# 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 NMDMBSGGRDTCGAACCSVKG MEME-1 NMDMBSGGRDTCGAACCSVKG 8.0e0000 1.0e0000 0.00 0.0 1 457 0 5 0.00219 1.0e0000 228 1 BKTGGCBYAGTKGKWWRRGSS MEME-2 BKTGGCBYAGTKGKWWRRGSS 8.0e0000 1.0e0000 -0.00 0.0 175 457 2 2 0.38293 1.5e-001 228 1 KATAGTGTARYGGYTAKCAYDKSMSSYT MEME-3 KATAGTGTARYGGYTAKCAYDKSMSSYT 8.0e0000 1.0e0000 -0.00 0.0 160 450 2 2 0.35556 1.3e-001 224 2 RGTTCGA DREME-1 GGTTCGA 8.0e0000 1.0e0000 0.00 0.0 1 471 0 5 0.00212 1.0e0000 235 2 ARAAAAAW DREME-2 AAAAAAAW 8.0e0000 1.0e0000 -0.00 0.0 12 470 1 5 0.02553 1.2e-001 234 2 CGYTACC DREME-3 CGYTACC 8.0e0000 1.0e0000 0.00 0.0 1 471 0 1 0.00212 1.0e0000 235 2 ACTBGGCC DREME-4 ACTBGGCC 8.0e0000 1.0e0000 0.00 0.0 2 470 0 1 0.00426 1.0e0000 234 2 ADGAAGA DREME-5 ADGAAGA 8.0e0000 1.0e0000 -0.00 0.0 123 471 3 4 0.26115 1.4e-001 235 ## # 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).