B L A S T! BLAST: Basic local alignment search tool 11/23/2010. Copyright notice. November 29, Outline of today s lecture BLAST. Why use BLAST?
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1 November 29, 2010 BLAST: Basic local alignment search tool B L A S T! Jonathan Pevsner, Ph.D. Bioinformatics pevsner@kennedykrieger.org Johns Hopkins School of Medicine Copyright notice Many of the images in this powerpoint presentation are from Bioinformatics and Functional Genomics by Jonathan PevsnerCopyright 2009 by John Wiley & Sons, Inc. These images and materials may not be used without permission from the publisher. We welcome instructors to use these powerpoints for educational purposes, but please acknowledge the source. The book has a homepage at including hyperlinks to the book chapters. Outline of today s lecture BLAST BLAST Practical use Algorithm Strategies Finding distantly tl related proteins: PSI-BLAST Hidden Markov models BLAST-like tools for genomic DNA PatternHunter Megablast BLAT, BLASTZ BLAST (Basic Local Alignment Search Tool) allows rapid sequence comparison of a query sequence against a database. The BLAST algorithm is fast, accurate, and web-accessible. page 101 Why use BLAST? Four components to a BLAST search BLAST searching is fundamental to understanding the relatedness of any favorite query sequence to other known proteins or DNA sequences. Applications include identifying orthologs and paralogs discovering new genes or proteins discovering variants of genes or proteins investigating expressed sequence tags (ESTs) exploring protein structure and function (1) Choose the sequence (query) (2) Select the BLAST program (3) Choose the database to search (4) Choose optional parameters Then click BLAST page 102 page 102 1
2 Step 1: Choose your sequence Sequence can be input in FASTA format or as accession number page 103 page 103 Example of the FASTA format for a BLAST query Step 2: Choose the BLAST program Fig. 2.9 page 32 page 104 Step 2: Choose the BLAST program blastn (nucleotide BLAST) blastp p(p (protein BLAST) blastx (translated BLAST) tblastn (translated BLAST) tblastx (translated BLAST) page 104 Choose the BLAST program Program Input Database 1 blastn DNA DNA 1 blastp protein protein 6 blastx DNA protein 6 tblastn protein DNA 36 tblastx DNA DNA page 104 2
3 DNA potentially encodes six proteins Step 3: choose the database 5 CAT CAA 5 ATC AAC 5 TCA ACT 5 CATCAACTACAACTCCAAAGACACCCTTACACATCAACAAACCTACCCAC 3 3 GTAGTTGATGTTGAGGTTTCTGTGGGAATGTGTAGTTGTTTGGATGGGTG 5 5 GTG GGT 5 TGG GTA 5 GGG TAG nr = non-redundant (most general database) dbest = database of expressed sequence tags dbsts = database of sequence tag sites gss = genomic survey sequences protein databases page 105 nucleotide databases page 106 Step 4a: Select optional search parameters Step 4a: optional blastp search parameters Expect Word size organism Entrez! algorithm Scoring matrix Filter, mask page 107 page 108 Step 4a: optional blastn search parameters Step 4: optional parameters Expect Word size Match/mismatch scores Filter, mask You can... choose the organism to search turn filtering on/off change the substitution matrix change the expect (e) value change the word size change the output format page 108 page 106 3
4 (a) Query: human insulin NP_ Program: blastp Database: C. elegans RefSeq Default settings: Unfiltered ( composition-based statistics ) (b) Query: human insulin NP_ Program: blastp Database: C. elegans RefSeq Option: No compositional adjustment Our starting point: search human insulin against worm RefSeq proteins by blastp using default parameters page 109 Note that the bit score, Expect value, and percent identity all change with the no compositional adjustment option page 109 (c) Query: human insulin NP_ Program: blastp Database: C. elegans RefSeq Option: conditional compositional score matrix adjustment (d) Query: human insulin NP_ Program: blastp Database: C. elegans RefSeq Option: Filter low complexity regions Note that the bit score, Expect value, and percent identity all change with the compositional score matrix adjustment page 109 Note that the bit score, Expect value, and percent identity all change with the filter option page 109 (e) Query: human insulin NP_ Program: blastp Database: C. elegans RefSeq Option: Mask for lookup table only (e) Query: human insulin NP_ Program: blastp Database: C. elegans RefSeq Option: Mask for lookup table only Filtering (the filtered sequence is the query in lowercase and grayed out) page 109 Note that the bit score, Expect value, and percent identity could change with the mask for lookup table only option page 109 4
5 BLAST search output: top portion BLAST search output: taxonomy report summarizes species with matches query database program taxonomy page 112 BLAST search output: graphical output BLAST search output: tabular output High scores low E values Cut-off:.05? 10-10? page 112 page 113 BLAST search output: alignment output Outline of today s lecture BLAST Practical use Algorithm Strategies Finding distantly tl related proteins: PSI-BLAST Hidden Markov models BLAST-like tools for genomic DNA PatternHunter Megablast BLAT, BLASTZ 5
6 BLAST: background on sequence alignment BLAST: background on sequence alignment There are two main approaches to sequence alignment: [1] Global alignment (Needleman & Wunsch 1970) using dynamic programming to find optimal alignments between two sequences. (Although the alignments are optimal, the search is not exhaustive.) Gaps are permitted in the alignments, and the total lengths of both sequences are aligned (hence global ). [2] The second approach is local sequence alignment (Smith & Waterman, 1980). The alignment may contain just a portion of either sequence, and is appropriate for finding matched domains between sequences. BLAST is a heuristic approximation to local alignment. It examines only part of the search space. page 115 page 115; 84 How a BLAST search works How the original BLAST algorithm works: three phases The central idea of the BLAST algorithm is to confine attention to segment pairs that contain a word pair of length w with a score of at least T. Altschul et al. (1990) (page 115) Phase 1: compile a list of word pairs (w=3) above threshold T Example: for a human RBP query FSGTWYA (query word is in yellow) A list of words (w=3) is: FSG SGT GTW TWY WYA YSG TGT ATW SWY WFA FTG SVT GSW TWF WYS Fig page 116 Phase 1: compile a list of words (w=3) GTW 6,5,11 22 neighborhood GSW 6,1,11 18 word hits ATW 0,5,11 16 > threshold NTW 0,5,11 16 (T=11) GTY 6,5,2 13 GNW 10 neighborhood GAW 9 word hits < below threshold Fig page 116 A 4 R -1 5 N D C Q E G H I Pairwise alignment scores are determined using a scoring matrix such as Blosum62 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 Page 73 6
7 How a BLAST search works: 3 phases Phase 2: Scan the database for entries that match the compiled list. This is fast and relatively easy. How a BLAST search works: 3 phases Phase 3: when you manage to find a hit (i.e. a match between a word and a database entry), extend the hit in either direction. Keep track of the score (use a scoring matrix) Stop when the score drops below some cutoff. Fig page 116 KENFDKARFSGTWYAMAKKDPEG 50 RBP (query) MKGLDIQKVAGTWYSLAMAASD. 44 lactoglobulin (hit) extend Hit! extend page 116 How a BLAST search works: 3 phases How a BLAST search works: threshold Phase 3: In the original (1990) implementation of BLAST, hits were extended in either direction. In a 1997 refinement of BLAST, two independent hits are required. The hits must occur in close proximity to each other. With this modification, only one seventh as many extensions occur, greatly speeding the time required for a search. You can modify the threshold parameter. The default value for blastp is 11. To change it, enter -f 16 or -f 5 in the advanced options of BLAST+. (To find BLAST+ go to BLAST help download.) page 116 page 117 Phase 1: compile a list of words (w=3) Fig page 118 GTW 6,5,11 22 neighborhood GSW 6,1,11 18 word hits ATW 0,5,11 16 > threshold NTW 0,5,11 16 (T=11) GTY 6,5,2 13 GNW 10 neighborhood GAW 9 word hits < below threshold Fig page 116 7
8 How to interpret a BLAST search: expect value For blastn, the word size is typically 7, 11, or 15 (EXACT match). Changing word size is like changing threshold of proteins. w=15 gives fewer matches and is faster than w=11 or w=7. For megablast (see below), the word size is 28 and can be adjusted to 64. What will this do? Megablast is VERY fast for finding closely related DNA sequences! It is important to assess the statistical significance of search results. For global alignments, the statistics are poorly understood. For local alignments (including BLAST search results), the statistics are well understood. The scores follow an extreme value distribution (EVD) rather than a normal distribution. page 118 The probability density function of the extreme value distribution (characteristic value u=0 and decay constant =1) probabili ity normal distribution probabili ity normal distribution extreme value distribution x Fig page 119 x Fig page 119 How to interpret a BLAST search: expect value The expect value E 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. The key equation describing an E value is: E = Kmn e - S page 120 This equation is derived from a description of the extreme value distribution S = the score E = Kmn e - S E = the expect value = the number of highscoring segment pairs (HSPs) expected to occur with a score of at least S m, n = the length of two sequences, K = Karlin Altschul statistics page 120 8
9 Some properties of the equation E = Kmn e - S The value of E decreases exponentially with increasing S (higher S values correspond to better alignments). Very high scores correspond to very low E values. The E value for aligning a pair of random sequences must be negative! Otherwise, long random alignments would acquire great scores Parameter K describes the search space (database). For E=1, one match with a similar score is expected to occur by chance. For a very much larger or smaller database, you would expect E to vary accordingly page 120 From raw scores to bit scores There are two kinds of scores: raw scores (calculated from a substitution matrix) and bit scores (normalized scores) Bit scores are comparable between different searches because they are normalized to account for the use of different scoring matrices and different database sizes S = bit score = ( S -lnk) / ln2 The E value corresponding to a given bit score is: E = mn 2 -S Bit scores allow you to compare results between different database searches, even using different scoring matrices. page 121 How to interpret BLAST: E values and p values How to interpret BLAST: E values and p values The expect value E 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. A p value is a different way of representing the significance of an alignment. p = 1 - e - page 121 Very small E values are very similar to p values. E values of about 1 to 10 are far easier to interpret than corresponding p values. E p (about 0.1) (about 0.05) (about 0.001) Table 4.3 page 122 How to interpret BLAST: overview word size w = 3 10 is the E value gap penalties BLOSUM matrix threshold score = 11 length of database Fig page 122 9
10 Why set the E value to 20,000? EVD parameters Suppose you perform a search with a short query (e.g. 9 amino acids). There are not enough residues to accumulate a big score (or a small E value) = 36 m n mn Effective search space = mn = length of query x db length Fig page 122 Indeed, a match of 9 out of 9 residues could yield a small score with an E value of 100 or 200. And yet, this result could be real and of interest to you. By setting the E value cutoff to 20,000 you do not change the way the search was done, but you do change which results are reported to you. Outline of today s lecture BLAST search strategies BLAST Practical use Algorithm Strategies Finding distantly tl related proteins: PSI-BLAST Hidden Markov models BLAST-like tools for genomic DNA PatternHunter Megablast BLAT, BLASTZ General concepts How to evaluate the significance of your results How to handle too many results How to handle too few results BLAST searching with HIV-1 pol, a multidomain protein page 123 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 short, nearly exact try a reciprocal BLAST to confirm real match? Fig page 125 long, only 31% identity, similar E value Fig page
11 Assessing whether proteins are homologous The universe of lipocalins (each dot is a protein) retinol-binding protein apolipoprotein D odorant-binding protein RBP4 and PAEP: Low bit score, E value 0.49, 24% identity ( twilight zone ). But they are indeed homologous. Try a BLAST search with PAEP as a query, and find many other lipocalins. ~Fig page 126 Fig. 5.7 Page 151 BLAST search with PAEP as a query finds many other lipocalins Using human beta globin as a query, here are the blastp results searching against human RefSeq proteins (PAM30 matrix). Where is myoglobin? It s absent! We need to use PSI-BLAST. Fig page 127 Outline of today s lecture Two problems standard BLAST cannot solve BLAST Practical use Algorithm Strategies Finding distantly tl related proteins: PSI-BLAST Hidden Markov models BLAST-like tools for genomic DNA PatternHunter Megablast BLAT, BLASTZ [1] Use human beta globin as a query against human RefSeq proteins, and blastp does not find human myoglobin. This is because the two proteins are too distantly related. PSI-BLAST at NCBI as well as hidden Markov models easily solve this problem. [2] How can we search using 10,000 base pairs as a query, or even millions of base pairs? Many BLASTlike tools for genomic DNA are available such as PatternHunter, Megablast, BLAT, and BLASTZ. Page
12 Position specific iterated BLAST: PSI-BLAST The purpose of PSI-BLAST is to look deeper into the database for matches to your query protein sequence by employing a scoring matrix that is customized to your query. PSI-BLAST is performed in five steps [1] Select a query and search it against a protein database Page 146 Page 146 PSI-BLAST is performed in five steps Inspect the blastp output to identify empirical rules regarding amino acids tolerated at each position [1] Select a query and search it against a protein database [2] PSI-BLAST constructs a multiple sequence alignment then creates a profile or specialized position-specific scoring matrix (PSSM) R,I,K C D,E,T K,R,T N,L,Y,G Page 146 Fig. 5.4 Page 147 A R N D C Q E G H I L K M F P S T W Y V 1 M K W amino acids 4 V W A L L L L A A all the amino acids W A 3-2 from -1 position to 4 the A end of your PSI- 16 A BLAST query protein 37 S G T W Y A Fig. 5.5 Page 149 A R N D C Q E G H I L K M F P S T W Y V 1 M K W V W A L L L L A A W A A A S G T W Y A Fig. 5.5 Page
13 A R N D C Q E G H I L K M F P S T W Y V 1 M K W V W A L L L note -2-3 that -4 a -3 given L A amino -1-1 acid 0-2(such as A alanine) -1-1 in -2 your -2-2 query W A protein -1-2 can 4-2 receive A different scores for A matching alanine 37 S depending on -2 the G T position in -2 the -1 protein W Y A Fig. 5.5 Page 149 A R N D C Q E G H I L K M F P S T W Y V 1 M K W V W A L L L note -2-3 that -4-3 a given L A amino -1-1 acid (such -2-1 as A tryptophan) in -2 your W A query -1-2 protein can A receive different A scores for matching 37 S tryptophan G T depending on -1-1 the W position in -3 the -3-2 protein Y A Fig. 5.5 Page 149 PSI-BLAST is performed in five steps [1] Select a query and search it against a protein database [2] PSI-BLAST constructs a multiple sequence alignment then creates a profile or specialized position-specific scoring matrix (PSSM) [3] The PSSM is used as a query against the database [4] PSI-BLAST estimates statistical significance (E values) Page 146 PSI-BLAST is performed in five steps [1] Select a query and search it against a protein database [2] PSI-BLAST constructs a multiple sequence alignment then creates a profile or specialized position-specific scoring matrix (PSSM) [3] The PSSM is used as a query against the database [4] PSI-BLAST estimates statistical significance (E values) [5] Repeat steps [3] and [4] iteratively, typically 5 times. At each new search, a new profile is used as the query. Page 146 Results of a PSI-BLAST search # hits Iteration # hits > threshold Table 5-2 Page
14 PSI-BLAST search: human RBP versus RefSeq, iteration 1 PSI-BLAST search: human RBP versus RefSeq, iteration 2 See Fig. 5.6 Page 150 See Fig. 5.6 Page 150 PSI-BLAST search: human RBP versus RefSeq, iteration 3 RBP4 match to ApoD, PSI-BLAST iteration 1 E value 3e-07 See Fig. 5.6 Page 150 Fig. 5.6 Page 150 RBP4 match to ApoD, PSI-BLAST iteration 2 E value 1e-42 Note that PSI-BLAST E values can improve dramatically! RBP4 match to ApoD, PSI-BLAST iteration 3 E value 6e-34 Fig. 5.6 Page 150 Fig. 5.6 Page
15 The universe of lipocalins (each dot is a protein) Scoring matrices let you focus on the big (or small) picture retinol-binding protein apolipoprotein D odorant-binding protein retinol-binding protein Fig. 5.7 Page 151 your RBP query Fig. 5.7 Page 151 Scoring matrices let you focus on the big (or small) picture PSI-BLAST generates scoring matrices more powerful than PAM or BLOSUM PAM250 PAM30 retinol-binding g protein Blosum80 Blosum45 retinol-binding protein Fig. 5.7 Page 151 Fig. 5.7 Page 151 PSI-BLAST: performance assessment PSI-BLAST: the problem of corruption Evaluate PSI-BLAST results using a database in which protein structures have been solved and all proteins in a group share < 40% amino acid identity. PSI-BLAST is useful to detect weak but biologically meaningful relationships between proteins. The main source of false positives is the spurious amplification of sequences not related to the query. For instance, a query with a coiled-coil motif may detect thousands of other proteins with this motif that are not homologous. Once even a single spurious protein is included in a PSI-BLAST search above threshold, it will not go away. Page 150 Page
![[3] Visually inspect the output from each iteration. Remove suspicious hits by unchecking the box.](/docs-images/80/81482516/images/16-2.jpg "Page 152 Main idea: you can search a query protein against a database of position-specific scoring matrices Fig. 5.")
16 PSI-BLAST: the problem of corruption Conserved domain database (CDD) uses RPS-BLAST Corruption is defined as the presence of at least one false positive alignment with an E value < 10-4 after five iterations. Three approaches to stopping corruption: [1] Apply filtering of biased composition regions [2] Adjust E value from (default) to a lower value such as E = [3] Visually inspect the output from each iteration. Remove suspicious hits by unchecking the box. Page 152 Main idea: you can search a query protein against a database of position-specific scoring matrices Fig. 5.8 Page 153 Outline of today s lecture BLAST Practical use Algorithm Strategies Finding distantly tl related proteins: PSI-BLAST Hidden Markov models BLAST-like tools for genomic DNA PatternHunter Megablast BLAT, BLASTZ Multiple sequence alignment to profile HMMs in the 1990 s people began to see that aligning sequences to profiles gave much more information than pairwise alignment alone. Hidden Markov models (HMMs) are states that describe the probability of having a particular amino acid residue at arranged in a column of a multiple sequence alignment HMMs are probabilistic models Like a hammer is more refined than a blast, an HMM gives more sensitive alignments than traditional techniques such as progressive alignments Page 155 Simple Markov Model Simple Hidden Markov Model Rain = dog may not want to go outside Sun = dog will probably go outside 0.85 S 0.15 S P(dog goes out in rain) = 0.1 P(dog goes out in sun) = R Observation: YNNNYYNNNYN Markov condition = no dependency on anything but nearest previous state 0.2 ( memoryless ) R courtesy of Sarah Wheelan 0.2 (Y=goes out, N=doesn t go out) What is underlying reality (the hidden state chain)? 16
![Fig. 5.11 Page 157 Fig. 5.11 Page 157 Fig. 5.12 Page 158 Fig. 5.12 Page 158 HMMER: build a hidden Markov model Determining effective sequence number... done. [4] Weighting sequences heuristically.](/docs-images/80/81482516/images/17-3.jpg ".. done. Constructing model architecture... done. Converting counts to probabilities... done. Setting model name, etc.... done. [x] Constructed a profile HMM (length 230) Average score: 411.")
17 Fig Page 157 Fig Page 157 Fig Page 158 Fig Page 158 HMMER: build a hidden Markov model Determining effective sequence number... done. [4] Weighting sequences heuristically... done. Constructing model architecture... done. Converting counts to probabilities... done. Setting model name, etc.... done. [x] Constructed a profile HMM (length 230) Average score: bits Minimum score: bits Maximum score: bits Std. deviation: bits Fig Page 160 Fig Page
18 HMMER: calibrate a hidden Markov model HMM file: lipocalins.hmm Length distribution mean: 325 Length distribution s.d.: 200 Number of samples: 5000 random seed: histogram(s) saved to: [not saved] POSIX threads: HMMER: search an HMM against GenBank Scores for complete sequences (score includes all domains): Sequence Description Score E-value N gi ref XP_ (XM_129259) e ret gi sp P04916 RETB_RAT Plasma retinol e gi ref XP_ (XM_005907) sim e gi ref NP_ (NM_006744) ret e gi sp P02753 RETB_HUMAN Plasma retinol e gi ref NP_ (NC_003197) out e-90 1 gi ref NP_ : domain 1 of 1, from 1 to 195: score 454.6, E = 1.7e-131 *->mkwvmkllllaalagvfgaaerdafsvgkcrvpspprgfrvkenfdv mkwv++llllaa + +aaerd Crv+s frvkenfd+ MKWVWALLLLAA--W--AAAERD------CRVSS----FRVKENFDK gi HMM : x mu : lambda : max : Fig Page 159 erylgtwyeiakkdprferglllqdkitaeysleehgsmsataegrirvl +r++gtwy++akkdp E GL+lqd+I+Ae+S++E+G+Msata+Gr+r+L gi ARFSGTWYAMAKKDP--E-GLFLQDNIVAEFSVDETGQMSATAKGRVRLL 80 enkelcadkvgtvtqiegeasevfltadpaklklkyagvasflqpgfddy +N+++cAD+vGT+t++E dpak+k+ky+gvasflq+g+dd+ gi NNWDVCADMVGTFTDTE DPAKFKMKYWGVASFLQKGNDDH 120 Fig Page 159 PFAM is a database of HMMs and an essential resource for protein families Outline of today s lecture BLAST Practical use Algorithm Strategies Finding distantly tl related proteins: PSI-BLAST Hidden Markov models BLAST-like tools for genomic DNA PatternHunter Megablast BLAT, BLASTZ BLAST-related tools for genomic DNA BLAST-related tools for genomic DNA The analysis of genomic DNA presents special challenges: There are exons (protein-coding sequence) and introns (intervening sequences). There may be sequencing errors or polymorphisms p The comparison may between be related species (e.g. human and mouse) Recently developed tools include: MegaBLAST at NCBI. BLAT (BLAST-like alignment tool). BLAT parses an entire genomic DNA database into words (11mers), then searches them against a query. Thus it is a mirror image of the BLAST strategy. See SSAHA at Ensembl uses a similar strategy as BLAT. See Page 161 Page
19 PatternHunter MegaBLAST at NCBI --very fast --uses very large word sizes (e.g. W=28) --use it to align long, closely related sequences Fig Page 163 Fig Page 167 MegaBLAST output To access BLAT, visit Fig Page 167 BLAT on DNA is designed to quickly find sequences of 95% and greater similarity of length 40 bases or more. It may miss more divergent or shorter sequence alignments. It will find perfect sequence matches of 33 bases, and sometimes find them down to 20 bases. BLAT on proteins finds sequences of 80% and greater similarity of length 20 amino acids or more. In practice DNA BLAT works well on primates, and protein blat on land vertebrates. --BLAT website BLAT output includes browser and other formats Paste DNA or protein sequence here in the FASTA format Fig Page
20 Blastz Blastz (laj software): human versus rhesus duplication Fig Page 165 Fig Page 166 Blastz (laj software): human versus rhesus gap BLAT Fig Page 166 Fig Page 167 BLAT LAGAN Fig Page 167 Fig Page
21 SSAHA Outline of today s lecture BLAST Practical use Algorithm Strategies Finding distantly tl related proteins: PSI-BLAST Hidden Markov models BLAST-like tools for genomic DNA PatternHunter Megablast BLAT, BLASTZ Where we are in the course --We started with access to information (Chapter 2) --We next covered pairwise alignment (Chapter 3), then BLAST in which one sequence is compared to a database (Chapters 4, 5) --Next we ll describe multiple sequence alignment (Chapter 6) --We ll then visualize multiple sequence alignments as phylogenetic trees (Chapter 7). That topic spans molecular evolution. 21
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