Pairwise Sequence Alignment Part 2

1. Pairwise Sequence Alignment Part 2

2. Outline • Global alignments-continuation • Local versus Global • BLAST algorithms • Evaluating significance of alignments

3. Global Alignment -Cont

4. Needleman-Wunsch Alignment • Global alignment between sequences • Compare entire sequence against another • Create scoring table • Sequence A across top, B down left • Cell at column i and row j contains the score of best alignment between the first i elements of A and the first j elements of B • Global alignment score is bottom right cell

6. A -

7. ACGCTG ------

8. ----- CATGT

9. A C

10. AC -C

11. ACG -C-

12. ACGC ---C ACGC -C--

13. ACG -CA

17. ACGCTG- -C-ATGT

18. ACGCTG- -CA-TGT

19. -ACGCTG CATG-T-

20. Global Alignment versus Local Alignment Global Alignment ATTGCAGTG-TCGAGCGTCAGGCT ATTGCGTCGATCGCAC-GCACGCT Local Alignment CATATTGCAGTGGTCCCGCGTCAGGCT TAAATTGCGT-GGTCGCACTGCACGCT

21. Global vs. Local alignment DOROTHY DOROTHY HODGKIN HODGKIN Global alignment: DOROTHY--------HODGKIN DOROTHYCROWFOOTHODGKIN Local alignment:

22. Local Alignment • Best score for aligning part of sequences • Often beats global alignment score • Similar algorithm: Smith-Waterman • Table cells never score below zero

23. TAA TAA TACTA TAATA

24. Problems with DP for sequence alignments -The complexity is very high - Given a score, how to evaluate the significance of the alignment?

25. Complexity • Complexity is determined by size of table • Aligning a sequence of lengthmagainst one of lengthnrequires calculating(mn)cells • Time of calculation Lets say we calculate 108 cells per second on a one processor PC • Aligning two mRNA sequences of8,000 bprequires64,000,000 cells 0.64 seconds • Aligning an mRNA and a107 bpchromosome requires~1011 cells 1,000 secs =15 minutes

26. Complexity for large databases • Let’s say a database contains3  1010base pairs • Searching an mRNA against the database will require ~2.5  1014 cells 2.5  106 secs =1 month! • We need an efficient algorithm to cut down on alignment

27. BLAST • Basic Local Alignment Search Technique • A set of tools developed at NCBI (BlastN, BlastP,..) • BLAST benefits • Search speed • Ease of use • Statistical rigor

28. BLAST • A good alignment contains subsequences of absolute identity: • First, identify very short (almost) exact matches. • Next, the best short hits from the 1st step are extended to longer regions of similarity. • Finally, the best hits are optimized using the Smith-Waterman algorithm.

29. BLAST Algorithm (1) Query sequence Words of length W W default = 11 • Compare the word list to the database • and identify exact matches

30. For each word match, extend alignment in both • directions (4) Score the alignments using Dynamic Programing (5) Evaluate the statistics significance

31. Random Related Database Searches • Using the pairwise comparison, each database search normally yields 2 groups of scores: genuinely related and unrelated sequences, with some overlap between them. • A good search method should completely separate between the 2 score groups.

32. E-value • The number of hits (with the same similarity score) one can "expect" to see just by chance when searching the given string in a database of a particular size. • higher e-value lower similarity • “sequences with E-value of less than 0.01 are almost always found to be homologous” • The lower bound is normally 0 (we want to find the best)

33. Expectation Values Increases linearly with length of query sequence Decreases exponentially with score of alignment Increases linearly with length of database