1. CS5263 Bioinformatics Lecture 20 Practical issues in motif finding Final project

2. Homework problem 3.1 Count separately the number of character comparisons and the number of steps needed to find the next matching character using the bad character rule Question: can you give an example?

3. Extended bad character rule charPosition in P a 6, 3 b7, 4 p2 t1 x5 T: xpbctbxabpqqaabpqz P: tpabxab *^^ P: tpabxab Find T(k) in P that is immediately left to i, shift P to align T(k) with that position k In this iteration: # of comparison = 3 Table lookup: 2

4. Results for some real genes llr = 394 E-value = 2.0e-023 llr = 347 E-value = 9.8e-002 llr = 110 E-value = 1.4e+004

5. Strategies to improve results Combine results from different algorithms usually helpful –Ones that appeared multiple times are probably more interesting Except simple repeats like AAAAA or ATATATATA –Cluster motifs into groups according to their similarities

6. Strategies to improve results Compare with known motifs in database –TRANSFAC –JASPAR Issues: –How to determine similarity between motifs? Alignment between matrices –How similar is similar? Empirically determine some threshold

7. Strategies to improve results Statistical test of significance –Enrichment in target sequences vs background sequences Target set T Background set B Assumed to contain a common motif, P Assumed to not contain P, or with very low frequency Ideal case: every sequence in T has P, no sequence in B has P

8. Statistical test for significance Intuitively: if n / N >> m / M –P is enriched (over-represented) in T –Statistical significance? Target set T Background set + target set B + T Size = N Size = M P Appear in n sequences Appear in m sequences

9. Hypergeometric distribution A box with M balls, of which N are red, and the rest are blue. –Red ball: target sequences –Blue ball: background sequences If we randomly draw m balls from the box, what’s the probability we’ll see n red balls? –If probability very small, we are probably not drawing randomly Total # of choices: (M choose m) # of choices to have n red balls: (N choose n) x (M-N choose m-n)

10. Cumulative hypergeometric test for motif significance We are interested in: if we randomly pick m balls, how likely that we’ll see at least n red balls? This can be interpreted as the p-value for the null hypothesis that we are randomly picking. Alternative hypothesis: our selection favors red balls. Equivalent: the target set T is enriched with motif P. Or: P is over-represented in T.

11. Examples Yeast genome has 6000 genes Select 50 genes believed to be co-regulated by a common TF Found a motif for these 50 genes It appeared in 20 out of these 50 genes In the whole genome, 100 genes have this motif M = 6000, N = 50, m = 100+20 = 120, n = 20 Intuition: –m/M = 120/6000. In Genome, 1 out 50 genes have the motif –N = 50, would expect only 1 gene in the target set to have the motif –20-fold enrichment P-value = 6 x 10 -22 n = 5. 5-fold enrichment. P-value = 0.003 Normally a very low p-value is needed, e.g. 10 -10

13. ROC curve for motif significance Motif is usually a PWM Any word will have a score –Typical scoring function: Log P(W | M) / P(W | B) –W: a word. –M: a PWM. –B: background model To determine whether a sequence contains a motif, a cutoff has to be decided –With different cutoffs, you get different number of genes with the motif –Hyper-geometric test first assumes a cutoff –It may be better to look at a range of cutoffs

14. ROC curve for motif significance With different score cutoff, will have different m and n Assume you want to use P to classify T and B Sensitivity: n / N Specificity: (M-N-m+n) / (M-N) False Positive Rate = 1 – specificity: (m – n) / (M-N) With decreasing cutoff, sensitivity , FPR  Target set T Background set + target set B + T Size = N Size = M P Appeared in n sequences Appeared in m sequences Given a score cutoff

15. ROC curve for motif significance ROC-AUC: area under curve. 1: perfect separation. 0.5: random. Motif 1 is better than motif 2. 1-specificity sensitivity Motif 1 Motif 2 Random A good cutoff Highest cutoff. No motif can pass the cutoff. Sensitivity = 0. specificity = 1. Lowest cutoff. Every sequence has the motif. Sensitivity = 1. specificity = 0. 0 1 10

16. Other strategies Cross-validation –Randomly divide sequences into 10 sets, hold 1 set for test. –Do motif finding on 9 sets. Does the motif also appear in the testing set? Phylogenetic conservation information –Does a motif also appears in the homologous genes of another species? –Strongest evidence –However, will not be able to find species-specific ones

17. Other strategies Finding motif modules –Will two motifs always appear in the same gene? Location preference –Some motifs appear to be in certain location E.g., within 50-150bp upstream to transcription start –If a detect motif has strong positional bias, may be a sign of its function Evidence from other types of data sources –Do the genes having the motif always have similar activities (gene expression levels) across different conditions? –Interact with the same set of proteins? –Similar functions? –etc.

18. To search for new instances Usually many false positives Score cutoff is critical Can estimate a score cutoff from the “true” binding sites Motif finding Scoring function A set of scores for the “true” sites. Take mean - std as a cutoff. (or a cutoff such that the majority of “true” sites can be predicted).

19. To search for new instances Use other information, such as positional biases of motifs to restrict the regions that a motif may appear Use gene expression data to help: the genes having the true motif should have similar activities Phylogenetic conservation is the key

20. Final project Write a review paper on a topic that we didn’t cover in lectures Or Implement an algorithm and do some experiments Compare several algorithms (existing implementation ok) Combine several algorithms to form a pipeline (e.g. gene expression + motif analysis) Final: –5 -10 pages report (single space, single column, 12pt) + 15 minutes presentation

21. Possible topics for term paper Possible topics: –Haplotype inferencing –Computational challenges associated with new microarray technologies –Phylogenetic footprinting –Small RNA gene / target prediction (siRNA, mRNA, …) –Biomedical text mining –Protein structure prediction –Topology of biological networks

22. An example project Given a gene expression data (say cell cycle) Cluster genes using k-means Find motifs using several algorithms (Cluster and combine similar motifs) Rank motifs according to their specificity to the target sequences comparing to the other clusters Get their logos Use the sequences to search the whole genome for more genes with the motif Do they have any functional significance?