# WARNING: this file is not sorted! # db id alt consensus E-value adj_p-value log_adj_p-value bin_location bin_width total_width sites_in_bin total_sites p_success p-value mult_tests 1 ATTTGCATAWSAAWR MEME-1 ATTTGCATAWSAAWR 2.9e-096 1.8e-097 -222.75 0.0 86 196 962 1326 0.43878 1.9e-099 97 1 TCTCTCTGAAGCCTGCTACCTGGAGGCTT MEME-2 TCTCTCTGAAGCCTGCTACCTGGAGGCTT 2.6e0000 1.7e-001 -1.80 0.0 122 182 29 32 0.67033 2.0e-003 90 2 ATGYWAA DREME-1 ATGCAAA 1.2e-042 7.7e-044 -99.27 0.0 78 204 591 973 0.38235 7.6e-046 101 2 MATRACAA DREME-2 MATRACAA 2.5e-037 1.6e-038 -87.04 0.0 55 203 239 408 0.27094 1.6e-040 101 2 WTATGCR DREME-3 WTATGCA 7.0e-057 4.4e-058 -132.07 0.0 80 204 437 607 0.39216 4.3e-060 101 2 AWTATTCA DREME-7 AWTATTCA 6.6e-001 4.2e-002 -3.18 0.0 89 203 62 101 0.43842 4.2e-004 101 2 ATCTRCAT DREME-8 ATCTRCAT 4.1e-016 2.6e-017 -38.21 0.0 65 203 117 180 0.32020 2.5e-019 101 2 ACCDCGG DREME-11 ACCDCGG 8.6e-001 5.3e-002 -2.93 0.0 102 204 35 47 0.50000 5.4e-004 101 2 CMTTTCA DREME-12 CMTTTCA 1.7e0000 1.1e-001 -2.24 0.0 100 204 156 267 0.49020 1.1e-003 101 2 ACAGCWGG DREME-13 ACAGCWGG 1.2e0000 7.3e-002 -2.61 0.0 89 203 204 392 0.43842 7.5e-004 101 ## # Detailed descriptions of columns in this file: # # db: The name of the database (file name) that contains the motif. # id: A name for the motif that is unique in the motif database file. # alt: An alternate name of the motif that may be provided # in the motif database file. # consensus: A consensus sequence computed from the motif. # E-value: The expected number motifs that would have least one. # region as enriched for best matches to the motif as the reported region. # The E-value is the p-value multiplied by the number of motifs in the # input database(s). # adj_p-value: The probability that any tested region would be as enriched for # best matches to this motif as the reported region is. # By default the p-value is calculated by using the one-tailed binomial # test on the number of sequences with a match to the motif # that have their best match in the reported region, corrected for # the number of regions and score thresholds tested. # The test assumes that the probability that the best match in a sequence # falls in the region is the region width divided by the # number of places a motif # can align in the sequence (sequence length minus motif width plus 1). # When CentriMo is run in discriminative mode with a negative # set of sequences, the p-value of a region is calculated # using the Fisher exact test on the # enrichment of best matches in the positive sequences relative # to the negative sequences, corrected # for the number of regions and score thresholds tested. # The test assumes that the probability that the best match (if any) # falls into a given region # is the same for all positive and negative sequences. # log_adj_p-value: Log of adjusted p-value. # bin_location: Location of the center of the most enriched region. # bin_width: The width (in sequence positions) of the most enriched region. # A best match to the motif is counted as being in the region if the # center of the motif falls in the region. # total_width: The window maximal size which can be reached for this motif: # rounded(sequence length - motif length +1)/2 # sites_in_bin: The number of (positive) sequences whose best match to the motif # falls in the reported region. # Note: This number may be less than the number of # (positive) sequences that have a best match in the region. # The reason for this is that a sequence may have many matches that score # equally best. # If n matches have the best score in a sequence, 1/n is added to the # appropriate bin for each match. # total_sites: The number of sequences containing a match to the motif # above the score threshold. # p_success: The probability of falling in the enriched window: # bin width / total width # p-value: The uncorrected p-value before it gets adjusted to the # number of multiple tests to give the adjusted p-value. # mult_tests: This is the number of multiple tests (n) done for this motif. # It was used to correct the original p-value of a region for # multiple tests using the formula: # p' = 1 - (1-p)^n where p is the uncorrected p-value. # The number of multiple tests is the number of regions # considered times the number of score thresholds considered. # It depends on the motif length, sequence length, and the type of # optimizations being done (central enrichment, local enrichment, # score optimization).