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