The name of the database (file name) that contains the motif.

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A name for the motif that is unique in the motif database file.

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An alternate name of the motif that may be provided in the motif database file.

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For statistical test methods, splitting the sorted input sequences into two partitions at this rank gives the optimal p-value. Sequences at and above this rank were treated as "positives", and sequences with larger rank were treated as "negatives" when applying the statistical test that gives the optimal p-value. For linreg), the optimal split is the one that yields the minimum mean squared error.

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The (optimal) enrichment p-value of the motif according to the statistical test.

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The adjusted (optimal) enrichment p-value of the motif according to the statistical test. This is the best p-value for the motif, adjusted for multiple tests using a Bonferroni correction. The number of multiple tests is given at the start of this section. If the p-value is p and the number of multiple tests is n, then the adjusted p-value is 1 - (1-p)**n.

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The value of Spearman's rank correlation coefficient (ρ). It varies between -1 and +1. Low values of ρ indicate high correlation between the ranks of sequences sorted by motif scores (decreasing) or FASTA scores (increasing). So, assuming small FASTA scores are "good", a low value of ρ indicates that a motif is enriched in the "good" sequences.

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The value of the mean squared error of the linear regression of X vs Y.

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The slope of the regression line: Y = mX + b.

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The Y-intercept of the regression line: Y = mX + b.

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For further information on how to interpret these results or to get a copy of the MEME software please access http://meme-suite.org.

If you use AME in your research, please cite the following paper:
Robert McLeay and Timothy L. Bailey, "Motif Enrichment Analysis: A unified framework and method evaluation", BMC Bioinformatics, 11:165, 2010, doi:10.1186/1471-2105-11-165. [full text]

Enriched Motifs   |   Input Files   |   Program information

Enriched Motifs

Fixed partition size: number of primary sequences (18)

Sequence motif score: avg_odds
Background model source: uniform
Background model frequencies: 0.25,0.25,0.25,0.25
Total pseudocount added to a motif column: 0.25

Statistical test: Wilcoxon rank-sum test
Ranksum method: quick
Threshold p-value for reporting results: 0.05
Number of multiple tests for Bonferroni correction: #Motifs × #PartitionsTested = 68 × 1 = 68

Database 
ID 
Alt ID 
Split 
p-value 
Adjusted p-value 
Spearman's Rank CC (ρ)  
Mean squared error 
dpinteractcrp1.38e-59.39e-4

Input Files

Sequences

Primary SequencesNumber Control SequencesNumber
crp0.fna18lex0.fna16

Motifs

DatabaseSourceMotif Count
dpinteractdpinteract.meme68
AME version
4.11.4 (Release date: Thu Mar 30 17:02:08 2017 -0700)
Copyright © Robert McLeay r.mcleay@imb.uq.edu.au & Timothy Bailey t.bailey@imb.uq.edu.au, 2009.
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