BCB 444/544 Lecture 11 First BLAST vs FASTA Plus some Gene Jargon Multiple Sequence Alignment (MSA) #11_Sept 14 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 1
Required Reading (before lecture) √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_alignment BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 2
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. 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 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 3
Review: Gene Jargon #1 (for HW 2, 1 c) 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 m. RNA • so are not translated into protein BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 4
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 HW 2 All assigned reading: Chps 2 -6 (but not HMMs) Eddy: What is Dynamic Programming BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 5
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 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 6
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 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 7
FASTA and BLAST • Both FASTA, BLAST are based on heuristics • Tradeoff: Sensitivity vs Speed • DP is slower, but more sensitive • 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 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 8
BLAST algorithms can generate both "global" and "local" alignments Global alignment Local alignment BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 9
BLAST - a Family of Programs: Different BLAST "flavors" • • • BLASTP - protein sequence query against protein DB BLASTN - DNA/RNA seq query against DNA DB (Gen. Bank) 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 Which tool should you use? http: //www. ncbi. nlm. nih. gov/blast/producttable. shtml BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 10
Detailed Steps in BLAST algorithm 1. Remove low-complexity regions (LCRs) 2. Make a list (dictionary): all words of length 3 aa 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 1. Prune list of matches using a score threshold 2. Evaluate significance of each remaining match 3. Perform Smith-Waterman to get alignment BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 11
1: Filter low-complexity regions (LCRs) This slide has been changed! K = computational complexity; • Low complexity regions, varies from 0 (very low complexity) transmembrane regions and to 1 (high complexity) coiled-coil regions often display Alphabet size significant similarity without (4 or 20) Window length homology. (usually 12) • Low complexity sequences can yield false positives. • Screen them out of your query sequences! When appropriate! e. g. , for GGGG: L! = 4!=4 x 3 x 2 x 1= 24 n. G=4 n. T=n. A=n. C=0 P ni! = 4!x 0!x 0! = 24 K=1/4 log 4 (24/24) = 0 For CGTA: K=1/4 log 4(24/1) = 0. 57 Frequency of ith letter in the window BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 12
2: List all words in query YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GGF GFM FMT MTS TSE SEK … BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 13
3: Augment word list YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GGF GFM AAA AAB FMT AAC MTS 203 = 8000 … TSE possible matches SEK YYY … BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 14
3: Augment word list G G F A A A 0 + -2 = -2 Non-match G G 6 + BLOSUM 62 scores Match F Y 3 = 15 A user-specified threshold, T, determines which 3 -letter words are considered matches and non-matches BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 15
3: Augment word list YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ YGG GGF GFM GGI GGL FMT GGM MTS GGF GGW TSE GGY SEK … … BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 16
3: Augment word list Observation: Selecting only words with score > T greatly reduces number of possible matches otherwise, 203 for 3 -letter words from amino acid sequences! BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 17
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 4 -1 -2 -2 0 -1 -1 0 -2 -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 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 -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 -3 2 4 -2 2 0 -3 -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 F -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 -1 -2 -2 -1 -1 -2 -1 1 5 -2 -2 0 W -3 -3 -4 -4 -2 -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 EAM DAM QAM ESM EAL BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 5 2 2 5 5 + + + 4 4 4 1 4 + + + 5 5 2 = = = 14 11 11 3/17/2018 18
4: Store words in search tree Augmented list of query words “Does this query contain GGF? ” Search tree “Yes, at position 2. ” BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 19
Search tree GGF GGL GGM GGW GGY G G F L M BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment W Y 3/17/2018 20
Example Put this word list into a search tree DAM QAM EAM KAM ECM EGM ESM ETM EVM EAI EAL EAV D A A M Q E K G S T V A M I C M M M V L M BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 21
5: Scan the database sequences Query sequence Database sequence BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 22
Example Scan this "database" for occurrences of your words MKFLILLFNILCLDAMLAADNHGVGPQGASGVDPITFDINSNQTGPAFLTAVEAIGVKYLQVQHGSNVNIHRLVEGNVKAMENA E A M P Q L S V D A M BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 23
6: Connect nearby occurences (diagonal matches in Gapped BLAST) Query sequence Database sequence Two dots are connected IFF if they are less than A letters apart & are on diagonal BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 24
7: Extend matches in both directions Scan DB BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 25
7: Extend matches, calculating score at each step L P M P P Q G L L P E G L L <word> 7 2 6 <-----> 2 7 7 2 6 4 4 Query sequence Database sequence BLOSUM 62 scores word score = 15 HSP SCORE = 32 (High Scoring Pair) • Each match is extended to left & right until a negative BLOSUM 62 score is encountered • Extension step typically accounts for > 90% of execution time BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 26
8: Prune matches • Discard all matches that score below defined threshold BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 27
9: Evaluate significance This slide has been changed! • 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 1. • 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 S'= ( X S - ln K)/ln 2 where: = Gumble distribution constant S = raw alignment score K = constant associated with scoring matrix For more details - see text & BLAST tutorial BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 28
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 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 29
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) BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 30
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: PAM 1 / BLOSUM 80: if expect/want less divergent proteins PAM 120 / BLOSUM 62: "average" proteins PAM 250 / BLOSUM 45: if need to find more divergent proteins • Proteins: >25 -30% identity (and >100 aa) 15 -25% identity <15% identity -> likely related -> twilight zone -> likely unrelated BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 31
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 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 32
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 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 33
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, Miller, Lipman (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25: 3389 -402 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 34
BLAST Notes - & DP Alternatives • BLAST uses heuristics: it may miss some good matches • But, it’s fast: 50 - 100 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! Ø 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. html Implements modified SW optimized for parallel processing • Par. Align www. paralign. org - parallel SW or heuristics BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 35
NCBI - BLAST Programs Glossary & Tutorials BLAST • http: //www. ncbi. nlm. nih. gov/BLAST/ • http: //www. ncbi. nlm. nih. gov/Education/BLASTinfo/glossary 2. html • http: //www. ncbi. nlm. nih. gov/Education/BLASTinfo/information 3. html BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 36
Chp 5 - Multiple Sequence Alignment SECTION II SEQUENCE ALIGNMENT Xiong: Chp 5 Multiple Sequence Alignment • • Scoring Function Exhaustive Algorithms Heuristic Algorithms Practical Issues BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 37
Multiple Sequence Alignments Credits for slides: Caragea & Brown, 2007; BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 38
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 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 39
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" BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 40
What is a MSA? ATT-GC ATTTG Not a MSA AT-TGC ATTTG- AT-T-GC ATTT-G- MSA Not a MSA Why? BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 41
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 ATT-GC ATTTG AT-TGC ATTTG- AT-T-GC ATTT-G- NO YES NO BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 42
Displaying MSAs: using CLUSTAL W 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 BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment AND hydropathy OR hydropathy 3/17/2018 43
What is a Consensus Sequence? A single sequence that represents most common residue of each column in a MSA Example: FGGHL-GF F-GHLPGF FGGHP-FG FGGHL-GF Steiner consensus seqence: Given sequences s 1, …, sk, find a sequence s* that maximizes Σi S(s*, si) BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 44
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) & m. RNA isoforms (alternatively spliced forms) • DNA fragment assembly (in genomic sequencing) BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 45
Application: Recover Phylogenetic Tree What was series of events that led to current species? NYLS NFLS NYLS BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 46
Application: Discover Conserved Patterns Is there a conserved cis-acting regulatory sequence? Rationale: if they are homologous (derived from a common ancestor), they may be structurally equivalent TATA box = transcriptional promoter element BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 47
Goal: Characterize Protein Families Which parts of globin sequences are most highly conserved? BCB 444/544 F 07 ISU Dobbs #11 - Multiple Sequence Alignment 3/17/2018 48
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