MEME-ChIP: motif analysis of large DNA datasets

By Philip Machanick, Timothy L. Bailey. Bioinform., 2011

1,733 citations recorded by the source corpus.

Motivation: Advances in high-throughput sequencing have resulted in rapid growth in large, high-quality datasets including those arising from transcription factor (TF) ChIP-seq experiments. While there are many existing tools for discovering TF binding site motifs in such datasets, most web-based tools cannot directly process such large datasets. Results: The MEME-ChIP web service is designed to analyze ChIP-seq ‘peak regions’—short genomic regions surrounding declared ChIP-seq ‘peaks’. Given a set of genomic regions, it performs (i) ab initio motif discovery, (ii) motif enrichment analysis, (iii) motif visualization, (iv) binding affinity analysis and (v) motif identification. It runs two complementary motif discovery algorithms on the input data—MEME and DREME—and uses the motifs they discover in subsequent visualization, binding affinity and identification steps. MEME-ChIP also performs motif enrichment analysis using the AME algorithm, which can detect very low levels of enrichment of binding sites for TFs with known DNA-binding motifs. Importantly, unlike with the MEME web service, there is no restriction on the size or number of uploaded sequences, allowing very large ChIP-seq datasets to be analyzed. The analyses performed by MEME-ChIP provide the user with a varied view of the binding and regulatory activity of the ChIP-ed TF, as well as the possible involvement of other DNA-binding TFs. Availability: MEME-ChIP is available as part of the MEME Suite at http://meme.nbcr.net. Contact: t.bailey@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4f9c45dd7eee5490a217b9cc5b7558b00a2d1863

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Citations in the corpus

HSF1 drives a transcriptional program distinct from heat shock to support highly malignant human cancers. (2012, methodology): “We analyzed the 100 bp genomic regions surrounding the peaks of HSF1 binding unique to BPLER cells using the ab initio motif discovery algorithm MEME (Machanick and Bailey, 2011).”
DNA methylation on N6-adenine in C. elegans (2015, positive): “MEME-ChIP (Machanick and Bailey, 2011) was then used to identify motifs in each group.”
Mapping genome-wide transcription-factor binding sites using DAP-seq (2017, background): “Incubate on ice or at −20°C for at least 15 min, but no longer than 24 hours.”
Facilitators and Impediments of the Pluripotency Reprogramming Factors’ Initial Engagement with the Genome (2012, positive): “De novo motif discovery (Machanick and Bailey, 2011) showed that O, S, and K are able to access many of their specific DNA targets in early reprogramming, with M accessing a sequence related to its canonical target (p 1 3 10 À5 ) (Figure 1B).”
Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors (2012, methodology): “We used the MEME-ChIP software suite (Machanick and Bailey 2011) to identify enriched sequence motifs in the [–50 bp, +50 bp] window around the summits of the top 500 peaks (the training set) for each ChIP-seq data set.”
The Xist lncRNA exploits three-dimensional genome architecture to spread across the X-chromosome (2013, methodology): “Motif analysis on initiation sites To identify de novo motifs, we tested 500-bp peaks using MEME-ChIP (74) (http://meme.”
Zc3h13 regulates nuclear RNA m6A methylation and mouse embryonic stem cell self-renewal (2018, neutral): “[PubMed: 24316715] Machanick P, Bailey TL. MEME-ChIP: motif analysis of large DNA datasets.”
Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features (2014, result): “We then compared our results with the previous PWMs found in the same data sets using a conventional tool (MEME-ChIP) [18,21].”
STREME: Accurate and versatile sequence motif discovery (2020, methodology): “We have incorporated STREME into the MEME-ChIP tool (Machanick and Bailey, 2011), which now combines the motifs discovered by MEME and STREME into a single comprehensive analysis of ChIPseq and similar data.”