CentriMo Tutorial - MEME Suite

# The MEME Suite

## Motif-based sequence analysis tools

## CentriMo Tutorial

### What CentriMo does

### Typical CentriMo applications

- Determining which motif(s) are enriched in a set of transcription factor ChIP-seq peaks.
- Determining which motif(s) are enriched in one set of transcription factor (TF) ChIP-seq peaks compared with another set of peaks for the same TF, perhaps in a difference cell type or after some treatment.
- Determining which motif(s) are enriched in a set of promoters of co-regulated genes.
- Determining which motif(s) are relatively enriched in one set of promoters compared to another set of promoters.
- Note: In all cases, the sequences input to CentriMo should be the same length, and aligned on some landmark such as a ChIP-seq peak or a TSS. If your sequences need to be of different lengths, or if there is no obvious landmark to align them on, AME may be a more appropriate tool to use.

### How CentriMo works

Note the following refers to the sequence set in which you are finding motifs as the primary sequences and to the comparative sequence set as the control sequences. In all cases, an E-value refers to a p-value corrected for multiple tests by multiplying by the number of motifs in all the databases used.

CentriMo can perform either central or local motif enrichment analysis. Central enrichment is determined by finding the highest likelihood ('best') site for the motif in each sequence in a given set. Central enrichment is quantified by a binomial p-value based on the a uniform distribution of best sites within or outside a central bin. CentriMo searches for the bin width that with the maximum significance. The same procedure is used for local motif enrichment analysis, except the center of the optimal bin may be anywhere along the sequences, not just aligned to their centers. CentriMo reports the position, width and significance of the most enriched bin, and it plots the distribution of 'best' sites. The algorithm proceeds as follows:

1. For each motif, scan each sequence and record the 'best' site; in the event of k ties for the 'best' score in a sequence, k > 1, count each tie as 1/k sites rather than 1.
2. For each bin size (increased in steps of 2 to maintain symmetry), calculate the binomial p-value
.
3. Select the bin size with lowest p-value, and plot the site probability for the distribution of location of 'best' sites.
4. If control sequences are provided, repeat the motif scan for the control sequences and count how many 'best' sites fall in the optimal bin determined from the primary sequences. Then compute the E-value for a Fisher's exact test comparing the counts in that bin in the primary and control sequences.

### Sequence and Motif Alphabets

CentriMo supports DNA, RNA, protein and custom sequence alphabets. The alphabet is specified in the motif file, and the sequences being searched must be compatible with that alphabet. (DNA motifs can be used to search RNA sequences and vice-versa.) If the motif alphabet is "complementable" (e.g., DNA-like alphabets), You can also override the the alphabet specified in the motif file with an alphabet that contains all the core symbols specified in the motif alphabet but which may contain additional core symbols. The motifs will be expanded to match this new alphabet with 0's filling in the probabilities for the new symbols (prior to applying pseudocounts).

### Primary Sequence set

These sequences should all be in the same sequence alphabet and be of the same length. CentriMo will ignore all sequences whose lengths don't match the first sequence. They can, for example, a set of ChIP-seq regions aligned on the summits or centers of the peaks, or be a set of promoters thought to be co-regulated aligned on the TSS. By default, CentriMo detects motifs enriched in the centers of these sequences, relative to the flanks. CentriMo can also search for motifs aligned locally (non-centrally) within the primary sequences.

CentriMo requires sequences derived using a method that causes motif sites to be distributed with a bias to be near (or at a fixed distance) from some landmark. Sequences should have sufficient flanking data around the likely binding site to achieve statistical significance. For ChIP-seq sequences centered on the peak summit (or peak center), a sequence length of 500 works well.

### Control sequence set

CentriMo can perform differential local motif enrichment if you provide a control set. These sequences should be the same length as the primary sequences, and aligned on the same type of landmark. After determining if a motif is enriched in the primary sequences, CentriMo will then test the relative enrichment of the motif in the same region in the control sequences. CentriMo reports two p-values for all the motifs that are enriched in the primary sequences—their primary enrichment and their relative enrichment.

A typical use case is comparing different ChIP-seq cell lines that may differ in one or more variables (e.g., karotype, development stage, tissue type). It is best to run this sort of study twice as a reality check, reversing the roles of the positive and negative set, since it is possible that statistical significance arising from the comparison of the positive and negative sets could be an artifact of the two sequence sets achieving their highest central enrichment with different central bin widths.

### Motif file(s)

You must specify one or more files containing motifs MEME Motif Format. CentriMo is fast enough to use a large numbers of motifs for general motif enrichment studies. You can achieve higher statistical power by using a more specific set of motifs if you have prior knowledge of what motifs might be enriched in your sequences.

## Interpreting Results

The primary basis for determining central (local) enrichment is the central (local) enrichment E-value. It is also useful to examine the shape of the site probability plot and the width of the bin with lowest E-value.

Note, however, that it is dangerous to directly compare the E-values of two motifs unless the motifs have approximately the same information content. This is because motifs with higher information content are more likely to accurately pin-point the actual binding site in a ChIP-seq experiment, for example. So a more specific version of the "true" binding motif can receive a lower E-value, even though it is not a "better" motif.

Note also that CentriMo is extremely sensitive, and when your sequence set is large, even motifs that are only slightly similar to the actual motif can show significant enrichment.

With ChIP-seq peaks, the width of the bin with lowest E-value is also an indication of a tendency to central enrichment; typically a best bin width of about 100 bases indicates direct binding, especially if the E-value is low and the plot has a clear peak.

With ChIP-seq peaks, if the maximum of the plot is off-center, that may indicate an offset inherent in the peak-calling method used in the ChIP-seq experiment. If the primary binding TF's motif is sharply centered, and other motifs have less clearly centered maxima, that may indicate binding partners. If there is no clear central enrichment, that may indicate a failure of the ChIP-seq method, the absence of a motif in the motif file(s) used that accurately represents binding, or indirect binding of the TF via some motif not contained in the motif file(s).

The shape of the graph and the best bin width are subjective, so we prefer to use the E-value as a discriminator between different degrees of central (local) enrichment.

### FAQ

Under construction...