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Motifs discovered by STREME in MEME motif format.

STREME results in XML format.

Read more about the MEME Suite's use of the IUPAC alphabets.

The number of positive sequences matching the motif (percentage).

The number of training set positive sequences matching the motif / the number of training set positive sequences.

Note these counts are made after erasing sites that match previously found motifs.

The number of training set positive sequences matching the motif.

Note these counts are made after erasing sites that match previously found motifs.

The number of training set negative sequences matching the motif / the number of training set negative sequences.

Note these counts are made after erasing sites that match previously found motifs.

The number test set positive sequences matching the motif / the number of test set positive sequences.

Note these counts are made after erasing sites that match previously found motifs.

The number of test set positive sequences matching the motif.

Note these counts are made after erasing sites that match previously found motifs.

The number of test set negative sequences matching the motif / the number of test set negative sequences.

Note these counts are made after erasing sites that match previously found motifs.

The mean distance from the center of the best match to the sequence center, averaged over all training set sequences with a match.

The mean distance from the center of the best match to the sequence center, averaged over all test set sequences with a match.

For determining if a motif is statistically significant, you should use the value in the E-value column. If there is no E-value column, that means that either the positive or negative hold-out set would have been too small (fewer than 5 sequences). For very small sequence sets, it is not practical for STREME to compute an accurate E-value. In that case, you can determine if your motif is significant by running STREME twenty or more times on shuffled versions of your positive dataset, and seeing if the Score is always larger than the Score using the original sequences. You can make shuffled sequence datasets using the MEME Suite command-line utility fasta-shuffle-letters) if you have installed the MEME Suite on your own computer.

The statistical test used in computing the Score is either the Fisher Exact Test, the Binomial Test, or the Cumulative Bates distribution. (See Inputs and Settings for the particular test being used.) The Fisher Exact Test and the Binomial Test both estimate the enrichment of the motif in the positive sequences compared to the the negative sequences. (The Binomial Test is used when the positive and negative sequences have different average lengths.) The Cumulative Bates distribution measures the tendency of motif to be near the center of the input sequences.

The statistical test used in computing the p-value is either the Fisher Exact Test, the Binomial Test, or the Cumulative Bates distribution. (See Inputs and Settings at the bottom of this document for the particular test being used.) The Fisher Exact Test and the Binomial Test both measure the enrichment of the motif in the positive test sequences compared to the the negative test sequences. (The Binomial Test is used when the positive and negative sequences have different average lengths.) The Cumulative Bates distribution measures the tendency of motif to be near the center of the sequences.

The score threshold for determining if a potential site is a match to the motif. The same threshold is applied when determining matches in the training and test sequences. The threshold is in bits.

The match score of a position in a sequence is determined by converting the motif to a base-2 log-odds matrix using the formula log2(prob[a][i]/background[a]). Here, prob[a][i] is the probability of the letter 'a' at position 'i' of the motif, and background[a] is the probability of the letter 'a' according to the background.

The names of the files containing the positive (primary) and negative (control) sequences input to STREME.

If you did not provide a file containing the negative (e.g., control) sequences, STREME created them using N-order shuffling. 0-order shuffling preserves 1-mer frequencies (i.e., the letter frequencies), 1-order shuffling preserves 2-mer frequencies, etc.

The name of the alphabet of the sequences.

The number of sequences.

The total length of the sequences.

The name of the alphabet symbol.

The frequency of the alphabet symbol in the negative sequences.

The frequency of the alphabet symbol as defined by the background model.