MEME Suite: tools for motif discovery and searching

By Timothy L. Bailey, Mikael Bodén, Fabian A. Buske, Martin C. Frith, Charles E. Grant, L. Clementi, Jingyuan Ren, Wilfred W. Li, William Stafford Noble. 2009

9,455 citations recorded by the source corpus.

The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.

Subfields: Molecular Biology

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “This concept resembles that of using k-mers for database searches [33], clustering [81], motifs [82, 83], and prediction methods [72, 76, 84 – 88].”
PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. (2014, neutral): “[1,2]).”
The MEME Suite (2015, methodology): “Six of these tools––DREME (3), MEME-ChIP (4), CentriMo (6), AME (7), SpaMo (8) and MCAST (12)––were developed or given web interfaces since the last publication describing the MEME Suite (15).”
TBtools - an integrative toolkit developed for interactive analyses of big biological data. (2020, methodology): “Functions in TBtools are, in most cases, coded from scratch using Java, with the exception of a few cross-platform programs, including BLAST (Camacho et al., 2009), MEME (Bailey et al., 2009), HMMER (Mistry et al., 2013), MUSCLE (Edgar, 2004), trimAl (Capella-Gutie´rrez et al., 2009), IQ-TREE (Nguye”
EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era (2012, background): “The latter is useful to users who would like to search promoters of differentially expressed genes identiﬁed by a micro-array experiments for over-represented sequence motifs by tools like MEME (32).”
A Unified Model for the Function of YTHDF Proteins in Regulating m6A-Modified mRNA (2020, methodology): “For each DF1, DF2 and DF3 iCLIP and miCLIP, the enriched motif presented in Figure 1B was obtained using MEME suite (Bailey et al., 2009) as previously described (Patil et al.”
DeepEnhancer: Predicting enhancers by convolutional neural networks (2016, methodology): “In order to compute the similarity of our motifs, we use a tool called TOMTOM with predefined statistical measure of motif-motif similarity [32], [33].”
Zc3h13 regulates nuclear RNA m6A methylation and mouse embryonic stem cell self-renewal (2018, methodology): “[PubMed: 24316715] Machanick P, Bailey TL. MEME-ChIP: motif analysis of large DNA datasets.”
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. (2018, positive): “TomTom identified knownmotif matches for 278 filters.”