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Gapped BLAST and PSI-BLAST : a new generation of protein database search programs

Gapped BLAST and PSI-BLAST : a new generation of protein database search programs. Presented by 佘健生 鄭為正 李定達 曾文鴻. Outline. BLAST 1.0 BLAST 2.0 The two-hit method Gapped alignment PSI-BLAST Performance evaluation Discussion and Conclusion NCBI website. Statistical preliminaries.

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Gapped BLAST and PSI-BLAST : a new generation of protein database search programs

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  1. Gapped BLAST and PSI-BLAST:a new generation of protein database search programs Presented by 佘健生 鄭為正 李定達 曾文鴻

  2. Outline • BLAST 1.0 • BLAST 2.0 • The two-hit method • Gapped alignment • PSI-BLAST • Performance evaluation • Discussion and Conclusion • NCBI website

  3. Statistical preliminaries • HSP: High-scoring segment pair • Locally optimal pair • S’ = (λS - ㏑K) / ㏑2 • S’: normalized score • Pi : background probability that amino acids occur randomly at all position • sij: score for aligning each pair of amino acids I and j • K : minor constant • λ: constant to adjust for matrix • sij and Pi → K and λ

  4. E = N / 2S’ • E: number of distinct HSPs with normalized score at least S’ • N = mn is search space • S’ = log2(N/E) • qij = PiPjeλuSij • qij : target frequency of aligned pair of letters (i, j) with HSP, high-scoring segment paris • λu: the unique positive number

  5. BLAST • Basic Local Alignment Search Tool(by Altschul, Gish, Miller, Myers and Lipman) • The BLAST program are widely used tools for searching protein and DNA database for sequence similarities • BLAST is a heuristic that attempts to optimize a specific similarity measure. • The central idea of the BLAST algorithm is that a statistically significant alignment is likely to contain a high-scoring pair of aligned words.

  6. The maximal segment pair measure • MSP(maximal segment pair): the highest scoring pair of identical length segments chosen from 2 sequences • for DNA: Identities: +5; Mismatches: -4 • for protein: BLOSUM62 … • BLAST heuristically attempts to calculate the MSP score. • DP is O(mn) ,but BLAST is O(m) the highest scoring pair

  7. BLAST 1.0 • Build the hash table for Sequence A. • Scan Sequence B for hits. • Extend hits.

  8. For DNA : Seq. A = ACGTAGTA 12345678 AAA AAC .. ACG 1 .. AGT 5 .. CGT 2 .. GTA 3 6 .. TAG 4 .. TTT For protein : Seq. A = YGGFMAdd xyz to the hash table if Score(xyz, YGG) ≧ T;Add xyz to the hash table if Score(xyz, GGF) ≧ T;Add xyz to the hash table if Score(xyz, GFM) ≧ T; T: ‘threshold’ parameter High T yelds greater speed, but weak similarities Step 1: Build the hash table for Sequence A. (3-tuple example) Hash table



  11. G G F A A A 0 + 0 + -2 = -2 Non-match BLOSUM62 scores G G F G G Y 6 + 6 + 3 = 15 Match A user-specified threshold determines which three-letter words are considered matches and non-matches.


  13. Store words in search tree Augmented list of query words “Does this query contain GGF?” Search tree O(1) time “Yes, at position 2.”

  14. Search tree G G F L M W Y

  15. Scan the database x x x x Query sequence x x x x Database sequence

  16. Extend hit L P P Q G L L Query sequence M P P E G L L Database sequence <word> 7 2 6 BLOSUM62 scores word score = 15 <--- ---> 2 7 7 2 6 4 4 HSP SCORE = 32 hit Extend This is done by extending a hit in both directions, until the running alignment’s score has dropped more than Xbelow

  17. BLAST 2.0The two-hit method • BLAST 1.0 • Extension step typically accounts for >90% of BLAST’ execution time • Observations: • A HSP of interest is much longer than a single word pair • Entail multiple hits on the same diagonal and within short distance of one another • Invoke an extension only when two non-overlapping hits are found within distance A on the same diagonal

  18. Extend! < A > A • Recent[i]: the most recent hit found on the ith diagonal (always increasing) overlap

  19. T must to be lowered • one-hits : W=3 ,T=13 • Two-hit : W=3 ,T=11 • More one-hits while the majority are dismissed • Sensitivity • For HSPs with at least 33 bits, the two-hit heuristic is more sensitive • Speed(two-hit): • Generates on average ~3.2 times as many hit, but only ~0.14 times as many hit extension(decide whether a hit need be extended) • Twice as rapid as one-hit

  20. Gapped alignment • Original BLAST: find several distinct HSPs • All HSPs related to one alignment should be found • Gapped BLAST: tolerate a much higher chance of missing any single moderately scoring HSP • Seeking a single gapped alignment, rather than a collection of umgapped ones • For example, result should > 0.95, p: miss prob of HSP • Orignial with 2 HSP: (1-p)(1-p)>0.95 p<0.025 • Now: p2<0.05p=0.22 • T can be raised  faster • Now: • Find one HSP only– seed, than use 2-hit

  21. Gapped alignment (contd) • A gapped extension takes much longer to execute than an ungapped extension, but by performing very few of them the fraction of the total time could be kept low. • Trigger a gapped extension for any HSP exceeding score Sg • Sg should be set at ~22 bits (1:50)

  22. Original BLAST locates only the first and the last ungapped aligment, E-value > 50 times

  23. Gapped Local Alignments

  24. actaactattacagactaactattacagactaactataca actaactattacggactaacttacagactaactaaaca Before Gap Insertion actaactattacagactaactattacagactaactataca |||||||||||| |||||||| | | | | actaactattacggactaacttacagactaactaaaca Percent Identity = 24/40 = 0.6 After Gap Insertion actaactattacagactaactattacagactaactataca |||||||||||| |||||||| ||||||||||||| ||| actaactattacggactaact--tacagactaactaaaca Percent Identity = 36/40 = 0.9

  25. Gapped Local Alignments • Start from a single aligned pair of residues, called the seed.

  26. Gapped expansion • Find out ungapped region with highest alignment score. • If the length of the ungapped region larger than Sg, then try using DP • Use its central residue pair as the seed. • Gapped extension is invoked less than once per 50 database sequences.

  27. PSSM

  28. conserved regions • same protein family • some regions are very similar • the structure and functionality typical to this family

  29. PSI-BLAST(Position-Specific Iterated BLAST) [1] Select a query and search it against a protein database [2] PSI-BLAST constructs a multiple sequence alignment then creates a “profile” or specialized position-specific scoring matrix (PSSM) [3] The PSSM is used as a query against the database [4] PSI-BLAST estimates statistical significance (E values) [5] Repeat steps [3] and [4] iteratively, typically 5 times. At each new search, a new profile is used as the query. PSSM PSSM From:

  30. Score matrix architecture • Each matrix has length precisely equal to that of the original query sequence.

  31. Multiple alignment construction • E-value < 0.01 from the output of BLAST output. • Any row identical to the query segment with which it aligns is purged. • Only one copy is retained of any rows that are above 98% identical to one another.

  32. Multiple alignment construction • Pairwise alignment columns that involve gap characters inserted into the query are simply ignored. • So M has exactly the same length as the query.

  33. Multiple alignment construction • The matrix scores for a given alignment column should depand not only upon the residues appearing there. • The set R of sequences it includes to be exactly those that contribute a residue to column C. • The columns of MC to be just those columns of M in which all the sequences of R are represented.

  34. Sequence weights • A large set of closely related sequences carries little more information than a single member, but its size may allow it outvote a small number of more divergent sequences. • One way is to assign weights. • Gap characters are treated as a 21st distinct char.

  35. Sequence weights • In constructing matrix scores, not only a column’s observed residue frequencies are important. • Estimate the relative number NC of independent observations constituted by the alignment MC. • NC: the mean number of different residue types.

  36. a large number of independent sequences, the estimate of Qi should converge simply to the observed frequency of residue i in that column. • Pseudocount frequencies • Estimate Qi by:

  37. Performance Evaluation

  38. Gapped BLAST: 1. 3X faster than original BLAST, finds more 2. >100X faster than S-W, misses only 8, same scores PSI-BLAST: 1. faster than original BLAST, 40X faster than S-W, much more sensitive 2. multiple iterations is even better, better for non-redundant database of NCBI 3. slower than gapped BLAST: time for construction of PSSM

  39. PSI-BLAST Examples(1) 1. 二者已被證明結構相似,但用HIT當作query, a BLAST search of SWISS-PROT reveals hits with E<0.01 only to other HIT proteins. 2. A PSI-BLAST search, using PSSM generated by yields the E-value of 2X10-4for uridylyltransferase.

  40. PSI-BLAST Examples(2)BRCT proteins

  41. Seven recent additions to the protein databases as members of BRCT superfamily

  42. Discussion

  43. Possible future improvement Gap costs • Allows a gap to involve residues in both sequences rather than just one • A gap in which k residues are inserted or deleted and j pairs of residues are left unaligned receives the score –(a+bk+cj)

  44. Possible future improvementRealignment • 不將所有超過threshold的pairwise alignment組合成單一multiple alignment,而是只選出the most significant建構initial multiple alignment and PSSM,然後再以此rescore and realign database sequences that received lower scores • 優點 • Improve weaker pairwise alignments • False positive can be downgraded by an improved matrix • False negative can have their scores increased

  45. Conclusion • Gapped version of BLAST is faster than original one, and able to produce gapped alignments. • PSI-BLAST greatly increase sensitivity to weak but biologically relevant sequence relationships. • PSI-BLAST retains the ability to report accurate statistics, per iteration runs in times not much greater than gapped BLAST, and can be used both iteratively and fully automatically.

  46. NCBI • Books • Pudmed • Blast (1)Nucleotide -- Quickly search for highly similar sequences -- Nucleotide-nucleotide BLAST (2)Protein -- Protein-protein BLAST (3)Translated -- Translated query vs. Protein database (4)Special -- Align two sequences

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