If your sequences are not in a standard alphabet (DNA, RNA or protein), you must input a custom alphabet file.

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Click on the menu at the left to see which of the following sequence input methods are available.
Type in sequences
When this option is available you may directly input multiple sequences by typing them. Sequences must be input in FASTA format.
Upload sequences
When this option is available you may upload a file containing sequences in FASTA format.
Databases (select category)
When this option is available you may first select a category of sequence database from the list below it. Two additional menus will then appear where you can select the particular database and version desired, respectively. The full list of available sequence databases and their descriptions can be viewed here.
Submitted sequences
This option is only available when you have invoked the current program by clicking on a button in the output report of a different MEME Suite program. By selecting this option you will input the sequences sent by that program.
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Select an available sequence database from this menu.

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Select an available version of the sequence database from this menu.

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Select an available tissue/cell-specificity from this menu.

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Selecting this option will filter the sequence menu to only contain databases that have additional information that is specific to a tissue or cell line.

This option causes MEME Suite to use tissue/cell-specific information (typically from DNase I or histone modification ChIP-seq data) encoded as a position specific prior that has been created by the MEME Suite create-priors utility. You can see a description of the sequence databases for which we provide tissue/cell-specific priors here.

Note that you cannot upload or type in your own sequences when tissue/cell-specific scanning is selected.

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Enter text naming or describing this analysis. The job description will be included in the notification email you receive and in the job output.

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Normal mode

You provide one set of sequences and MEME discovers motifs enriched in this set. Enrichment is measured relative to a random model based on frequencies of the letters in your sequences, or relative to the frequencies given in a "Custom background model" that you may provide (see Advanced options).

Discriminative mode

You provide two sets of sequences and MEME discovers motifs that are enriched in the first (primary) set relative to the second (control) set. In Discriminative mode, we first calculate a position-specific prior from the two sets of sequences. MEME then searches the first set of sequences for motifs using the position-specific prior to inform the search. This approach is based on the simple discriminative prior "D" described in Section 3.5 of Narlikar et al. We modify their approach to search for the "best" initial motif width, and to handle protein sequences using spaced triples.

Refer to the psp-gen documentation and to our paper for more details.

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<< back to discovery mode description

Position-specific priors (PSPs) assign a probability that a motif starts at each possible location in your sequence data. MEME uses PSPs to guide its search, biasing the search towards sites that have higher values in the PSP. MEME creates a PSP when you use it in "Discriminative mode", up-weighting words in the primary dataset that occur frequently there but are infrequent in the negative dataset.

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<< back to modes description

Spaced triples are sub-sequences in which only the first and last letter (residue or amino acid for protein) and one interior letter are used in matches. For example, the subsequence MTFEKI contains the following triples:

where "." matches anything. We use spaced triples for protein because the probability of exact matches is much lower than for DNA due to the much larger amino acid alphabet.

To score a word using spaced triples, we count how often each triple contained in the word occurs in the primary and control sequence sets, and use the maximum over all triples as the word count in the formula for scoring words described by Narlikar et al.

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Please enter sequences that you believe share one or more motifs. When running MEME in "Discriminative mode", this set of sequences is referred to as the "primary sequence set".

The sequences may contain no more than 60,000 characters total and must be in FASTA format.

See the example DNA sequences which were used to create the sample output.

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Please enter sequences that you believe contain patterns you wish to avoid making motifs from. This is referred to as the control set.

The control set should contain sequences that are in some sense a contrast to likely sites for motifs (e.g. sequences rejected as unlikely to contain a transcription factor binding site), but otherwise similar to the primary sequence set.

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You can use a background model with MEME in order to normalize for biased distribution of letters and groups of letters in your sequences. A 0-order model adjusts for single letter biases, a 1-order model adjusts for dimer biases (e.g., GC content in DNA sequences), etc.

By default MEME will use a the letter frequencies in the primary sequence set to create a 0-order background model. Alternatively, you may select 'Upload background model' and you can then specify here a file containing a background model in a simple format.

The downloadable version of the MEME Suite also contains a program named fasta-get-markov that you can use to create background model files in the correct format from FASTA sequence files.

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This is where you tell MEME how you believe occurrences of the motifs are distributed among the sequences. Selecting the correct type of distribution improves the sensitivity and quality of the motif search.

Zero or one occurrence per sequence
MEME assumes that each sequence may contain at most one occurrence of each motif. This option is useful when you suspect that some motifs may be missing from some of the sequences. In that case, the motifs found will be more accurate than using the one occurrence per sequence option. This option takes more computer time than the one occurrence per sequence option (about twice as much) and is slightly less sensitive to weak motifs present in all of the sequences.
One occurrence per sequence
MEME assumes that each sequence in the dataset contains exactly one occurrence of each motif. This option is the fastest and most sensitive but the motifs returned by MEME will be "blurry" (less specific) if they do not occur in every input sequence.
Any number of repetitions
MEME assumes each sequence may contain any number of non-overlapping occurrences of each motif. This option is useful when you suspect that motifs repeat multiple times within a single sequence. In that case, the motifs found will be much more accurate than using one of the other options. This option can also be used to discover repeats within a single sequence. This option takes much more computer time than the one occurrence per sequence option (about ten times as much) and is somewhat less sensitive to weak motifs that do not repeat within a single sequence than the other two options.
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MEME will keep searching until it finds this many motifs or until it exceeds one of its other thresholds (e.g., maximum run time). Note that unlike DREME, MEME does not use an E-value threshold, so you should always check the E-value of any motifs discovered by MEME.

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This is the width (number of characters in the sequence pattern) of a single motif. MEME chooses the optimal width of each motif individually using a heuristic function. You can choose limits for the minimum and maximum motif widths that MEME will consider. The width of each motif that MEME reports will lie within the limits you choose.

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This is the total number of sites in the primary sequence set where a single motif occurs. You can choose limits for the minimum and maximum number of occurrences that MEME will consider. If you have prior knowledge about the number of occurrences that motifs have in your primary sequence set, limiting MEME's search in this way can can increase the likelihood of MEME finding true motifs.

MEME chooses the number of occurrences to report for each motif by optimizing a heuristic function, restricting the number of occurrences to the range you give here.

If you do not select one of these fields, MEME uses the following defaults for the range of the number of motif sites, where "n" is the number of sequences in the primary sequence set:

Zero or one occurrence per sequencesqrt(n)n
One occurrence per sequencenn
Any number of repetitionssqrt(n)min(5*n, 600)
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Checking this box instructs MEME to NOT check the reverse complement of the input sequences for motif sites when analyzing DNA or RNA sequences.

Note: When your sequences are RNA, you should select this option to ensure that only the given strand is searched for motifs.

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Checking this box causes MEME to search only for DNA palindromes.

This causes MEME to average the letter frequencies in corresponding motif columns together. For instance, if the width of the motif is 10, columns 1 and 10, 2 and 9, 3 and 8, etc., are averaged together. The averaging combines the frequency of A in one column with T in the other, and the frequency of C in one column with G in the other. If this box is not checked, the columns are not averaged together.

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Checking this box causes MEME to shuffle each of the primary sequences individually. The sequences will still be the same length and have the same character frequencies but any existing patterns will be obliterated.

Using this option repeatedly you can get an idea of the E-values of motifs discovered in "random" sequence datasets similar to your primary dataset. This can help you determine a reasonable E-value cutoff for motifs discovered in your unshuffled primary sequence dataset.

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Data Submission Form

Perform motif discovery on DNA, RNA or protein datasets.

Select the motif discovery mode

Select the sequence alphabet

Use sequences with a standard alphabet or specify a custom alphabet.

Input the primary sequences

Enter sequences in which you want to find motifs.

Select the site distribution

How do you expect motif sites to be distributed in sequences?

Select the number of motifs

How many motifs should MEME find?

Input job details

(Optional) Enter your email address.

(Optional) Enter a job description.

Advanced options hidden modifications! [Reset]

What should be used as the background model?

How wide can motifs be?

How many sites must each motif have?

Can motif sites be on both strands? (DNA/RNA only)

Should MEME restrict the search to palindromes? (DNA only)

Should MEME shuffle the sequences?

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Note: if the combined form inputs exceed 80MB the job will be rejected.