DeepTFactor: A deep learning-based tool for the prediction of transcription factors
Gi Bae Kim, Ye Gao, Bernhard Ø. Palsson, Sang Yup Lee
Abstract
K-12 MG1655. Among them, 84 candidate TFs belong to the y-ome, which is a collection of genes that lack experimental evidence of function. We experimentally validated the results of DeepTFactor prediction by further characterizing genome-wide binding sites of three predicted TFs, YqhC, YiaU, and YahB. Furthermore, we made available the list of 4,674,808 TFs predicted from 73,873,012 protein sequences in 48,346 genomes. DeepTFactor will serve as a useful tool for predicting TFs, which is necessary for understanding the regulatory systems of organisms of interest. We provide DeepTFactor as a stand-alone program, available at https://bitbucket.org/kaistsystemsbiology/deeptfactor.
Topics & Concepts
Computational biologyTranscription factorIdentification (biology)Computer scienceBiologyGenomeTranscription (linguistics)Protein–protein interactionGeneArtificial intelligenceGeneticsPhilosophyBotanyLinguisticsRNA and protein synthesis mechanismsBacterial Genetics and BiotechnologyGenomics and Phylogenetic Studies