The document discusses various sequence comparison techniques including pairwise alignment, local alignment, global alignment, and multiple alignment. It describes heuristic methods like FASTA and BLAST that are faster than dynamic programming but may miss optimal alignments. It provides details on the FASTA algorithm including finding identical words, re-scoring, joining segments, and dynamic programming. It also explains the BLAST algorithm and steps including query preprocessing, database scanning, and hit extension. Specialized BLAST databases and tools are also listed.

1. Sequence comparison technique Ms.ruchiyadavlectureramity institute of biotechnologyamity universitylucknow(up)

2. Sequence comparison technique Pairwise Alignment Local Alignment(Smith WatermanAlgorithm) Global Alignment(Needleman Wunsch Algorithm) Multiple Alignment Heuristic Methods Rather than struggling to find the optimal alignment we may save a lot of time by employing heuristic algorithms Execution time is much faster May completely miss the optimal alignment FASTA and BLAST

3. A T T G A C T T A A G 1 1 1 1 1 1 1 1 1 1 1 G 2 2 2 2 1 1 1 1 1 1 1 G 2 2 2 2 2 2 2 2 2 2 1 A 3 3 3 3 3 3 3 3 2 2 1 T 4 4 4 4 4 4 3 3 2 2 1 C 5 5 5 5 4 4 3 3 2 2 1 G 6 5 5 5 5 4 3 3 3 2 1 A Heuristic Methods Problem of Dynamic Programming D.P. compute the score in a lot of useless area for optimal sequence FASTA focuses on diagonal area

4. Heuristic Heuristic Good local alignment should have some exact match subsequence. FASTA focus on this area

5. Heuristic Methods: FASTA and BLAST FASTA First fast sequence searching algorithm for comparing a query sequence against a database. BLAST Basic Local Alignment Search Technique Improvement of FASTA: Search speed, ease of use, statistical rigor.

6. FASTA ALGORITHM (a)Find runs of identical words Identify regions shared by the two sequences that have the highest density of single identities (ktup=1) or two consecutive identities(ktup=2) (b) Re-score using PAM matrix. Longest diagonals are scored again using the PAM-250 matrix (or other matrix). The best scores are saved as “init1” scores.

7. FASTA Algorithm “init1” ktup=2

8. FASTA ALGORITHM (c) Join segments using gaps and eliminate other segments. 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. (d) Use DP to create the optimal alignment. construct an optimal alignment of the query sequence and the library sequence (SW algorithm).This score is reported as the optimized score

9. FASTA Alignments “initn”

10. FASTA Algorithm- Find words of identical words. Lookup table showing the positions of each word of length k, or k-tuple, is constructed for each sequence. 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. Words that have the same offset position are in phase and reveal a region of alignment between the two sequences.

11. Look-up table

12. A T T G A C T T A A G * * G Location Q * * G 2,3,7,11 A * * * * A 6 C * * * * T 1,8 G * C * * G 4,5,9,10 T * * * * A FASTA - Algorithm - Use look-up Table Query : G A A T T C A G T T A Sequence: G G A T C G A Dot—Matrix 1 2 3 4 5 6 7 8 9 10 11 Look-up Table

13. FASTA - Algorithm - Use the dynamic programming in restricted area around the best-score alignment to find out the higher-score alignment than the best-score alignment Width of this band is a parameter

14. FASTA - Complexity Complexity Step 1 and 2 // select the best 10 diagonal run// Let n be a sequence from DB O(n) because Step 1 just uses look up table O(n) << O(mn) m,n = 100 to 200

15. FASTA - Complexity compute partial D.P. Depends on the restricted area < O(mn) Therefore, FASTA is faster than D.P. Width of this band is a parameter

16. Step 1: Finding Seeds t s 16

17. Step 2: Re-scoring Segments, Keeping Top 10 t s 17

18. Step 3: Eliminating Unlikely Segments t s 18

19. Step 4: Finding the Best Alignment t s 19

20. Versions of FASTA 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. TFASTA compares a query protein sequence to a DNA sequence library, after translating the DNA sequence library in all six reading frames. FASTX and FASTY translate a query DNA sequence in all three reading forward frames and compare all three frames to a protein sequence database. TFASTX and TFASTY compare a query protein sequence to a DNA sequence database, translating each DNA sequence in all six possible reading frames.

21. BLAST Publications: Ungapped BLAST – Alttschul et al., 1990 Gapped BLAST, PSI-BLAST - Altschul et al., 1997 Basic Local Alignment Search Tool Altschul et al. 1990,1994,1997 Heuristic method for local alignment Designed specifically for database searches Based on the same assumption as FASTA that good alignments contain short lengths of exact matches

22. Basic Local Alignment Search Tool (BLAST) Input: Query (target) sequence– either DNA, RNA or Protein Scoring Scheme– gap penalties, substitution matrix for proteins, identity/mismatch scores for DNA/RNA Word length W– typical is W=3 for proteins and W=11 for DNA/RNA Output: Statistically significant matches 22

23. BLAST ALGORITHM PARAMETERS

24. Algorithm of BLAST There are three distinct steps, which are represented as follow: Step1: Query preprocessing; Step2: Scan the database for hits; Step3: Extension of hits.

25. BLAST - Algorithm Step 1: Query preprocessing; Create neighbourhood words for each query word Max:L-w+1 Query Word Neighborhood words

26. BLAST - Algorithm Step 1: Query preprocessing; A list of words of length 3 for protein (word length 11 is used for DNA sequences)

27. BLAST -Query preprocessing Compile the short-hit scoring word list from query. The length of query word, is 3. Words below threshold are not further pursued.

28. BLAST - Algorithm Step 2: Scan the database for hits; For each words list, identify all exact matches with DB sequences Neighborhood Word list Query Word Sequences in DB Sequence 1 Sequence 2 Step 2 Step 1 The purpose of Step 1 and 2 is as same as FASTA

29. Step3:Extension of the hits Every hit that has been generated is now extended in both directions, without gaps. To determine whether each hit may be part of a longer segment pair with higher score,

30. Step3:Extension of the hits HSP (High scoring Segment Pair). If the extended segment pair has score better than equal to S (set as a parameter of the program), it is called HSP MSP (Maximal segment pair). In a comparison, for every sequence in the database, the best scoring HSP is called the MSP

31. HIGH –SCORING PAIR(HSP)

32. Maximal segment pair(msp)

33. Step 2: Extracting Seeds t s 33

34. Step 3: Finding HSPs t s 34

35. Step 4: Combining HSPs t s 35

36. BLAST

37. Basic 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

51. Databases available on BLAST Web server

52. Databases available on BLAST Web server

53. Options and parameter settings available on the BLAST server