HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

Ivan V. Kulakovskiy(Engelhardt Institute of Molecular Biology), Ilya E. Vorontsov(Vavilov Institute of General Genetics), Ivan Yevshin(Siberian Branch of the Russian Academy of Sciences), Anastasiia V. Soboleva(Moscow Institute of Physics and Technology), Artem S. Kasianov(Vavilov Institute of General Genetics), Haitham Ashoor(King Abdullah University of Science and Technology), Wail Ba-Alawi(King Abdullah University of Science and Technology), Vladimir B. Bajić(King Abdullah University of Science and Technology), Yulia A. Medvedeva(Russian Academy of Sciences), Fedor Kolpakov(Siberian Branch of the Russian Academy of Sciences), Vsevolod J. Makeev(Engelhardt Institute of Molecular Biology)
Nucleic Acids Research
November 19, 2015
Cited by 253Open Access
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Abstract

Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.


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