Abstract
Summary: Current methods for motif discovery from chromatin immunoprecipitation followed by sequencing (ChIP-seq) data often identify non-targeted transcription factor (TF) motifs, and are even further limited when peak sequences are similar due to common ancestry rather than common binding factors. The latter aspect particularly affects a large number of proteins from the Cys2His2 zinc finger (C2H2-ZF) class of TFs, as their binding sites are often dominated by endogenous retroelements that have highly similar sequences. Here, we present recognition code-assisted discovery of regulatory elements (RCADE) for motif discovery from C2H2-ZF ChIP-seq data. RCADE combines predictions from a DNA recognition code of C2H2-ZFs with ChIP-seq data to identify models that represent the genuine DNA binding preferences of C2H2-ZF proteins. We show that RCADE is able to identify generalizable binding models even from peaks that are exclusively located within the repeat regions of the genome, where state-of-the-art motif finding approaches largely fail.Availability and implementation: RCADE is available as a webserver and also for download at http://rcade.ccbr.utoronto.ca/.Supplementary information: Supplementary data are available at Bioinformatics online.Contact: t.hughes@utoronto.ca
Highlights
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most widely used method for mapping the genomic regions that are associated with transcription factors (TFs) (ENCODE Project Consortium, 2012)
Current approaches for motif finding from ChIP-seq data almost exclusively rely on the assumption that the genomic regions associated with a particular TF have diverse sequences except at the sites that are directly bound by the TF, where the sequences are converged to match the TF binding preference
This assumption is violated in many cases, such as when the ChIP-seq peaks are dominated by binding sites of the interacting partners of the TF of interest, represent targets of multiple cooperative regulatory factors, and/or are enriched for repetitive DNA sequences such as endogenous retroelements (EREs)
Summary
Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most widely used method for mapping the genomic regions that are associated with transcription factors (TFs) (ENCODE Project Consortium, 2012). Current approaches for motif finding from ChIP-seq data almost exclusively rely on the assumption that the genomic regions associated with a particular TF have diverse sequences except at the sites that are directly bound by the TF, where the sequences are converged to match the TF binding preference. Not all of the C2H2-ZF domains within a protein participate in DNA binding at the same time, further complicating the task of predicting DNA preference from protein sequence To address these issues, we present recognition code-assisted discovery of regulatory elements (RCADE), which combines predictions from a recent recognition code of C2H2-ZFs (Najafabadi et al, 2015) with motif optimization based on ChIP-seq data to overcome limitations associated with current approaches, and to identify regions of the C2H2-ZF protein that engage in DNAbinding
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