1. From Pairwise Alignment to Database Similarity Search

2. Best score for aligning part of sequences Dynamic programming Algorithm: Smith-Waterman Table cells never score below zero Best score for aligning the full length sequences Dynamic programming Algorithm: Needelman- Wunch Table cells are allowed any score Global Local Pairwise Alignment Summary

3. 3 Sequences that are similar probably have the same function Why do we care to align sequences?

4. new sequence ? Sequence Database ≈ Similar function Discover Function of a new sequence

5. Searching Databases for similar sequences Naïve solution: Use exact algorithm to compare each sequence in the database to query. Is this reasonable ?? How much time will it take to calculate?

6. Complexity for genomes Human genome contains 3  10 9 base pairs –Searching an mRNA against HG requires ~10 13 cells -Even efficient exact algorithms will be extremely slow when preformed millions of times even with parallel computing.

7. So what can we do?

8. Searching databases Solution: Use a heuristic (approximate) algorithm to discard most irrelevant sequences and perform the exact algorithm on the small group of remaining sequences.

9. Heuristic strategy Remove regions that are not useful for meaningful alignments Preprocess database into new data structure to enable fast accession

10. Heuristic strategy Remove regions that are not useful for meaningful alignments Preprocess database into new data structure to enable fast accession

11. AAAAAAAAAAA ATATATATATATA Transposable elements (LINEs, SINEs) What sequences to remove? Low complexity sequences

12. Low Complexity Sequences What's wrong with them? Produce artificial high scoring alignments. So what do we do? We apply Low Complexity masking to the database and the query sequence Mask TCGATCGTATATATACGGGGGGTA TCGATCGNNNNNNNNCNNNNNNTA

13. Low Complexity Sequences Complexity is calculated as: Where N=4 in DNA (4 bases), L is the length of the sequence and n i the number of each residue in the sequence K=1/L log N (L!/Π n i !) all i For the sequence GGGG: L! =4x3x2x1=24 n g =4 n c =0 n a =0 n t =0 Πn i =24x1x1x1=24 K =1/4 log 4 (24/24)=0 For the sequence CTGA: L! =4x3x2x1=24 ng =1 nc =1 na =1 nt =1 Πni =1x1x1x1 K =1/4 log 4 (24/1)=0.573

14. Heuristic strategy Remove low-complexity regions that are not useful for meaningful alignments Preprocess database into new data structure to enable fast accession

15. Heuristic (approximate solution) Methods: FASTA and BLAST FASTA (Lipman & Pearson 1985) –First fast sequence searching algorithm for comparing a query sequence against a database BLAST - Basic Local Alignment Search Technique (Altschul et al 1990) –improvement of FASTA: Search speed, ease of use, statistical rigor

16. FASTA and BLAST Common idea - a good alignment contains subsequences of absolute identity: –First, identify very short (almost) exact matches. –Next, the best short hits from the 1st step are extended to longer regions of similarity. –Finally, the best hits are optimized using the Smith- Waterman algorithm.

17. FastA (fast alignment) Assumption: a good alignment probably matches some identical ‘words’ Example: Aligning a query sequence to a database Database record: ACTTGTAGATACAAAATGTG Query sequence: A-TTGTCG-TACAA-ATCTG

18. Preprocess of all the sequences in the database. Find short words and organize in dictionaries. Process the query sequence and prepare a dictionary. –ATGGCTGCTCAAGT…. ATGGTGGCGGCT… … FastA Query

19. FastA locates regions of the query sequence and the search set sequence that have high densities of exact word matches. For DNA sequences the word length used is 6. Words in seq1 Words in seq2

20. The 10 highest-scoring sequence regions are saved and re-scored using a scoring matrix. seq1 seq2

21. FastA determines if any of the initial regions from different diagonals may be joined together to form an approximate alignment with gaps. Only non-overlapping regions may be joined. seq1 seq2

22. The score for the joined regions is the sum of the scores of the initial regions minus a joining penalty for each gap. seq1 seq2

23. BLAST Basic Local Alignment Search Tool Developed to be as sensitive as FastA but much faster. Also searches for short words. –Protein 3 letter words –DNA 11 letter words. –Words can be similar, not only identical

24. BLAST (Protein Sequence Example) 1.Search the database for matching word pairs (> T) Example: …FSGTWYA… A list of words (w=3) is: FSG SGT GTW TWY WYA YSG TGT ATW SWY WFA FTG SVT GSW TWF WYS

25. BLAST (Protein Sequence Example) 1.Search the database for matching word pairs (>T) 2.Extend word pairs as much as possible, i.e., as long as the total score increases Result: High-scoring Segment Pairs (HSPs) THEFIRSTLINIHFSGTWYAAMESIRPATRICKREAD INVIEIAFDGTWTCATTNAMHEWASNINETEEN

26. BLAST 3. Try to connect HSPs by aligning the sequences in between them: THEFIRSTLINIHFSGTWYAA____M_ESIRPATRICKREAD INVIEIAFDGTWTCATTNAMHEW___ASNINETEEN The Gapped Blast algorithm allows several segments that are separated by short gaps to be connected together to one alignment

27. How to interpret a BLAST search: The score is a measure of the similarity of the query to the sequence shown. How do we know if the score is significant? -Statistical significance -Biological significance

28. Assessing Alignment Significance Determine probability of alignment occurring at random IdealNo Good Random Related Score Frequency For each score we can count the probability of getting it by chance

29. The expect value E-value is the number of alignments with scores greater than or equal to score S that are expected to occur by chance in a database search. An E value is related to a probability value p (p-value). page 105 How to interpret a BLAST search: For each blast score we get an E-value

30. BLAST- E value: Increases linearly with length of query sequence Increases linearly with length of database Decreases exponentially with score of alignment –K,λ: statistical parameters dependent upon scoring system and background residue frequencies m = length of query ; n= length of database ; s= score

31. From raw scores to bit scores Bit scores S’ are normalized and are comparable in different databases The E value corresponding to a given bit score is: E = mn 2 -S’ page 106

32. What is a Good E-value (Thumb rule) E values of less than 0.00001 show that sequences are almost always homologues. Greater E values, can represent homologues as well. Generally the decision whether an E-value is biologically significant depends on the size of database that is searched Sometimes a real match has an E value > 1 Sometimes a similar E value occurs for a short exact match and long less exact match

33. Treating Gaps in BLAST >Human DNA CATGCGACTGACcgacgtcgatcgatacgactagctagcATCGATCATA >Human mRNA CATGCGACTGACATCGATCATA Biologically, indels occur in groups we want our gap score to reflect this

34. Gap Scores Standard solution: affine gap model w x = g + r(x-1) w x : total gap penalty; g: gap open penalty; r: gap extend penalty ;x: gap length –Once-off cost for opening a gap –Lower cost for extending the gap –Changes required to algorithm

35. Significance of Gapped Alignments Gapped alignments use same statistics and K cannot be easily estimated Empirical estimations and gap scores determined by looking at random alignments

36. BLAST BLAST is a family of programs Query:DNAProtein Database:DNAProtein

37. Choose the BLAST program ProgramInputDatabase 1 blastnDNADNA 1 blastpproteinprotein 6 blastxDNAprotein 6 tblastnprotein DNA 36 tblastxDNA DNA

38. Example :The lipocalins (each dot is a protein) retinol-binding protein odorant-binding protein apolipoprotein D Example is taken from Bioinformatics and Functional Genomics by Jonathan Pevsner (ISBN 0-471-21004-8). Copyright © 2003 by John Wiley & Sons, Inc.

39. BLAST search with PAEP as a query finds many other lipocalins

40. Assessing whether proteins are homologous RBP4 and PAEP: Low bit score, E value 0.49, 24% identity but they are indeed homologous.