# 18.3: Predicting Regular Targets

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## Motif Instance Identification

Once potential motifs are discovered, the next step is to discover which motif matches are real. This can be done by both experimental and computational methods.

• Experimental - Instances can be identified experimentally using ChIP-Chip and ChIP-Deq methods. Both of these are in vivo methods. This is done by cross linking cells. DNA is first broken into sections. Then the protein and its antibody or tagged protein is added, which binds to various sequences. These bound sequences are now pulled out and cross linking is reversed. This allows us to determine where in the genome the factor was bound. This has a high false positive rate because there are many instances where a factor binds, but is not functional. This is a very popular experimental methods, but it is limited by the availability of antibodies, which are difficult to get for many factors.
• Computational- Computation approaches. There are also many computational approaches to identify instances. Single genome approaches use motif clustering. They look for many matches to increase power and are able to find regulatory regions (CRMs). However, they miss instances of motifs that occur alone and require a set of specific factors that act together. Multi-genome approaches, known as phylogentic footprinting, face many challenges. They begin by aligning many sequences, but even in functional motifs, sequences can move, mutate, or be missing. The approach taken by Kheradpour handles this by not requiring perfect conservation (by using a branch length score) and by not requiring an exact alignment (by searching within a window).

Branch Length Scores (BLS) are computed by taking a motif match and searching for it in other species. Then, the smallest subtree containing all species with a motif match is found. The percentage of total tree is the BLS. Calculating the BLS in this way allows for mutations permitted by motif degeneracy, misalighment and movement within a window, and missing motifs in dense species trees.

This BLS is then translated into a confidence score. This enables us to evaluate the likelihood of a given score and to account for differences in motif composition and length. We calculate this confidence score by counting all motif instances and control motifs at each BLS. We then want to see which fraction of the motif instances seem to be real. The confidence score is then signal/(signal+noise). The control motifs used in this calculation are produced by producing 100 shuffles of the original motif, and filtering the results by requiring that they match the genome with +/- 20% of the original motif. These are then sorted based on their similarity to known motifs and clustered. At most one motif is taken from each cluster, in increasing order of similarity, to produce our control motifs.

## Validating Targets

Similar to motif discovery, we can validate targets by seeing where they fall in the genome. Confidence selects for TF motif instances in promoters and miRNA motifs in 3’ UTRs, which is what we expect. TFs can occur on either strand, whereas miRNA must fall on only one strand. Thus, although there is no preference for TFs, miRNA are found preferentially on the plus strand.

Another method of validating targets is by computing enrichments. This requires having a background and foreground set of regions. These could be a promoter of co-regulated genes vs all genes or regions bound by a factor vs other intergenic regions. Enrichment is computed by taking the fraction of motif instances inside the foreground vs the fraction of bases in the foreground. Composition and conservation level are corrected for with control motifs. These fractions can be made more conservative using a binomial confidence interval.

Targets can then be validated by comparing to experimental instances found using ChIP-Seq. This shows the conserved CTCF motif instances are highly enriched in ChIP-Seq sites. Increasing confidence also increases enrichment. Using this, many motif instances are verified. ChIP-Seq does not always find functional motifs, so these results can further be verified by comparing to conserved bound regions. This finds that enrichment in intersections is dramatically higher. This shows where factors are binding that have an effect worthwhile conserving in evolution. These two approaches are complementary and are even more effective when used together.

This page titled 18.3: Predicting Regular Targets is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Manolis Kellis et al. (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.