Pairwise Local Alignment and Database Search

1. Pairwise Local Alignment and Database Search Csc 487/687 Computing for Bioinformatics

2. Which Program should one use? • Most researchers use methods for determining local similarities: • Smith-Waterman (gold standard) • FASTA • BLAST } Do not find every possible alignment of query with database sequence. These are used because they run faster than S-W

3. Heuristic Database Search Methods • Smith-Waterman dynamic programming too computer and time intensive for searching big databases • e.g., UniProt July 2004 – 1.5M sequences • Most popular: BLASTx (Altschul et al 1990, 1997) and FASTx (Lipman and Pearson 1985)

4. BLAST – Basic Local Alignment Search Tool • Basic idea: • Identify short very similar segment pairs – extend local alignment • Critical issues: • For every database sequence d significantly similar to q, one should find at least one segment pair • Fewer segment pairs means faster computation

5. Definitions • Maximal Segment Pair (MSPqd) – pair of identical length segments having the highest score of all ungapped local alignments between q and d. • High-Scoring Segment Pair (HSP) – segment pair for which the score cannot be increased by shortening or extension • Word – segment of fixed length w • Word pair – pair of segments of length w

6. Reformulating the Problem • Identify those database sequences d such that MSPqd is over a threshold V. • A segment pair scoring at least V has with a high probability a word pair scoring at least T. • Identify word pairs with score at least T, extend to high-scoring segment pairs – check if score over V

7. Finding Hits and HSPs • Hit – word pair scoring at least T • Preprocess q • Find all words oT(length w) that can score at least T against a word in q • Save in easy-to-use data structure • Find the hits • Search in d for all occurrences (od) of the words oT • Extend (heuristically) to high-scoring segment pairs • Perform dynamic programming around HSPs scoring over a certain threshold – allows introduction of gaps

8. Pre-processing q • Aim: • Allow rapid identification of all words oT in d – and the location of corresponding words in q to allow extension into HSPs • Possibility: table of 20w entries

9. Pre-processing q

10. Finding HSPs • For each word in d (starting in position j) hitting a word in q (starting in position i), record the hit indexed by its diagonal (j-i ). • Hits close together on the same diagonal are joined before extension to HSPs • Extending to HSP: • Ideally – move to the end of the sequences in both directions • Heuristic – if score falls “far below” best seen so far, stop extension

11. Dynamic Programming Around HSPs • DP is time consuming and need to be constrained • Starting from identified HSP, find ”seed pair” • Perform ”forward” and ”backward” DP from seed pair (independently) • Stop DP if score falls T below best score S’ seen so far

12. Significance of alignments • Suppose alignment reveals an intriguing similarity between two sequences. • Is the similarity significant ? • Or could it have arisen by chance?

13. Significance of alignment • If the score of the alignment observed is no better than might be expected from a random permutation of the sequence, then it is likely to have arisen by chance.

14. How to Generate the Random Sequences? • Global alignment • Randomize one of the sequences, many times, realign each result to the second sequence (fixed), and collect the distribution of resulting scores. • Local alignment • Uses the population of results returned from the entire database as the population with which to measure the statistics.

15. Statistical parameters • Z-score • A measure of how unusual our original match is A z-score of 0 means the observed similarity is no better than the average of the control population. The higher the Z-score, the greater the probability. Z-score  5

16. Statistical parameters • P = the probability that the alignment is better than random • P ≤ 10-100 exact match • P in range 10-100 - 10-50 sequences very nearly identical • P in range 10-50 - 10-10 closely-related sequences, homology certain • P in range 10-5 - 10-1 distant relatives, usually • P > 10-1 match probably insignificant

17. Statistical parameters • E-value • The expected number of sequences that give the same Z-score or better if the database is probed with a random sequence. • found by multiplying the value of P by the size of the database probed. • Note that E but not P depends on the size of the database.

18. Statistical parameters • Interpreting E values • E ≤ 0.02 sequences probably homologous • E between 0.02 and 1 homologous cannot be ruled out • E > 1 you’d have to expect this good a match just by chance

19. Rules and thinking.. • Percent of identical residues in the optimal alignment • over 45%, very similar structures, common or at least a related function. • Over 25%, a similar general folding pattern. • A lower degree of sequence similarity cannot rule out homology

20. Rules and thinking.. • 18%-25% twilight zone, the suggestion of homology is tantalizing but dangerous • Absence of significant similarity does not imply that the sequences are not homologous – could be distantly related (twilight zone or beyond)