# The MEME Suite

## Motif-based sequence analysis tools

# AME

## Analysis of Motif Enrichment

### Usage:

`ame [options] <sequence file> <motif file>+`

### Description

AME (Analysis of Motif Enrichment) scores a set of DNA sequences given a set of DNA-binding motifs, treating each subsequence (and its reverse complement) in the sequence as a possible match to the motif.

AME supports a wide variety of methods for scoring motif enrichment, many methods of testing the scored motif enrichment for significance. If no control sequence set is given, the primary sequences are assumed to be sorted in decreasing order of "importance" according to some secondary criterion. For each motif, AME determines if the sequences at the top of the list are significantly enriched for matches to it. With no control sequences, by default AME performs unconstrained partition maximization, looking for the pair of thresholds (on motif score and the secondary criterion, respectively) that yields the most significant result.

If a set of control sequences is provided, AME determines if each motif is enriched in the primary set compared to the control set.

### Input

#### Sequence File

A collection of (primary) sequences in FASTA format. The sequences must be sorted by increasing value of some secondary criterion (e.g., expression level, peak height, fluorescence score). In this documentation, we refer to this secondary criterion as the "FASTA score". This score can optionally be placed in the FASTA ID line. If present, the FASTA score must come immediately after the sequence ID. For example, if the FASTA ID line is

`>seq_1 0.123`

then `0.123` is the FASTA score for that sequence.

#### Motif File

A file containing motifs in MEME format. More than one file can be specified.

### Output

AME writes to a directory, `ame_out`, unless a different directory name is specified on the command line. The output directory contains outputs in two formats: HTML and plain text, in files named respectively `ame.html` and `ame.txt`.

### Options

Option | Parameter | Description | Default Behaviour
--- | --- | --- | ---
General Options
--control | control file | A collection of control sequences in FASTA format. If a set of control sequences is provided, AME determines if each motif is enriched in the primary set compared to the control set. AME does this by appending the control sequence set to the primary sequence set and sets `--fix-partition` to the number of primary sequences.
--method | fisher|ranksum|mhg|4dmhg|spearman|linreg | Select the association function for testing motif enrichment significance. `fisher` - one-tailed Fisher's Exact test `ranksum` - one-tailed Wilcoxon rank-sum test, also known as the Mann-Whitney U test `mhg` and `4dmhg` - two-tailed tests described in McLeay and Bailey, "Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data", BMC Bioinformatics 11:165, 2010. Note: Motifs enriched in either the primary or control sequences (or at the top or bottom of the primary sequences if only one sequence file is given) are considered significant by these tests. `spearman` - Spearman's rank coefficient (ρ) between the motif score and the FASTA score (in the FASTA IDs of the sequences). Not valid with `--control`. Not valid with `--fix-partition`; always uses all input sequences. `linreg` - mean-squared error of the linear regression of the motif score and the FASTA score (in the FASTA IDs of the sequences). Note that the FASTA ID lines must each contain a FASTA score with this function. Not valid with `--control`. Using this function, AME reports raw values only. For the full approach used in the paper, please use the tool RAMEN, which is also included in the MEME Suite. RAMEN also supports estimation of p-values. All statistical tests except `mhg` and `4dmhg` use the alternative hypothesis that high motif scores are enriched in the primary sequences (or among the first sequences if only one set is given). Note that linear regression and Spearman rank correlation tests do not calculate p-values. Please use RAMEN if you desire to use linear regression with p-values. | The Fisher's Exact test (`fisher`) is used for testing motif enrichment.
--scoring | avg|max|sum|totalhits | Method of scoring a single sequence for matches to a motif. The score assigned to a sequence is either: `avg` - the average motif score of the sequence (note: motif scores are odds scores, not log-odds scores) `max` - the maximum motif score of any position in the sequence `sum` - the sum of the motif (odds) scores over the sequence `totalhits` - the total number of positions in the sequence whose motif score p-value is less than the `--pvalue-threshold` (see below). | The `totalhits` scoring method is used.
--bgformat | 0|1|2 | Select the background source. 0 - uniform background 1 - MEME motif file 2 - Background file | The background specified in the MEME motif file is used.
--bgfile | bfile | The file should be in MEME background file format. | Use frequencies based on the motif file or files. See also --bgformat
--length-correction |  | Correct for length bias: subtract expected hits. | No length correction.
--pvalue-threshold | pvalue | Threshold to consider single motif hit significant; only used with `totalhits` scoring method. | A p-value threshold of 0.0002 is used.
--pvalue-report-threshold | pvalue | Corrected p-value threshold for reporting a motif as significantly enriched. | The threshold of 0.05 is used for reporting a motif.
--pwm-threshold | score | For the Fisher's exact test only. This is the minimum motif-based sequence score for a sequence to be a 'true positive'. | A minimum motif score of 1 is used to call a sequence a 'true positive'.
--fasta-threshold | p-value | For the Fisher's exact test only when `--poslist` is used. This is the maximum FASTA score to call a sequence a 'true positive'. | A maximum FASTA score of 0.001 is used to call a sequence a 'true positive'.
--fix-partition | num | Number of positive sequences; the remainder are the negative sequences. | Partition maximization is performed over the (sorted) input sequences. The partition with the lowest p-value is used.
--rsmethod | better|quick | Select how to compute the Wilcoxon rank-sum test: `better` - compute the proper test (slow) `quick` - use faster heuristic version of test | Use the heuristic version of the test (`quick`).
--poslist | pwm|fasta | For partition maximization, test thresholds on either X (motif) or Y (FASTA score). Only applies for partition maximisation and for the Ranksum test. `pwm` - Use motif score (X). `fasta` - Use FASTA score (Y). Hint: Be careful when switching the `poslist`. It switches between using X and Y for determining true positives in the contingency matrix, in addition to switching which of X and Y is used for partition maximization. | Use the FASTA score.
--log-fscores |  | For linear regression and Spearman tests only: regress using ln(FASTA score), rather than the score directly. | Use the score directly.
--log-pwmscores |  | For linear regression and Spearman tests only: regress using ln(pwm score), rather than the score directly. | Use the score directly.
--normalise-affinity |  | Normalise motif scores so that motif scores can be compared directly. Only relevant for Spearman and Linear Regression tests, where p-values are not calculated. |
--linreg-switchxy |  | Make the x-points FASTA scores and the y-points motif scores. Only relevant for Spearman and Linear Regression tests. | Keep the original axis.
--verbose | 1|2|3|4|5 | A number that regulates the verbosity level of the output information messages. If set to 1 (quiet) then it will only output error messages whereas the other extreme 5 (dump) outputs lots of mostly useless information. This option is best placed first. At verbosity level 3, AME will report the significance of each set of each partition of the sequences that it considers. | The verbosity level is set to 2 (normal).
--help |  | Print a usage message and exit. | Run as normal.

### Citing

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]