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.
Upload BED file
When this option is available you may upload a file containing
sequence coordinates in BED 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.
Specify a file to upload containing
sequence coordinates in BED format.
The file must be based on the exact genome version you specified in the
menus above.
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.
Enter the email address where you want the job notification email to
be sent. Please check that this is a valid email address!
The notification email will include a link to your job results.
Note: You can also access your jobs via the Recent Jobs
menu on the left of all MEME Suite input pages. That menu only
keeps track of jobs submitted during the current session of your internet browser.
Note: Most MEME Suite servers only store results for a couple of days.
So be sure to download any results you wish to keep.
Select the PTM file(s) that you wish to analyze. The PTM file(s) should
all use the same format. This can be in either a
Peptide-Spectrum Match (PSM) format
or a pure-sequence format (FASTA or Raw).
For PSM formats, which contain tab-separated columns and a header line
giving the column names, you must specify the name of the column
that contains the modified peptides in the provided field.
The website will attempt to guess the file format based on
what appears in the first (non-blank) line in the file, and will indicate
the its guess in the 'Format?' column. The website will also indicate
the correct value of the 'Modified Peptide Column Name' for that format,
which you can change if your files use a slightly different format.
If the first (non-empty) line in the file contains one or more
tab characters, but none of the known values for 'Modified Peptide Column Name'
are found (see Peptide-Spectrum Match (PSM) format),
the website will display 'unknown PSM format' in the 'Format?' column.
This will allow you to use PSM formats that have different column names
than the known formats, as long as modified amino acids are indicated using a
one of the 'Modified Peptide Formats' described in the last column of the table in
Peptide-Spectrum Match (PSM) format.
For pure-sequence formats, you may not specify a 'Modified Peptide Column Name',
the peptides must use the standard IUPAC protein alphabet,
and must all be the same length. The pure-sequence formats are:
FASTA: List of fixed width peptides centered around the modification site in FASTA format.
Raw: List of fixed width peptides centered around the modification site,
one per line. For example,
the following is a (partial) list of length-7 peptides centered around a (modified)
serine residue:
The width of motifs to discover.
Because motifs will be symmetric around the central, modified residue, width
must be odd. The behavior of MoMo depends on the format of the PTM input file(s).
PTM file format
MoMo Behavior
FASTA or Raw format
No effect. An error is reported if the length of any sequence in the input
files differs from Width.
PSM format
If a modified peptide is shorter than Width,
MoMo will first attempt to expand it by looking up its context in the
protein database file, if given (see option
--protein-database, below.)
If the modified peptide is still shorter than Width,
MoMo will pad it on either side as required using the Protein IUPAC 'X' character.
If the longest modified peptide is still shorter than Width,
MoMo will set the motif width to the length of the longest (expanded and padded) modified peptide.
The background peptides will be extracted from the context sequences.
By default, background peptides are generated by shuffling each foreground
peptide while conserving its central residue.
Mimic the behavior of (the Harvard version of)
motif-x
more closely by only calculating binomial p-values no smaller than
10-16 for residue/position pairs.
Smaller p-values are set to 10-16,
and ties are broken by sorting residue/position pairs by decreasing number of peptides
that match them.
Create one motif per mass instead of one motif per mass and central peptide.
(The modification mass is given as a number following
the modified amino acid in the modified peptide as described in the
PSM format documentation.)
For example, phosphorylation is typically specified
as a mass of 79.97 added to the residues S, T or Y. If this option
is not checked, then three separate motifs are generated, each with a perfectly
conserved central residue. If this option is checked,
then all the phosphorylation events are combined into a single
motif, with a mixture of S, T and Y in the central position.
Any groups of modified peptides whose <width> central residues, after expansion and padding,
are identical will be replaced with a single copy. Note: Since shorter peptides will be
padded with the 'X' character, which matches any other character, shorter peptides will
match longer ones that contain them, and will be subject to elimination.
Filter the entries in the PSM-formatted input file based on one of its fields.
Select the check box and then specify the Field, Test and Threshold in the three columns that will be provided.
(Not available with FASTA and Raw formatted input files.)
Creates a maximum-likelihood position weight matrix (PWM) motif for
each distinct central residue present in the modified peptides in the input PTM file(s).
The weights in the PWM are the observed frequencies of the amino acids in the equal-length
modified peptides, aligned on their central residue (the modified amino acid).
If the modified peptides in the input PTM file(s) have differing lengths,
their lengths are adjusted to be equal, as described below under the advanced option "How wide
will the motifs be?".
motif-x
The motif-x algorithm utilizes a greedy iterative search to discover motifs by recursively
picking the most statistically significant position/residue pair according to
binomial probabilility, reducing the dataset to only sequences containing that pair,
and continuing until no more position/residue pairs are significant according
to a user-defined threshold. If this motif has at least one statistically significant
position/residue pair, all instances of the pattern are removed,
and the algorithm continues to generate motifs until this condition fails.
The motif-x algorithm is described in the paper
Schwartz, D. and Gygi, S. P. (2005).
"An iterative statistical approach to the identification of protein phosphorylation motifs from
large-scale data sets". Nature Biotechnology, 23(11), 1391-1398.
MoDL
The MoDL algorithm is based on the principle of minimum description length (MDL).
It searches for a set of motifs that minimizes the number of bits to encode the set of
modified peptides and motifs, using a greedy and iterative approach.
The algorithm uses a list of candidate single-residued motifs (excluding the modified site)
that exist in the modification dataset. Starting with an empty set of motifs, at each iteration,
a set of potential motif sets are generated by either removing a candidate motif,
adding a candidate motif, adding a candidate motif then removing a motif,
merging a motif with a candidate motif, or merging a motif with a candidate motif
and then removing a motif. From this set of potential motifs, the algorithm chooses
the motif set with the minimum description length, and repeats the algorithm a specified
number of times t, or until the description length does not change for L iterations
(t=50 and L=10 by default). Finally, the algorithm returns the motif
with the minimum description length among all motifs found.
The MoDL algorithm described in the paper
Ritz, A., Shakhnarovich, G., Salomon, A., and Raphael, B. (2009).
"Discovery of phosphorylation motif mixtures in phosphoproteomics data".
Bioinformatics, 25(1), 14-21.
