The MEME Suite allows the biologist to discover novel motifs in collections of unaligned nucleotide or protein sequences, and to perform a wide variety of other motif-based analyses.
The MEME Suite supports motif-based analysis of DNA, RNA and protein sequences. You can also us it with sequences over novel sequence alphabets that you define. The Suite provides motif discovery algorithms using both probabilistic (MEME) and discrete models (STREME), which have complementary strengths. It also allows discovery of motifs with arbitrary insertions and deletions (GLAM2). The MEME Suite provides three tools for motif enrichment analysis--measuring the enrichment of known motifs in sets of sequences. The motif enrichment may be anywhere in the sequences (SEA, AME), or concentrated in the central regions of the sequences (CentriMo). The Suite also provides an algorithm for measuring the similarity between motifs (Tomtom). These three types of analysis are combined in a pipeline in two MEME Suite tools--XSTREME and MEME-ChIP--that perform comprehensive motif analysis in general sequences (XSTREME) or in ChIP-seq peaks (MEME-ChIP). In addition to motif discovery, the MEME Suite provides tools for scanning sequences for matches to motifs (FIMO, MAST and GLAM2Scan), scanning for clusters of motifs (MCAST), finding preferred spacings between motifs (SpaMo), predicting the biological roles of motifs (GOMo), and predicting the regulatory targets of transcription factors.
The MEME Suite is comprised of a collection of tools that work together, as shown below. Not all the tools are available as webservices, so to get the full power of the MEME Suite you will need to download and install a local copy of the software. To see what has changed recently you can peruse the release notes.
|Find ungapped motifs in unaligned DNA, RNA or protein sequences.
|Find ungapped motifs in large sets of sequences in the DNA, RNA, protein or a custom alphabet that are relativel enriched in your sequences compared to shuffled sequences or your control sequences.
|Comprehensive motif analysis of datasets where the motifs can be anywhere in the sequences.
|Comprehensive motif analysis of datasets where the motifs tend to be centrally located, such as those from ChIP-seq experiments.
|Find gapped motifs in DNA or protein sequences. It has a tutorial.
|Find PTM (post-translationally modified) motifs in fixed-width protein sequences.
|Find short, ungapped motifs in large sets of DNA or RNA sequences. Also allows discriminative motif analysis using a set of control sequences.
|Motif Enrichment Analysis
|Simple Motif Enrichment Analysis: find known DNA, RNA or protein motifs that are relatively enriched in the input sequences compared to shuffled version of those sequences or control sequences.
|Local Motif Enrichment Analysis: Find motifs that are enriched in local regions in equal-length sequences.
|Motif Enrichment Analysis: find known DNA, RNA or protein motifs that are relatively enriched in the input sequences compared to shuffled version of those sequences or control sequences, or that are enriched in sequences with small values of scores that you can specify with your input sequences.
|Motif Spacing Analysis: Find known motifs that occur with preferred spacings relative to a primary motif in a set of DNA sequences.
|Genome Ontology Motif Enrichment: Identify possible roles (Gene Ontology terms) for DNA binding motifs.
|Search a sequence database for occurrences of known motifs. This program treats each motif independently and reports all putative motif occurrences below a specified p-value threshold.
|Search a sequence database for occurrences of known motifs. This program assumes exactly one occurrence of each motif per sequence, and each sequence in the database is assigned a p-value, based on the product of the p-values of the individual motif occurrences in that sequence.
|Search a sequence database for clusters of known motifs. mcast employs a motif-based hidden Markov model, using a star topology and a novel scoring algorithm. The motifs may appear in any order.
|Search a set of aligned sequences for conserved matches to motifs. In addition to the aligned sequences and the motifs, this program requires a maximum likelihood phylogenetic tree estimating the evolutionary relationships and distances among the sequences.
|Search for occurrences of gapped motifs, discovered by GLAM2.
|Find motifs that are similar to a given DNA or RNA motif by searching a database of known motifs.
|Predict regulatory links between regulatory elements (chromosomal regions) and genes.
|Extract the regions specified in a BED file from a genome.
|Additional Primary Tools
|Print the Average Motif Affinity score of each sequence in a database. The score is calculated by averaging the likelihood ratio scores for all feasible binding events to the given sequence and to its reverse strand.
|Motif Format Conversion Scripts
|Foreign Motif Formats
|Convert an BEEML matrix file to MEME format.
|Convert a CHEN matrix file to MEME format.
|Convert a ELM tab separated file to MEME format.
|Convert an IUPAC string to MEME format.
|Convert a directory of JASPAR files to MEME format.
|Convert count or frequency matrices to MEME format.
|Convert and merge multiple MEME formatted files.
|Convert a nestedMICA (BioTiffin/XMS) matrix file to MEME format.
|Convert a PRIORITY matrix file to MEME format.
|Convert a PROSITE pattern file to MEME format.
|Convert a FASTA file with micro-RNA sequences into MEME motifs for their mRNA target sequences.
|Convert an SCPD matrix file to MEME format.
|Convert files containing sites into MEME format.
|Convert a tab-separated file exported from a spreadsheet of Taipale results to MEME format.
|Convert a TAMO matrix file to MEME format.
|Convert a TRANSFAC matrix file to MEME format.
|Convert a UNIPROBE matrix file to MEME format.
|File Format Conversion Utilities
|Convert a Clustalw multiple alignment into FASTA format.
|Convert a Clustalw multiple alignment into Phylip format.
|Convert glam2 motifs to standard alignment formats.
|Convert a Gene Ontology OBO file into a GO DAG file.
|FASTA Sequence Utilities
|Mask low-complexity regions in DNA sequences in a FASTA file to N characters.
|Output the central portion of each sequence in a FASTA file of sequences.
|Shuffle the letters in each sequence in a file of FASTA file of nucleotide sequences, preserving the dinucleotide frequencies.
|Fetch sequences from a FASTA sequence file. Requires an index file made by fasta-make-index.
|Create an index for a FASTA file for use with programs such as BED2FASTA.
|Estimate a Markov model from a FASTA file of sequences.
|Find matches to a Perl regular expression in a FASTA file of sequences.
|Compute the relative enrichment of a regular expression in two sets of sequence, where the shortest Hamming distance is used to classify sequences.
|Split primary and control sequences into training and testing sets. Control sequences are generated by shuffling if not specified. Primary sequences will be centrally trimmed to a specified length if requested.
|Read and write FASTA files.
|Make an index for a FASTA file for use by fasta-fetch.
|Writes the most frequently occurring sequence length and how many times it occurs.
|Shuffle the letters in each sequence in a file of FASTA file preserving k-mer frequencies. This makes use of uShuffle.
|Extract a random selection of the sequences in a FASTA file. Can also subsample the sequences themselves.
|Copy a FASTA sequence file changing any duplicate sequence names to insure there are no duplicates.
|Generate FASTA sequences from a Markov model.
|Print statistics about or (higher-order) shuffle sequences read from a FASTA file.
|Classify a string passed as a command line argument as an instance of the DNA or protein alphabet.
|Add q-values to AMA output.
|Create motif logos.
|Compute the distribution of priors in a MEME PSP format file.
|Compute a uniform position-specific prior equal to the mean of the position-specific prior contained in a MEME PSP format file.
|Compute priors and their distribution from raw scores in a Wiggle format file.
|Compute the Fisher Exact test p-value.
|Fit an extreme value distribution to data.
|Mask glam2 motifs out of sequences so that weaker motifs can be found.
|Identify GO terms which are implied by other GO terms, allowing the most specific GO terms to be highlighted in the conversion to html.
Create index files for the genomes in the sequence database directory
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|Extract the sequence alphabet definition from a MEME Motif Format file (e.g., MEME, STREME or DREME output).
|Create motif logos from a MEME Motif Format file (e.g., MEME, STREME or DREME output).
|Extract specified motifs from a MEME text (.txt) format file.
|Easily rename MEME Suite HTML files to unique names incorporating the path name (rather than "meme.html").
|Shuffle the columns of each motif in a file of motifs in MEME motif format.
|Calculate the statistical power of phylogentic motif models.
|Generate position-specific priors from positive (likely to contain a feature of interest) and negative (unlikely to contain a feature of interest) sequences for use as an additional input to MEME.
|Remove highly similar members of a set of sequences.
|Compute q-values from p-values.
|Given a tree and an alignment, identify the intersection of the sets of sequence IDs and leaf labels. Trim the extra sequences and leaves and print the resulting alignment and tree.
|Extract specified columns from a multiple alignment.
|Remove from an alignment all columns that correspond to a gap in a specified species.
|Perform phylogenetic shadowing on a given DNA alignment, using a given tree.
|Create or update the sequence databases for a MEME Suite web server.
|Input File Formats
|The motif format which is supported by the MEME Suite.
|A method for defining custom alphabets.
|Background frequencies for DNA or protein sequences.
|Peptides identified from tandem mass spectra.
|Position-Specific Priors (PSP)
|Priors (weights) on each position in each input sequence that can bias the search for motifs by MEME.
|A Dirichlet mixture file specifies residues' tendencies to align with one another, and is the basis for scoring columns of aligned residues in MEME and GLAM2.
|DNA or protein sequences.
|Sequence coordinates (e.g., genomic coordinates) in FASTA sequence headers.
|A multiple alignment of DNA or protein sequences.
|Foreign Motif Formats
|Motif formats that can be converted into MEME motifs using the motif format conversion scripts available when you install the MEME Suite on your own computer.
|A custom sequence alphabet for GLAM2. This can be used to provide alternate alphabets other than the standard DNA and protein.
|A file format which stores the structure of the Gene Ontology so it can be used to improve GOMo output.
|Motif Discovery Output Formats
|STREME Output Formats
|The file formats output by the STREME tool.
|XSTREME Output Formats
|The file formats output by the XSTREME tool.
|MEME-ChIP Output Formats
|The file formats output by the MEME-ChIP tool.
|MoMo Output Formats
|The file formats output by the MoMo tool.
|Motif Enrichment Output Formats
|SEA Output Formats
|The file formats output by the SEA tool.
|CentriMo Output Formats
|The file formats output by the CentriMo tool.
|AME Output Formats
|The file formats output by the AME tool.
|SpaMo Output Formats
|The file formats output by the SpaMo tool.
|GOMo Output Formats
|The file formats output by the GOMo tool.
|Motif Scanning Output Formats
|FIMO Output Formats
|The file formats output by the FIMO tool.
|MCAST Output Formats
|The file formats output by the MCAST tool.
|Motif Comparison Output Formats
|Tomtom Output Formats
|The file formats output by the Tomtom tool.
|Gene Regulation Output Formats
|T-Gene Output Formats
|The file formats output by the T-Gene tool.
|Guides and Tutorials
|Web Service Access
|How to access MEME Suite web services from Perl or Python scripts.
|How to install a local copy of the MEME Suite.
|A list of changes included in the latest release.
Development of the MEME Suite was funded by grant R01 GM103544 from the National Institutes of Health.