# 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 GTGTGTGTGTGTGTGT MEME-1 GTGTGTGTGTGTGTGT 7.4e-013 1.0e-015 -34.53 0.0 89 485 94 214 0.18351 4.2e-018 242 1 ACACACACACACACACACAC MEME-2 ACACACACACACACACACAC 1.7e-008 2.3e-011 -24.48 0.0 167 481 152 266 0.34719 9.7e-014 240 1 CCTGTAATCCCAGCWMYTYGGGAGGCTGAG MEME-3 CCTGTAATCCCAGCWMYTYGGGAGGCTGAG 2.1e-001 2.8e-004 -8.17 0.0 169 471 39 58 0.35881 1.2e-006 235 2 CACGCSYG DREME-2 CACGCSYG 8.1e-006 1.1e-008 -18.33 0.0 125 493 72 140 0.25355 4.5e-011 246 3 M0198_1.02 (SOHLH2)_(Mus_musculus)_(DBD_0.84) NGYVCGTGCN 6.0e-002 8.0e-005 -9.43 0.0 179 491 261 556 0.36456 3.3e-007 245 3 M0212_1.02 (TCFL5)_(Mus_musculus)_(DBD_1.00) NBCDCGHGVN 1.9e0000 2.5e-003 -5.98 0.0 127 491 163 468 0.25866 1.0e-005 245 3 M0413_1.02 (ZBTB1)_(Mus_musculus)_(DBD_0.99) NDTGCGKGDN 1.5e-001 2.0e-004 -8.52 0.0 275 491 373 565 0.56008 8.2e-007 245 3 M0432_1.02 (ZFP161)_(Mus_musculus)_(DBD_1.00) NNCGYGCHH 5.1e-005 6.9e-008 -16.49 0.0 134 492 184 451 0.27236 2.8e-010 245 3 M4427_1.02 CTCF NYGGCCASCAGRKGGCRSYVB 1.8e0000 2.4e-003 -6.04 0.0 88 480 121 456 0.18333 1.0e-005 239 3 M4532_1.02 MYC CCACGTGSYY 7.5e0000 1.0e-002 -4.59 0.0 135 491 185 521 0.27495 4.2e-005 245 3 M4536_1.02 E2F1 VRRVRGVGCGCGCRS 2.3e0000 3.0e-003 -5.79 0.0 148 486 191 484 0.30453 1.3e-005 242 3 M5509_1.02 HEY1 GGCACGTGBC 3.4e-001 4.6e-004 -7.68 0.0 141 491 183 472 0.28717 1.9e-006 245 3 M5875_1.02 TBX1 AGGTGTGAAAAAAGGTGTGA 1.3e0000 1.8e-003 -6.32 0.0 171 481 119 241 0.35551 7.5e-006 240 3 M5981_1.02 ZSCAN4 TTTTCAGKGTGTGCA 5.2e-004 7.1e-007 -14.16 0.0 108 486 116 314 0.22222 2.9e-009 242 3 M6139_1.02 AHR KCACGCRAH 6.3e-008 8.5e-011 -23.19 0.0 150 492 251 557 0.30488 3.5e-013 245 3 M6151_1.02 ARNT BYRCGTGC 2.4e-005 3.3e-008 -17.24 0.0 143 493 221 527 0.29006 1.3e-010 246 3 M6200_1.02 EGR3 WGAGTGGGYGT 4.6e-002 6.3e-005 -9.68 0.0 104 490 166 545 0.21224 2.6e-007 244 3 M6210_1.02 ENO1 YDSMCACRTGSYB 1.6e-002 2.2e-005 -10.72 0.0 126 488 200 557 0.25820 9.1e-008 243 3 M6212_1.02 EPAS1 CMCACGYAYDCAC 2.0e-005 2.6e-008 -17.45 0.0 278 488 387 551 0.56967 1.1e-010 243 3 M6266_1.02 GLI3 BTGGGTGGTCB 3.3e0000 4.4e-003 -5.43 0.0 210 490 273 525 0.42857 1.8e-005 244 3 M6273_1.02 HEY2 GBBGGCWCGTGGCHTBV 2.9e-001 3.9e-004 -7.84 0.0 144 484 192 483 0.29752 1.6e-006 241 3 M6275_1.02 HIF1A SBSTACGTGCSB 1.8e-006 2.4e-009 -19.83 0.0 171 489 255 516 0.34969 1.0e-011 244 3 M6323_1.02 KLF3 HRCYWGGGTGKGGCT 4.0e-002 5.4e-005 -9.82 0.0 116 486 167 489 0.23868 2.3e-007 242 3 M6352_1.02 MYCN CCACGTGS 8.3e-003 1.1e-005 -11.41 0.0 137 493 199 513 0.27789 4.5e-008 246 3 M6456_1.02 RREB1 DKGKKKGKGGKTGKTTKGGGKT 2.3e0000 3.0e-003 -5.80 0.0 87 479 127 489 0.18163 1.3e-005 239 3 M6464_1.02 SMAD2 GTGTCHGKCTV 9.5e0000 1.3e-002 -4.36 0.0 90 490 143 572 0.18367 5.3e-005 244 ## # 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).