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Heuristic Approaches for Sequence Alignments

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Presentation on theme: "Heuristic Approaches for Sequence Alignments"— Presentation transcript:

1 Heuristic Approaches for Sequence Alignments

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

10 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? Answer: This ratio = Where Pb is the probability of a random occurrence of b. Mab Pb /course/eleg 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/eleg 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/eleg 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/eleg 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/eleg f/Topic-2b.ppt

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

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

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

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

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

21 BLAST (Basic Local Alignment Search Tool)
A list of high-scoring “segment pairs” between the query and database sequences with scores above a certain threshold Query sequence BLAST database 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, ] /course/eleg f/Topic-2b.ppt

22 Maximum segment pair (MSP) – is a segment pair of maximum score.
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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/eleg 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/eleg 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 find the list of words with score > T w4 w5 = 6 < T . C R Y wk = 28 > T word list PAM matrices can be used to compute the scores /course/eleg 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/eleg f/Topic-2b.ppt

27 BLAST- Search for hits, each hit gives a seed
Lookup (hash) table: w F(w) Database sequence 1 2 3 4 5 6 7 8 w5 w1 w2 w4 w3 w6 w8 A: 00 C: 01 G: 10 T: 11 Byte A C G T DNA sequences w7 word list /course/eleg 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> LETTER WORD FOUND INITIALLY word score = 14 < > EXTENSION EXTENSION TO LEFT TO RIGHT < MAXIMAL SEGMENT PAIR > SCORE = 31 Maximum Segment Pairs (MSPs) For each exact word match, alignment is extended in both directions to find high score segments /course/eleg f/Topic-2b.ppt

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

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

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

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

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