#11 - Multiple Sequence Alignment 9/14/07

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1 BCB 444/544 Required Reading (before lecture) First Lecture 11 BLAST vs FASTA Plus some Gene Jargon Multiple Sequence Alignment (MSA) #11_Sept14 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 1 Mon Sept 10 - for Lecture 9/10 BLAST variations; BLAST vs FASTA, SW Chp 4 - pp Wed Sept 12 - for Lecture 11 & Lab 4 Multiple Sequence Alignment (MSA) Chp 5 - pp Fri Sept 14 - for Lecture 12 Position Specific Scoring Matrices & Profiles Chp 6 - pp (but not HMMs) Good Additional Resource re: Sequence Alignment? Wikipedia: BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 2 Assignments & Announcements - #1 Revised Grading Policy has been sent via Please review! Mon Sept 10 - Lab 3 Exercise due 5 PM: to: terrible@iastate.edu 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?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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 3 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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 4 Assignments & Announcements - #2 Chp 4- Database Similarity Searching 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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 5 SECTION II Xiong: Chp 4 SEQUENCE ALIGNMENT 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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 6 BCB 444/544 Fall 07 Dobbs 1

2 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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 7 FASTA FASTA and BLAST 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 Both FASTA, BLAST are based on heuristics Tradeoff: Sensitivity vs Speed DP is slower, but more sensitive 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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 8 BLAST algorithms can generate both "global" and "local" alignments 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 Global alignment Local alignment PSI-BLAST - protein "profile" query against protein DB PHI-BLAST - protein pattern against protein DB Which Newest: tool MEGA-BLAST should you use? - optimized for highly similar sequences BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 9 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 10 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 1: Filter low-complexity regions (LCRs) 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! e.g., for GGGG: L! = 4!=4x3x2x1= 24 n G =4 n T =n A =n C =0 P n i! = 4!x0!x0!x0! = 24 K=1/4 log 4 (24/24) = 0 K = computational complexity; varies from 0 (very low complexity) to 1 (high complexity) Window length (usually 12) 1 K = log L This slide has been changed! Alphabet size (4 or 20) Frequency of ith 9. Evaluate significance of each remaining match letter in the window For CGTA: K=1/4 log 4 (24/1) = 0.57 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/ Perform Smith-Waterman to get alignment 11 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 12 N & $ $ $ % # L!!! ni!! " ' i BCB 444/544 Fall 07 Dobbs 2

3 2: List all words in query 3: Augment word list YMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GFM FMT MTS TSE SEK YMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GFM FMT MTS TSE SEK AAA AAB AAC YYY 20 3 = 8000 possible matches BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 13 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/ : Augment word list 3: Augment word list BLOSUM62 scores G G F A A A = -2 G G F G G Y = 15 Non-match Match A user-specified threshold, T, determines which 3-letter words are considered matches and non-matches YMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GFM FMT MTS TSE SEK GGI GGL GGM GGW GGY BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 15 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 16 Observation: 3: Augment word list Selecting only words with score > T greatly reduces number of possible matches otherwise, 20 3 for 3-letter words from amino acid sequences! Example Find all words that match EAM with a score greater than or equal to 11 A R N D C Q E G H I L K M F P S T W Y V A R N D C Q E G H I L K M F P S T W Y V EAM = 14 DAM = 11 QAM = 11 ESM = 11 EAL = 11 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 17 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 18 BCB 444/544 Fall 07 Dobbs 3

4 4: Store words in search tree Search tree Augmented list of query words Search tree Does this query contain? GGL GGM GGW GGY G G F L M W Y Yes, at position 2. BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 19 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 20 Example 5: Scan the database sequences Put this word list into a search tree DAM QAM EAM KAM ECM EGM ESM ETM EVM EAI EAL EAV D Q E K A A A C G S T V A M M M M M M M M I L M V Query sequence Database sequence BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 21 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 22 Example 6: Connect nearby occurences (diagonal matches in Gapped BLAST) Scan this "database" for occurrences of your words MKFLILLFNILCLDAMLAADNHGVGPQGASGVDPITFDINSNQTGPAFLTAVEAIGVKYLQVQHGSNVNIHRLVEGNVKAMENA E A M P Q L S V D A M Query sequence Database sequence Two dots are connected IFF if they are less than A letters apart & are on diagonal BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 23 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 24 BCB 444/544 Fall 07 Dobbs 4

5 7: Extend matches in both directions 7: Extend matches, calculating score at each step Scan DB L P P Q G L L Query sequence M P P E G L L Database sequence <word> BLOSUM62 scores word score = 15 < > HSP SCORE = 32 (High Scoring Pair) Each match is extended to left & right until a negative BLOSUM62 score is encountered Extension step typically accounts for > 90% of execution time BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 25 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/ : Prune matches 9: Evaluate significance This slide has been changed! Discard all matches that score below defined threshold BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 27 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 2. Bit Score: S' = lower E-value, less likely to result from random chance, thus higher significance 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 S'= score, (λ X so: S - ln higher K)/ln2 S' where: means alignment λ = Gumble has distribution higher significance constant S = raw alignment score For more details - see text & BLAST tutorial K = constant associated with scoring matrix BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/ : 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 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 = for DNA T is the most critical parameter: T background hits (faster) T ability to detect more distant relationships (at cost of increased noise) BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 29 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 30 BCB 444/544 Fall 07 Dobbs 5

6 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 Practical Issues Searching on DNA or protein level? In general, protein-encoding DNA should be translated! 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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 31 <15% identity -> likely unrelated 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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 32 BLAST vs FASTA Seeding: BLAST integrates scoring matrix into first phase FASTA requires exact matches (uses hashing) BLAST & FASTA References FASTA - developed first Pearson & Lipman (1988) Improved Tools for Biological Sequence Comparison. PNAS 85: 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 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 33 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: BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 34 BLAST Notes - & DP Alternatives NCBI - BLAST Programs Glossary & Tutorials BLAST BLAST uses heuristics: it may miss some good matches But, it s fast: X 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: Implements modified SW optimized for parallel processing ParAlign - parallel SW or heuristics BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/ BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 36 BCB 444/544 Fall 07 Dobbs 6

7 Chp 5- Multiple Sequence Alignment Multiple Sequence Alignments SECTION II SEQUENCE ALIGNMENT Xiong: Chp 5 Multiple Sequence Alignment Scoring Function Exhaustive Algorithms Heuristic Algorithms Practical Issues BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 37 Credits for slides: Caragea & Brown, 2007; Fernandez-Baca, Heber &Hunter BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 38 Overview Multiple Sequence Alignment 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 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" BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 39 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 40 What is a MSA? Definition: MSA ATT-GC ATTTGC ATTTG Not a MSA AT-TGC ATTTGC ATTTG- MSA Why? AT-T-GC ATTT-GC ATTT-G- Not a 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 ATT-GC ATTTGC ATTTG NO AT-TGC ATTTGC ATTTG- YES AT-T-GC ATTT-GC ATTT-G- NO BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 41 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 42 BCB 444/544 Fall 07 Dobbs 7

8 Displaying MSAs: using CLUSTAL W What is a Consensus Sequence? A single sequence that represents most common residue of each column in a MSA RED: AVFPMILW (small) BLUE: DE (acidic, negative chg) MAGENTA: RHK (basic, positive chg) GREEN: STYHCNGQ (hydroxyl + amine + basic) * entirely conserved column : all residues have ~ same size AND hydropathy Example: 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 ). all residues have ~ same size OR hydropathy BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 43 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 44 Applications of MSA Application: Recover Phylogenetic Tree 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) What was series of events that led to current species? NYLS NFLS NYLS BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 45 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 46 Application: Discover Conserved Patterns Is there a conserved cis-acting regulatory sequence? Goal: Characterize Protein Families Which parts of globin sequences are most highly conserved? Rationale: if they are homologous (derived from a common ancestor), they may be structurally equivalent TATA box = transcriptional promoter element BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 47 BCB 444/544 F07 ISU Dobbs #11 - Multiple Sequence Alignment 9/14/07 48 BCB 444/544 Fall 07 Dobbs 8