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huristic approach

blast and fasta

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- 1. Sequence comparison technique<br />Ms.ruchiyadavlectureramity institute of biotechnologyamity universitylucknow(up)<br />
- 2. Sequence comparison technique<br />Pairwise Alignment<br />Local Alignment(Smith WatermanAlgorithm)<br />Global Alignment(Needleman Wunsch Algorithm)<br />Multiple Alignment<br />Heuristic Methods<br />Rather than struggling to find the optimal alignment we may save a lot of time by employing heuristic algorithms<br />Execution time is much faster<br />May completely miss the optimal alignment<br /> FASTA and<br /> BLAST<br />
- 3. A<br />T<br />T<br />G<br />A<br />C<br />T<br />T<br />A<br />A<br />G<br />1<br />1<br />1<br />1<br />1<br />1<br />1<br />1<br />1<br />1<br />1<br />G<br />2<br />2<br />2<br />2<br />1<br />1<br />1<br />1<br />1<br />1<br />1<br />G<br />2<br />2<br />2<br />2<br />2<br />2<br />2<br />2<br />2<br />2<br />1<br />A<br />3<br />3<br />3<br />3<br />3<br />3<br />3<br />3<br />2<br />2<br />1<br />T<br />4<br />4<br />4<br />4<br />4<br />4<br />3<br />3<br />2<br />2<br />1<br />C<br />5<br />5<br />5<br />5<br />4<br />4<br />3<br />3<br />2<br />2<br />1<br />G<br />6<br />5<br />5<br />5<br />5<br />4<br />3<br />3<br />3<br />2<br />1<br />A<br />Heuristic Methods<br />Problem of Dynamic Programming<br /> D.P. compute the score in a lot of useless area for optimal sequence<br />FASTA focuses on diagonal area<br />
- 4. Heuristic <br />Heuristic<br /> Good local alignment should have some exact match subsequence.<br />FASTA focus on this area<br />
- 5. Heuristic Methods: FASTA and BLAST<br />FASTA <br />First fast sequence searching algorithm for comparing a query sequence against a database.<br />BLAST <br />Basic Local Alignment Search Technique<br /> Improvement of FASTA: Search speed, ease of use, statistical rigor.<br />
- 6. FASTA ALGORITHM<br />(a)Find runs of identical words<br />Identify regions shared by the two sequences that have the highest density of single identities (ktup=1) or two consecutive identities(ktup=2)<br />(b) Re-score using PAM matrix. <br />Longest diagonals are scored again using the PAM-250 matrix (or other matrix). The best scores are saved as “init1” scores.<br />
- 7. FASTA Algorithm<br />“init1” <br />ktup=2<br />
- 8. FASTA ALGORITHM <br />(c) Join segments using gaps and eliminate other segments. <br />Longdiagonals that are neighbors are joined. The score for this joined region is“initn”. This score may be lower due to a penalty for a gap.<br />(d) Use DP to create the optimal alignment. <br />construct an optimal alignment of the query sequence and the library sequence (SW algorithm).This score is reported as the optimized score<br />
- 9. FASTA Alignments<br />“initn” <br />
- 10. FASTA Algorithm- Find words of identical words. <br />Lookup table showing the positions of each word of length k, or k-tuple, is constructed for each sequence. <br />The relative positions of each word in the two sequences are then calculated by subtracting the position in the first sequence from that in the second. <br />Words that have the same offset position are in phase and reveal a region of alignment between the two sequences.<br />
- 11. Look-up table<br />
- 12. A<br />T<br />T<br />G<br />A<br />C<br />T<br />T<br />A<br />A<br />G<br />*<br />*<br />G<br />Location<br />Q<br />*<br />*<br />G<br />2,3,7,11<br />A<br />*<br />*<br />*<br />*<br />A<br />6<br />C<br />*<br />*<br />*<br />*<br />T<br />1,8<br />G<br />*<br />C<br />*<br />*<br />G<br />4,5,9,10<br />T<br />*<br />*<br />*<br />*<br />A<br />FASTA - Algorithm -<br />Use look-up Table<br />Query : G A A T T C A G T T A<br />Sequence: G G A T C G A<br />Dot—Matrix <br /> 1 2 3 4 5 6 7 8 9 10 11<br />Look-up Table<br />
- 13. FASTA - Algorithm -<br />Use the dynamic programming in restricted area around the best-score alignment to find out the higher-score alignment than the best-score alignment<br />Width of this band is a parameter<br />
- 14. FASTA - Complexity <br />Complexity<br /> Step 1 and 2 // select the best 10 diagonal run//<br /> Let n be a sequence from DB<br />O(n) because Step 1 just uses look up table<br /> O(n) << O(mn) m,n = 100 to 200<br />
- 15. FASTA - Complexity <br />compute partial D.P. Depends on the restricted area<br />< O(mn) <br />Therefore, FASTA is faster than D.P.<br />Width of this band is a parameter<br />
- 16. Step 1: Finding Seeds <br />t<br />s<br />16<br />
- 17. Step 2: Re-scoring Segments, Keeping Top 10 <br />t<br />s<br />17<br />
- 18. Step 3: Eliminating Unlikely Segments <br />t<br />s<br />18<br />
- 19. Step 4: Finding the Best Alignment <br />t<br />s<br />19<br />
- 20. Versions of FASTA<br />FASTA compares a query protein sequence to a protein sequence library to find similar sequences. FASTA also compares a DNA sequence to a DNA sequence library.<br />TFASTA compares a query protein sequence to a DNA sequence library, after translating the DNA sequence library in all six reading frames.<br />FASTX and FASTY translate a query DNA sequence in all three reading forward frames and compare all three frames to a protein sequence database.<br />TFASTX and TFASTY compare a query protein sequence to a DNA sequence database, translating each DNA sequence in all six possible reading frames.<br />
- 21. BLAST<br />Publications:<br />Ungapped BLAST – Alttschul et al., 1990<br />Gapped BLAST, PSI-BLAST - Altschul et al., 1997<br />Basic Local Alignment Search Tool<br />Altschul et al. 1990,1994,1997<br />Heuristic method for local alignment<br />Designed specifically for database searches<br />Based on the same assumption as FASTA that good alignments contain short lengths of exact matches<br />
- 22. Basic Local Alignment Search Tool (BLAST)<br />Input:<br />Query (target) sequence– either DNA, RNA or Protein<br />Scoring Scheme– gap penalties, substitution matrix for proteins, identity/mismatch scores for DNA/RNA<br />Word length W– typical is<br />W=3 for proteins and<br />W=11 for DNA/RNA<br />Output:<br />Statistically significant matches <br />22<br />
- 23. BLAST ALGORITHM PARAMETERS<br />
- 24. Algorithm of BLAST<br />There are three distinct steps, which are represented as follow:<br />Step1: Query preprocessing;<br />Step2: Scan the database for hits;<br />Step3: Extension of hits.<br />
- 25. BLAST - Algorithm <br />Step 1: Query preprocessing;<br /> Create neighbourhood words for each query word <br /> Max:L-w+1<br />Query Word<br />Neighborhood words<br />
- 26. BLAST - Algorithm <br />Step 1: Query preprocessing;<br />A list of words of length 3 for protein (word length 11 is used for DNA sequences)<br />
- 27. BLAST -Query preprocessing<br />Compile the short-hit scoring word list from query.<br /> The length of query word, is 3.<br />Words below threshold are not further pursued.<br />
- 28. BLAST - Algorithm <br />Step 2: Scan the database for hits;<br />For each words list, identify all exact matches with DB sequences<br />Neighborhood Word list<br />Query Word<br />Sequences in DB<br />Sequence 1<br />Sequence 2<br />Step 2<br />Step 1<br />The purpose of Step 1 and 2 is as same as FASTA<br />
- 29. Step3:Extension of the hits<br />Every hit that has been generated is now extended in both directions, without gaps.<br />To determine whether each hit may be part of a longer segment pair with higher score,<br />
- 30. Step3:Extension of the hits<br />HSP (High scoring Segment Pair). <br />If the extended segment pair has score better than equal to S (set as a parameter of the program), it is called HSP<br />MSP (Maximal segment pair). <br />In a comparison, for every sequence in the database, the best scoring HSP is called the MSP<br />
- 31. HIGH –SCORING PAIR(HSP)<br />
- 32. Maximal segment pair(msp)<br />
- 33. Step 2: Extracting Seeds<br />t<br />s<br />33<br />
- 34. Step 3: Finding HSPs<br />t<br />s<br />34<br />
- 35. Step 4: Combining HSPs<br />t<br />s<br />35<br />
- 36. BLAST<br />
- 37. Basic BLAST<br />
- 38. Specialized BLAST<br /><ul><li> Make specific primers with Primer-BLAST
- 39. Search trace archives
- 40. Find conserved domains in your sequence (cds)
- 41. Find sequences with similar conserved domain architecture (cdart)
- 42. Search sequences that have gene expression profiles (GEO)
- 43. Search immunoglobulins(IgBLAST)
- 44. Search for SNPs(snp)
- 45. Screen sequence for vector contamination (vecscreen)
- 46. Align two (or more) sequences using BLAST (bl2seq)
- 47. Search protein or nucleotide targets in PubChem BioAssay
- 48. Search SRA transcript and genomic libraries
- 49. Constraint Based Protein Multiple Alignment Tool
- 50. Needleman-Wunsch Global Sequence Alignment Tool</li></li></ul><li>BLAST DATABASES<br />
- 51. Databases available on BLAST Web server<br />
- 52. Databases available on BLAST Web server<br />
- 53. Options and parameter settings available on the BLAST server<br />

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