MEME: discovering and analyzing DNA and protein sequence motifs

By Timothy L. Bailey, Nadya Williams, C. Misleh, Wilfred W. Li. 2006

2,594 citations recorded by the source corpus.

MEME (Multiple EM for Motif Elicitation) is one of the most widely used tools for searching for novel ‘signals’ in sets of biological sequences. Applications include the discovery of new transcription factor binding sites and protein domains. MEME works by searching for repeated, ungapped sequence patterns that occur in the DNA or protein sequences provided by the user. Users can perform MEME searches via the web server hosted by the National Biomedical Computation Resource () and several mirror sites. Through the same web server, users can also access the Motif Alignment and Search Tool to search sequence databases for matches to motifs encoded in several popular formats. By clicking on buttons in the MEME output, users can compare the motifs discovered in their input sequences with databases of known motifs, search sequence databases for matches to the motifs and display the motifs in various formats. This article describes the freely accessible web server and its architecture, and discusses ways to use MEME effectively to find new sequence patterns in biological sequences and analyze their significance.

Subfields: Molecular Biology, Plant Science

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Citations in the corpus

Impact of cytosine methylation on DNA binding specificities of human transcription factors (2017, methodology): “See table S1 for the sequences of the Illumina sequencing adapters, and table S4 for the number of aligned reads and E-values for the enriched motifs in the peak regions detected by MEME (76) for each experiment.”
RSAT: regulatory sequence analysis tools (2008, methodology): “The MEME server (23) combines an expectation–maximization pattern-discovery algorithm (24) with a matrix-based pattern-matching tool.”
Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data (2008, methodology): “For this, we used MEME (21) 3825) with 60 or more tags.”
MEME-ChIP: motif analysis of large DNA datasets (2011, neutral): “Received on January 23, 2011; revised on March 18, 2011; accepted on April 6, 2011”
Most Sinorhizobium meliloti Extracytoplasmic Function Sigma Factors Control Accessory Functions (2018, methodology): “Promoter consensus motifs for putative target genes of each ECF were generated by subjecting sequences upstream of the TSS to MEME (Multiple Em for Motif Elicitation) analyses (83), as described previously (37), and by comparison to cross-species promoter consensuses (16, 17, 31).”
A computational pipeline for comparative ChIP-seq analyses (2011, neutral): “org/ ) De novo motifs search: MEME-ChIP (MEM”
Global analysis of the insulator binding protein CTCF in chromatin barrier regions reveals demarcation of active and repressive domains. (2008, methodology): “MEME (Bailey et al. 2006) with default parameters was used to identify statistically over-represented consensus motifs within the inferred binding sites.”
ChIP-Seq: advantages and challenges of a maturing technology (2009, methodology): “The sequences of the top-scoring sites can be entered into motif-finding algorithm programs such as MEM”
The Amborella Genome and the Evolution of Flowering Plants (2013, background): “Woodgett, GSK-3: tricks of the trade for a multi-tasking kinase, J Cell Sci 116, 1175–1186 (2003).”
MEME Suite: tools for motif discovery and searching (2009, background): “It offers a significantly expanded set of programs for these tasks compared with the earlier web server (1).”
An extended transcriptional network for pluripotency of embryonic stem cells. (2008, methodology): “Interestingly the consensus motif (ATTTGCAT) predicted from MEME (e-value 1.4E-50, Figure 3D) was similar to sequences previously predicted by different algorithms as Oct4 or Sox2-Oct4 target sequences (Loh et al., 2006; Macisaac et al., 2006).”
A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery (2016, methodology): “We compare our MD algorithm with a well-known Multiple EM for Motif Elicitation (MEME) method (Bailey et al., 2006).”
DOOR: a database for prokaryotic operons (2008, methodology): “Two of them are identified here by running MEME.”
Human Splicing Finder: an online bioinformatics tool to predict splicing signals (2009, background): “Note that these motifs were too short to be processed with MEME (34).”