1. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment1 BCB 444/544 Lecture 11 First BLAST vs FASTA Plus some Gene Jargon Multiple Sequence Alignment (MSA) #11_Sept14

2. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment2 √Mon Sept 10 - for Lecture 9/10 BLAST variations; BLAST vs FASTA, SW Chp 4 - pp 51-62 √Wed Sept 12 - for Lecture 11 & Lab 4 Multiple Sequence Alignment (MSA) Chp 5 - pp 63-74 Fri Sept 14 - for Lecture 12 Position Specific Scoring Matrices & Profiles Chp 6 - pp 75-78 (but not HMMs) Good Additional Resource re: Sequence Alignment? Wikipedia: http://en.wikipedia.org/wiki/Sequence_alignmenthttp://en.wikipedia.org/wiki/Sequence_alignment Required Reading (before lecture)

3. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment3 Assignments & Announcements - #1 Revised Grading Policy has been sent via email Please review! √Mon Sept 10 - Lab 3 Exercise due 5 PM: to: terrible@iastate.eduterrible@iastate.edu ?Thu Sept 13 - Graded Labs 2 & 3 will be returned at beginning of Lab 4 Fri Sept 14 - HW#2 due by 5 PM (106 MBB) Study Guide for Exam 1 will be posted by 5 PM

4. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment4 Review: Gene Jargon #1 (for HW2, 1c) Exons = "protein-encoding" (or "kept" parts) of eukaryotic genes vs Introns = "intervening sequences" = segments of eukaryotic genes that "interrupt" exons Introns are transcribed into pre-RNA but are later removed by RNA processing & do not appear in mature mRNA so are not translated into protein

5. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment5 Assignments & Announcements - #2 Mon Sept 17 - Answers to HW#2 will be posted by 5 PM Thu Sept 20 - Lab = Optional Review Session for Exam Fri Sept 21 - Exam 1 - Will cover: Lectures 2-12 (thru Mon Sept 17) Labs 1-4 HW2 All assigned reading: Chps 2-6 (but not HMMs) Eddy: What is Dynamic Programming

6. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment6 Chp 4- Database Similarity Searching SECTION II SEQUENCE ALIGNMENT Xiong: Chp 4 Database Similarity Searching √Unique Requirements of Database Searching √Heuristic Database Searching √Basic Local Alignment Search Tool (BLAST) FASTA Comparison of FASTA and BLAST Database Searching with Smith-Waterman Method

7. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment7 Why search a database? Given a newly discovered gene, Does it occur in other species? Is its function known in another species? Given a newly sequenced genome, which regions align with genomes of other organisms? Identification of potential genes Identification of other functional parts of chromosomes Find members of a multigene family

8. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment8 FASTA and BLAST FASTA user defines value for k = word length Slower, but more sensitive than BLAST at lower values of k, (preferred for searches involving a very short query sequence) BLAST family Family of different algorithms optimized for particular types of queries, such as searching for distantly related sequence matches BLAST was developed to provide a faster alternative to FASTA without sacrificing much accuracy Both FASTA, BLAST are based on heuristics Tradeoff: Sensitivity vs Speed DP is slower, but more sensitive

9. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment9 BLAST algorithms can generate both "global" and "local" alignments Global alignment Local alignment

10. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment10 BLAST - a Family of Programs: Different BLAST "flavors" BLASTP - protein sequence query against protein DB BLASTN - DNA/RNA seq query against DNA DB (GenBank) BLASTX - 6-frame translated DNA seq query against protein DB TBLASTN - protein query against 6-frame DNA translation TBLASTX - 6-frame DNA query to 6-frame DNA translation PSI-BLAST - protein "profile" query against protein DB PHI-BLAST - protein pattern against protein DB Newest: MEGA-BLAST - optimized for highly similar sequences http://www.ncbi.nlm.nih.gov/blast/producttable.shtml Which tool should you use?

11. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment11 Detailed Steps in BLAST algorithm 1.Remove low-complexity regions (LCRs) 2.Make a list (dictionary): all words of length 3aa or 11 nt 3.Augment list to include similar words 4.Store list in a search tree (data structure) 5.Scan database for occurrences of words in search tree 6.Connect nearby occurrences 7.Extend matches (words) in both directions 8.Prune list of matches using a score threshold 9.Evaluate significance of each remaining match 10.Perform Smith-Waterman to get alignment

12. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment12 1: Filter low-complexity regions (LCRs) Window length (usually 12) Alphabet size (4 or 20) Frequency of ith letter in the window Low complexity regions, transmembrane regions and coiled-coil regions often display significant similarity without homology. Low complexity sequences can yield false positives. Screen them out of your query sequences! When appropriate! K = computational complexity ; varies from 0 (very low complexity) to 1 (high complexity) e.g., for GGGG: L! = 4!=4x3x2x1= 24 n G =4 n T =n A =n C =0  n i ! = 4!x0!x0!x0! = 24 K=1/4 log 4 (24/24) = 0 For CGTA: K=1/4 log 4 (24/1) = 0.57 This slide has been changed!

13. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment13 2: List all words in query YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GGF GFM FMT MTS TSE SEK …

14. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment14 3: Augment word list YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GGF GFM FMT MTS TSE SEK … AAA AAB AAC … YYY 20 3 = 8000 possible matches

15. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment15 3: Augment word list G G F A A A 0 + 0 + -2 = -2 BLOSUM62 scores Non-match G G F G G Y 6 + 6 + 3 = 15 Match A user-specified threshold, T, determines which 3-letter words are considered matches and non-matches

16. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment16 3: Augment word list YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GGF GFM FMT MTS TSE SEK … GGI GGL GGM GGF GGW GGY …

17. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment17 3: Augment word list Observation: Selecting only words with score > T greatly reduces number of possible matches otherwise, 20 3 for 3-letter words from amino acid sequences!

18. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment18 Example A R N D C Q E G H I L K M F P S T W Y V A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 Find all words that match EAM with a score greater than or equal to 11 EAM 5 + 4 + 5 = 14 DAM 2 + 4 + 5 = 11 QAM 2 + 4 + 5 = 11 ESM 5 + 1 + 5 = 11 EAL 5 + 4 + 2 = 11

19. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment19 4: Store words in search tree Search tree Augmented list of query words “Does this query contain GGF?” “Yes, at position 2.”

20. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment20 Search tree G G LMFWY GGF GGL GGM GGW GGY

21. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment21 Example Put this word list into a search tree DAM QAM EAM KAM ECM EGM ESM ETM EVM EAI EAL EAV DQEK AAAGSTVAC MMMMMMMM L M I V

22. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment22 5: Scan the database sequences Database sequence Query sequence        

23. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment23 Example Scan this "database" for occurrences of your words MKFLILLFNILCLDAMLAADNHGVGPQGASGVDPITFDINSNQTGPAFLTAVEAIGVKYLQVQHGSNVNIHRLVEGNVKAMENA E A M P Q L S V D A M 

24. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment24 6: Connect nearby occurences (diagonal matches in Gapped BLAST) Database sequence Query sequence         Two dots are connected IFF if they are less than A letters apart & are on diagonal

25. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment25 7: Extend matches in both directions DB Scan

26. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment26 7: Extend matches, calculating score at each step Each match is extended to left & right until a negative BLOSUM62 score is encountered Extension step typically accounts for > 90% of execution time L P P Q G L L Query sequence M P P E G L L Database sequence 7 2 6 BLOSUM62 scores word score = 15 2 7 7 2 6 4 4 HSP SCORE = 32 (High Scoring Pair)

27. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment27 8: Prune matches Discard all matches that score below defined threshold

28. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment28 9: Evaluate significance BLAST uses an analytical statistical significance calculation RECALL: 1.E-value: E = m x n x P m = total number of residues in database n = number of residues in query sequence P = probability that an HSP is result of random chance lower E-value, less likely to result from random chance, thus higher significance 2.Bit Score: S' = normalized score, to account for differences in size of database (m) & sequence length(n) ; Note (below) that bit score is linearly related to raw alignment score, so: higher S' means alignment has higher significance This slide has been changed! S'= ( X S - ln K)/ln2 where: = Gumble distribution constant S = raw alignment score K = constant associated with scoring matrix For more details - see text & BLAST tutorial

29. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment29 10: Use Smith-Waterman algorithm (DP) to generate alignment ONLY significant matches are re-analyzed using Smith-Waterman DP algorithm. Alignments reported by BLAST are produced by dynamic programming

30. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment30 BLAST: What is a "Hit"? A hit is a w-length word in database that aligns with a word from query sequence with score > T BLAST looks for hits instead of exact matches Allows word size to be kept larger for speed, without sacrificing sensitivity Typically, w = 3-5 for amino acids, w = 11-12 for DNA T is the most critical parameter: ↑ T  ↓ “background” hits (faster) ↓ T  ↑ ability to detect more distant relationships (at cost of increased noise)

31. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment31 Tips for BLAST Similarity Searches If you don’t know, use default parameters first Try several programs & several parameter settings If possible, search on protein sequence level Scoring matrices: PAM1 / BLOSUM80: if expect/want less divergent proteins PAM120 / BLOSUM62: "average" proteins PAM250 / BLOSUM45: if need to find more divergent proteins Proteins: >25-30% identity ( and >100aa )-> likely related 15-25% identity -> twilight zone likely unrelated

32. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment32 Practical Issues Searching on DNA or protein level? In general, protein-encoding DNA should be translated! DNA yields more random matches: 25% for DNA vs. 5% for proteins DNA databases are larger and grow faster Selection (generally) acts on protein level Synonymous mutations are usually neutral DNA sequence similarity decays faster

33. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment33 BLAST vs FASTA Seeding: BLAST integrates scoring matrix into first phase FASTA requires exact matches (uses hashing) BLAST increases search speed by finding fewer, but better, words during initial screening phase FASTA uses shorter word sizes - so can be more sensitive Results: BLAST can return multiple best scoring alignments FASTA returns only one final alignment

34. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment34 BLAST & FASTA References FASTA - developed first Pearson & Lipman (1988) Improved Tools for Biological Sequence Comparison. PNAS 85:2444- 2448 BLAST Altschul, Gish, Miller, Myers, Lipman, J. Mol. Biol. 215 (1990) Altschul, Madden, Schaffer, Zhang, Zhang, Miller, Lipman (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25:3389-402

35. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment35 BLAST Notes - & DP Alternatives BLAST uses heuristics: it may miss some good matches But, it’s fast: 50 - 100X faster than Smith-Waterman (SW) DP Large impact: NCBI’s BLAST server handles more than 100,000 queries/day Most used bioinformatics program in the world!  But - Xiong says: "It has been estimated that for some families of protein sequences BLAST can miss 30% of truly significant matches." Increased availability of parallel processing has made DP-based approaches feasible: 2 DP-based web servers: both more sensitive than BLAST Scan Protein Sequence: http://www.ebi.ac.uk/scanps/index.htmlhttp://www.ebi.ac.uk/scanps/index.html Implements modified SW optimized for parallel processing ParAlign www.paralign.org - parallel SW or heuristicswww.paralign.org

36. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment36 NCBI - BLAST Programs Glossary & Tutorials http://www.ncbi.nlm.nih.gov/BLAST/ http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/glossary2.html http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/information3.html BLAST

37. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment37 Chp 5- Multiple Sequence Alignment SECTION II SEQUENCE ALIGNMENT Xiong: Chp 5 Multiple Sequence Alignment Scoring Function Exhaustive Algorithms Heuristic Algorithms Practical Issues

38. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment38 Multiple Sequence Alignments Credits for slides: Caragea & Brown, 2007; Fernandez-Baca, Heber &Hunter

39. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment39 Overview 1.What is a multiple sequence alignment (MSA)? 2.Where/why do we need MSA? 3.What is a good MSA? 4.Algorithms to compute a MSA

40. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment40 Multiple Sequence Alignment Generalize pairwise alignment of sequences to include > 2 homologous sequences Analyzing more than 2 sequences gives us much more information: Which amino acids are required? Correlated? Evolutionary/phylogenetic relationships Similar to PSI-BLAST idea (not yet covered in lecture): use a set of homologous sequences to provide more "sensitivity"

41. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment41 What is a MSA? ATTTG- ATTTGC AT-TGC ATTTG ATTTGC ATT-GC ATTT-G- ATTT-GC AT-T-GC MSA Not a MSA Why?

42. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment42 Definition: MSA Given a set of sequences, a multiple sequence alignment is an assignment of gap characters, such that resulting sequences have same length no column contains only gaps ATTTG- ATTTGC AT-TGC ATTTG ATTTGC ATT-GC ATTT-G- ATTT-GC AT-T-GC YESNO

43. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment43 Displaying MSAs: using CLUSTAL W * entirely conserved column : all residues have ~ same size AND hydropathy. all residues have ~ same size OR hydropathy RED: AVFPMILW (small) BLUE: DE (acidic, negative chg) MAGENTA: RHK (basic, positive chg) GREEN: STYHCNGQ (hydroxyl + amine + basic)

44. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment44 A single sequence that represents most common residue of each column in a MSA Example: What is a Consensus Sequence? FGGHL-GF F-GHLPGF FGGHP-FG FGGHL-GF Steiner consensus seqence: Given sequences s 1,…, s k, find a sequence s* that maximizes Σ i S(s*,s i )

45. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment45 Applications of MSA Building phylogenetic trees Finding conserved patterns, e.g.: Regulatory motifs (TF binding sites) Splice sites Protein domains Identifying and characterizing protein families Find out which protein domains have same function Finding SNPs (single nucleotide polymorphisms) & mRNA isoforms (alternatively spliced forms) DNA fragment assembly (in genomic sequencing)

46. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment46 Application: Recover Phylogenetic Tree NYLS NFLS What was series of events that led to current species?

47. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment47 Application: Discover Conserved Patterns Rationale: if they are homologous (derived from a common ancestor), they may be structurally equivalent TATA box = transcriptional promoter element Is there a conserved cis-acting regulatory sequence?

48. 9/14/07BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment48 Goal: Characterize Protein Families Which parts of globin sequences are most highly conserved?