1 / 37

Heuristic Approaches for Sequence Alignments

Heuristic Approaches for Sequence Alignments. Outline. Sequence Alignment Database Search FASTA BLAST. Sequence Alignment. Dynamic Programming (give optimal solution(s)) Needleman-Wunsch (Global Alignment) Smith-Waterman (Local Alignment) Heuristics (give approximate solution(s))

milla
Download Presentation

Heuristic Approaches for Sequence Alignments

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Heuristic Approaches forSequence Alignments

  2. Outline • Sequence Alignment • Database Search • FASTA • BLAST /course/eleg667-01-f/Topic-2b.ppt

  3. Sequence Alignment Dynamic Programming (give optimal solution(s)) • Needleman-Wunsch (Global Alignment) • Smith-Waterman (Local Alignment) Heuristics (give approximate solution(s)) Trade speed for precision (good for DB search) • FASTA (finds local alignments) • BLAST (Basic Local Alignment Search Tool) /course/eleg667-01-f/Topic-2b.ppt

  4. Database Search • One of the major uses of alignments is to find similar sequences in a database, i.e. compare one input sequence with all sequences in the database and obtain the most similar ones; • Current databases contain massive number of sequences; • Finding homologies in these databases optimally with dynamic programming can take long. /course/eleg667-01-f/Topic-2b.ppt

  5. Database Search using Heuristic Sequence Comparison Algorithms • Most database search algorithms relay on heuristic procedures • These are not guaranteed to find the best match • Sometimes, they will completely miss a high-scoring match /course/eleg667-01-f/Topic-2b.ppt

  6. Database Search and PAM Matrices - Motivation • Simple scoring scheme (e.g. +1 for match, 0 for mismatch, -1 for mismatch) is not enough, especially for protein sequences • Amino Acids: must consider their relative replacement features in an evolutionary scenario /course/eleg667-01-f/Topic-2b.ppt

  7. (Cont’d) • Factors affecting such mutual substitution are numerous (size, chemical properties, etc.) • PAM (Point Accepted Mutations) matrices are widely used – they are derived by direct observation of actual substitution rates. /course/eleg667-01-f/Topic-2b.ppt

  8. PAM Matrices (Contn’d) • 1-PAM Matrix: reflect an amount of evolution producing on average one mutation per hundred amino acids • How to build a 1-PAM matrices? • A probability transition matrix M: each entry Mab denotes the probability of a changing into b • A scoring matrix S • S is derived from M /course/eleg667-01-f/Topic-2b.ppt

  9. How to Build a Probability Transition Matrix M? • We need: • A list of accepted mutations • The probability of occurrence Pa for each amino acid a • M1 (M for 1-PAM) can be computed by simple probability arguments • Mk (M for K-PAM) = M1k /course/eleg667-01-f/Topic-2b.ppt

  10. Answer: This ratio = Where Pb is the probability of a random occurrence of b. Mab Pb How to Derive S from M? Question: Assuming pairing an amino acid a with b what is the probability (called a likelihood ratio) this pair is a mutation, not a random occurrence? /course/eleg667-01-f/Topic-2b.ppt

  11. How to Pick Up a PAM Matrix to Use • Use default one – but should know what it is • Select several to cover a wide range if little is known for the sequences • In general low PAM numbers are good for finding local, strong similarities, while large PAM numbers good for detecting long, weak ones. /course/eleg667-01-f/Topic-2b.ppt

  12. A Note on FAST Algorithms • Fast is a family of algorithm, e.g. FASTP, FASTA, TFASTA, LFASTA, ... • In this lecture we use FAST or FASTA interchangeably • References:[Pearson90,91, PearsonLipman88, etc.] /course/eleg667-01-f/Topic-2b.ppt

  13. FASTA (Pearson and Lipman, 1988) • Determine k-tuples (exact matches) common to both sequences (with two parameters: ktup and offset). • Join k-tuples that are in the same diagonal and not very far apart – creates regions; • Find region with best score – “initial score” to rank the sequences; • Compute an “optimized score”, using DP, restricted to a band around the region. /course/eleg667-01-f/Topic-2b.ppt

  14. Parameters ktup and offset • ktup (k = 1, 2) specify the length of a common segment • offset determines a relative displacement between the query sequence and a database sequence (hint: under a DP method, an offset can be viewed as a diagnal in the similarity matrix) /course/eleg667-01-f/Topic-2b.ppt

  15. 1 2 3 4 5 6 7 8 9 10 11 query sequence H A R F Y A A Q I V L 1 2 3 4 5 6 7 8 Database sequence V D M A A Q I A +9 offsets -2 +2 +3 -3 +1 +2 +2 +2 -6 -2 -1 -7 -6 -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 1 1 1 4 1 1 2 1 Offset vector FASTA - Determine k-tuples Ktup = 1 lookup table A 2, 6, 7 F 4 H 1 I 9 L 11 Q 8 R 3 V 10 Y 5 /course/eleg667-01-f/Topic-2b.ppt

  16. 0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 -1 -2 -3 -4 -5 -6 -7 H A R F Y A A Q I V L V D M A A Q I A -7 -6 -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 1 1 1 4 1 1 2 1 Offset vector FASTA – Diagonal method Database sequence 1 2 3 4 5 6 7 8 V D M A A Q I A +9 -2 +2 +3 -3 +1 +2 offsets +2 +2 -6 -2 -1 /course/eleg667-01-f/Topic-2b.ppt

  17. Join k-tuples that are in the same diagonal and not very far apart – creates regions; Typically ktup=1 or 2 for proteins and ktup=4 or 6 for DNA sequence The larger ktup, the faster the program FASTA - Join k-tuples Determine k-tuples (exact matches) common to both sequences; Note: region should be gapless, and is created by certain heuristic /course/eleg667-01-f/Topic-2b.ppt

  18. Compute an “optimized score”, using DP, restricted to a band around the region. FASTA - Compute an optimized score for highest score region Find region with best score – “initial score”; /course/eleg667-01-f/Topic-2b.ppt

  19. Some Issues of FAST Algorithms • Selectivity vs. Sensitivity • Ktup selectivity • Ktup sensitivity • Statistical significance of the scores /course/eleg667-01-f/Topic-2b.ppt

  20. BLAST (Altschul et al, 1990) • Compile list of high-scoring words based on the query sequence; • Scanning the database to search for hits – each hit gives a seed; • Extend seeds for each sequence; • Report high scoring segments /course/eleg667-01-f/Topic-2b.ppt

  21. BLAST (Basic Local Alignment Search Tool) • Segment: a substring of a sequence • Segment pair: a pair of segments with the same length • Segment pairs are gapless local alignments [S.F.Altschul, W.Gish, W.Miller, E.Myers and D.Lipman: Basic Local Alignment Search Tool, J. Mol. Biology, (1990) 215, 403-410] A list of high-scoring “segment pairs” between the query and database sequences with scores above a certain threshold Query sequence BLAST database /course/eleg667-01-f/Topic-2b.ppt

  22. Maximum segment pair (MSP) – is a segment pair of maximum score. /course/eleg667-01-f/Topic-2b.ppt

  23. A segment pair is locally optimal if its score cannot be improved by either extending or shortening both segments. Note: Local similarity is useful for finding conserved regions (e.g. in a protein) /course/eleg667-01-f/Topic-2b.ppt

  24. BLAST is interested in finding only those sequences with MSP scores over some cutoff score S. • The main strategy of BLAST is to seek only segment pairs that contain a word pair with a score of at least T. /course/eleg667-01-f/Topic-2b.ppt

  25. BLAST- Compile list of high-scoring words w, T – program parameters N Query sequence w Maximum of N-w+1 words Typically w=3 for proteins and w=11 for DNA sequence . . . w1 Example: w = 3, T = 15 w2 w3 A N S A N S find the list of words with score > T w4 w5 2 2 2 = 6 < T . . . C R Y C R Y wk 12 6 10= 28 > T word list PAM matrices can be used to compute the scores /course/eleg667-01-f/Topic-2b.ppt

  26. BLAST- Search for hits, each hit gives a seed seeds Database sequences Exact matches of words from the word list to the database sequence /course/eleg667-01-f/Topic-2b.ppt

  27. w w7 w5 w1 w2 w4 w3 w6 w8 F(w) DNA sequences A C G T A: 00 C: 01 G: 10 T: 11 0 0 0 1 1 0 1 1 Byte BLAST- Search for hits, each hit gives a seed Lookup (hash) table: Database sequence 1 2 3 4 5 6 7 8 word list /course/eleg667-01-f/Topic-2b.ppt

  28. BLAST- Extend seeds for each sequence L P S L D L L QUERY SEQUENCE M P S L D L L DATABASE SEQUENCE < WORD> 3-LETTER WORD FOUND INITIALLY 4 4 6 word score = 14 <------- -------> EXTENSION EXTENSION TO LEFT TO RIGHT 2 7 4 4 6 4 4 < MAXIMAL SEGMENT PAIR > SCORE 14 + 9 + 8 = 31 Maximum Segment Pairs (MSPs) For each exact word match, alignment is extended in both directions to find high score segments /course/eleg667-01-f/Topic-2b.ppt

  29. BLAST- report high scoring segments Choose high score segments: scores > S /course/eleg667-01-f/Topic-2b.ppt

  30. Why BLAST is Fast? Because: the alignments are gapless! /course/eleg667-01-f/Topic-2b.ppt

  31. Statistical Significance of BLAST Results Question: If a match found by BLAST – what is the probability that such match is due to chance alone? A well-funded statistical theory is used by BLAST in determine the matching scores. /course/eleg667-01-f/Topic-2b.ppt

  32. Questions Q1: What proportion of segment pairs with a given score contain a word pair with a score at least T? Answer: [Karlin91] Q2: What probability q of a MSP pair found (under a threshold score S) will fail to contain a seed word W (of score >= T)? Answer: See Plot [Alschul et.al.90] /course/eleg667-01-f/Topic-2b.ppt

  33. - ln q Score S Note: PIM-120 scores are used, w=4 and T=8 /course/eleg667-01-f/Topic-2b.ppt

  34. Improvement of The Basic BLAST-Gapped BLAST and PSI-BLAST [S.F. Altschul, et.al., Gapped BLAST and PSI-BLAST: A New Generation of Protein Database Search Algorithms, Nucleic Acids Research, 1997, Vol25, No. 17, 3389-3402] • Objectives • Speedup the execution substantially • Enhance the sensitivity to weak similarities /course/eleg667-01-f/Topic-2b.ppt

  35. Major Extensions/Changes to BLAST • Add ability to generate gapped alignment using dynamic programming to extend a seed in both directions • Using a “two-hit” method to “filter” out the candidate pairs for extension • The search may be iterated: round i will generate a new position-specific score matrix from significant alignments found to be used for round i+1 (this process involves the construction of a multiple sequence alignment – see Topic 2C) /course/eleg667-01-f/Topic-2b.ppt

  36. The Two-Hit Method • Observation: an HSP of interest is much longer than a single word pair, thus may contain multiple hits on the same diagonal within a relatively short distance apart. • Methods: Choose a “window” , and do extension only when two non-overlapping hits are found within distance A of one another on the same diagonal • Effectiveness: reduce candidate pairs for extension substantially (by 86%) /course/eleg667-01-f/Topic-2b.ppt

  37. An Example The BLAST comparison of broad bean leghemoglobin I (87) (SSWISS-PROT accession no.PO2232) and horse beta -globin (88) (SWISS_PROT accession no.P02062). The 15 hits with score at least 13 are indicated by plus signs. An additional 22 non-overlaping hits with score at least 11 are indicated by dots. Of these 37 hits, only the two indicated pairs are on the same diagonal and within distance 40 of one another. Thus the two-hit heuristic with T=11 triggers two extensions, in place of the 15 extensions invoked by the one-hit heuristic with T=13. /course/eleg667-01-f/Topic-2b.ppt

More Related