Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments

By Pouya Kheradpour, Manolis Kellis. 2013

452 citations recorded by the source corpus.

Recent advances in technology have led to a dramatic increase in the number of available transcription factor ChIP-seq and ChIP-chip data sets. Understanding the motif content of these data sets is an important step in understanding the underlying mechanisms of regulation. Here we provide a systematic motif analysis for 427 human ChIP-seq data sets using motifs curated from the literature and also discovered de novo using five established motif discovery tools. We use a systematic pipeline for calculating motif enrichment in each data set, providing a principled way for choosing between motif variants found in the literature and for flagging potentially problematic data sets. Our analysis confirms the known specificity of 41 of the 56 analyzed factor groups and reveals motifs of potential cofactors. We also use cell type-specific binding to find factors active in specific conditions. The resource we provide is accessible both for browsing a small number of factors and for performing large-scale systematic analyses. We provide motif matrices, instances and enrichments in each of the ENCODE data sets. The motifs discovered here have been used in parallel studies to validate the specificity of antibodies, understand cooperativity between data sets and measure the variation of motif binding across individuals and species.

Subfields: Molecular Biology

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Citations in the corpus

Biological Sequence Modeling with Convolutional Kernel Networks (2017, neutral): “We train a 1-layer CKN-seq and CNN on two tasks of the respective motif FOXA1 and GATA1 [Kheradpour and Kellis, 2013], using the same hyperpa-rameter settings as previously.”
Formation of Chromosomal Domains by Loop Extrusion (2015, methodology): “Genome-wide CTCF motif matches were also obtained from ENCODE (Kheradpour and Kellis, 2014), using the CTCF_known1 motif.”
Fine-mapping inflammatory bowel disease loci to single variant resolution (2017, methodology): “For each motif in the ENCODE TF ChIP-seq data (http://compbio.mit.edu/encode-motifs/, accessed Nov 2014)20, we calculated the overall information content (IC) as the sum of IC for each position53, and only considered motifs with overall IC ≥ 14 bits (equivalent to 7 perfectly conserved positions).”
Gkmexplain: Fast and Accurate Interpretation of Nonlinear Gapped k-mer SVMs Using Integrated Gradients (2018, background): “0-3 instances of the TAL1_known1 and GATA1_disc1 motifs [7] were then embedded into non-overlapping positions in each sequence.”
Cistrome and Epicistrome Features Shape the Regulatory DNA Landscape (2016, unknown): “Chromatin immunoprecipitation sequencing (ChIP-seq) is a powerful approach for TFBS discovery (Kheradpour and Kellis, 2014; Stamatoyannopoulos et al., 2012).”
Reverse-complement parameter sharing improves deep learning models for genomics (2017, methodology): “TF ChIP-seq datasets for Gm12878 were obtained from the ENCODE consortium [3].”
HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease (2015, methodology): “…that the SNP changes the match to the p300 PWM, ATTAYR-WCA, with the alternate allele changing a match to the fourth A to a G. Hover over the ‘p300 disc’ ID to see that the motif was discovered using the Trawler algorithm on a p300 ChIP-Seq experiment in HeLa cells from the EN-CODE dataset (37).”
chromVAR: Inferring transcription factor-associated accessibility from single-cell epigenomic data (2017, methodology): “The transpose of that matrix was then used as input to the Rtsne package 28 , with a perplexity parameter of 8 used for the down-sampled bulk hematopoiesis data and 25 for the single cell ATAC-seq data.”
Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest (2018, methodology): “Our motif database consists of 2065 motifs from ENCODE [44], as well as two de novo TCF7L2 motifs identified by Frietze et al.”
Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding (2017, background): “…and Bulyk, 2009) or CisBP (Weirauch et al. , 2014), or from motif collections like Factorbook (Wang et al. , 2012), the ENCODE-71 motif collection (Kheradpour and Kellis, 2014), or Homer (Heinz et al. , 2010), while some perform de-novo motif discovery based on k-mers (Arvey et al. , 2012) or as…”
motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites (2015, methodology): “In addition to these, we have included motifs from Encode (Kheradpour and Kellis, 2014), Homer (Heinz et al. , 2010), Factorbook (Wang et al. , 2012) and HOCOMOCO (Kulakovskiy et al. , 2013).”