2. NCBI FieldGuide MapViewer Genome Resources and Sequence SimilarityLocusLink UniGene Homologene Basic Local Alignment Search Tool Gene database

3. NCBI FieldGuide Basic Local Alignment Search Tool

4. NCBI FieldGuide Topics Why use sequence similarity? BLAST algorithm –blastn, blastp, megablast BLAST statistics BLAST output Examples

5. NCBI FieldGuide Why Do We Need Sequence Similarity Searching? To identify and annotate sequences To evaluate evolutionary relationships Other: –model genomic structure (e.g., Spidey) –check primer specificity in silico : NCBI’s tool

6. NCBI FieldGuide Global vs Local Alignment Seq 1 Seq 2 Seq 1 Seq 2 Global alignment Local alignment

7. NCBI FieldGuide Global vs Local Alignment Seq1: WHEREISWALTERNOW (16aa) Seq2: HEWASHEREBUTNOWISHERE (21aa) Global Seq1:1 W--HEREISWALTERNOW 16 W HERE Seq2:1 HEWASHEREBUTNOWISHERE 21 Local Seq1: 1 W--HERE 5 W HERE W HERE Seq2: 3 WASHERE 9 Seq2: 15 WISHERE 21

8. NCBI FieldGuide Global programming algorithm

9. NCBI FieldGuide Global Dynamic Programming Full sequence must be aligned Gaps at ends are penalized as much as internal ones F(n,m) is the best score for alignment Traceback can give >1 correct alignment Used to examine closely related sequences http://www.sbc.su.se/~per/molbioinfo2001/ dynprog/dynamic.htmlhttp://www.sbc.su.se/~per/molbioinfo2001/ dynprog/dynamic.html

10. NCBI FieldGuide Local Alignment – Smith-Waterman

11. NCBI FieldGuide Local alignments - How Notice the top row and left column are now filled with 0 (if the best alignment has a negative score, it’s better to start a new one) The alignment can end anywhere in the matrix Instead of starting at F (n, m), start traceback at highest value of F (i, j); the traceback ends when you hit a 0

12. NCBI FieldGuide Heuristic alignment algorithms Shortcuts are important –Searching a sequence length of 1000 against a database with 10 8 residues requires approximately 10 11 matrix cells. At ten million matrix cells a second, it would take about 3 hours. BLAST – the heuristic is based on that true match alignments are very likely to contain somewhere within them a sort stretch of identities. Look for short stretches to serve as seeds to extend.

13. NCBI FieldGuide Seeding BLAST takes your query and breaks it down into words of fixed length (3 for protein, 11 for nucleotide) It scans through a database looking for a word from the query set with some minimum score T, when it finds it, it begins a “hit” extension to extend the possible match in both directions, stopping at the maximum scoring extension.

14. NCBI FieldGuide Extension The seeds are extended to locally optimal pairs, whose scores cannot be improved by extension or trimming. These locally optimal alignments are called high scoring segment pairs or HSP’s Sometimes you return only a portion of a sequence – this is the reason you need to look carefully at your BLAST alignments

15. NCBI FieldGuide Alignment example The quick brown fox jumps over the lazy dog. The quiet brown cat purrs when she sees him. Matches = +1; Mismatches = -1; ignore spaces and do not allow gaps. Assume the seed is the capital T, extend the alignment You’ll hit a mismatch c/e should you continue and how far? Generate a variable X to measure how far the score drops off. Set X = 5 and try the alignment… Set X = 2 and try again … A large X value will increase the speed, however, speed is often modulated by word size and other parameters…

16. NCBI FieldGuide Gapped BLAST – a time saver Extension is costly, now have a two hit (gapped) BLAST where you require two hits within a distance (A) A gapped extension takes much longer to execute than ungapped, but overall run fewer extensions – time saver Gapped BLAST requires two non-overlapping hits of at least score (T) within distance A of one another before ungapped extension of second hit T is adjustable, higher the T then the smaller the search space

17. NCBI FieldGuide Evaluation Once seeds are extended to generate alignments, these alignments are tested for statistical significance. Can establish thresholds for reporting

18. NCBI FieldGuide The Flavors of BLAST Standard BLAST –traditional “contiguous” word hit –position independent scoring –nucleotide, protein and translations (blastn, blastp, blastx, tblastn, tblastx) Megablast –optimized for large batch searches –can use discontiguous words PSI-BLAST –constructs PSSMs automatically; uses as query –very sensitive protein search RPS BLAST –searches a database of PSSMs –tool for conserved domain searches

19. NCBI FieldGuide BLASTN variations BLASTN seeds are always identical words; T is never used To make BLASTN faster, increase word size, to make it more sensitive decrease word size MegaBLAST increases word size to 28 The minimum word size is 7 http://monod.uwaterloo.ca/papers/02ph.pd fhttp://monod.uwaterloo.ca/papers/02ph.pd f

20. NCBI FieldGuide BLASTP implementation To make searches faster, set word size to 3 and T to a large value (999), which removes all potential neighborhood words (two-hit distance is 40 amino acids by default) Affine gaps –Decreased penalty for gap extension relative to gap introduction

21. NCBI FieldGuide Also, FASTA Similar to Gapped BLAST – except bigger neighborhood Generates a lookup table to locate all identically matching words of length ktup protein 1-2, DNA 4-6 Once identified, looks for diagonals with many mutually supporting word matches Extensions similar to BLAST

22. NCBI FieldGuide Scoring Matrices Scoring matrix specifies a score, s ij, for aligning sequence I with sequence II. Choice of matrix depends on the divergence level of desired/expected hits. Examples: PAM, BLOSUM Both can be modified for different divergence levels (eg, BLOSUM40, BLOSUM62) Advice: try several matrices when possible.

23. NCBI FieldGuide Dayhoff Family of Matrices Dayhoff model measures sequence evolution in units of “PAMs” –One PAM unit represents the evolutionary distance in which 1% of the amino acids have changed. Mutability of an aa is its relative rate of change (amino acids with high mutabilities are more likely to change) –Mutability of alanine was defined to be 100.

24. NCBI FieldGuide Dayhoff Family of Matrices Problems with the original Dayhoff scheme It does not consider the genetic code. –Not all amino acid substitutions can occur by a single nucleotide substitution event. Parameters were estimated from a small sample of closely related proteins. Evolution at the “average site” of the “average protein” is being modeled.

25. NCBI FieldGuide

26. BLOSUM Scoring Matrices Blocks Substitution Matrix. A substitution matrix in which scores for each position are derived from observations of the frequencies ofsubstitutions in blocks of local alignments in related proteins. Each matrix is tailored to a particular evolutionary distance. In the BLOSUM62 matrix, for example, the alignment from which scores were derived was created using sequences sharing no more than 62% identity. Sequences more identical than 62% are represented by a single sequence in the alignment so as to avoid over-weighting closely related family members. (Henikoff and Henikoff)

27. NCBI FieldGuide

28. Widely used similarity search tool Heuristic approach based on Smith Waterman algorithm Finds best local alignments Provides statistical significance All combinations (DNA/Protein) query and database. –DNA vs DNA –DNA translation vs Protein –Protein vs Protein –Protein vs DNA translation –DNA translation vs DNA translation www, standalone, and network clients Widely used similarity search tool Heuristic approach based on Smith Waterman algorithm Finds best local alignments Provides statistical significance All combinations (DNA/Protein) query and database. –DNA vs DNA –DNA translation vs Protein –Protein vs Protein –Protein vs DNA translation –DNA translation vs DNA translation www, standalone, and network clients Basic Local Alignment Search Tool

29. NCBI FieldGuide How BLAST Works Make lookup table of “words” for query Scan database for hits Ungapped extensions of hits (initial HSPs) Gapped extensions (no traceback) Gapped extensions (traceback; alignment details) Make lookup table of “words” for query Scan database for hits Ungapped extensions of hits (initial HSPs) Gapped extensions (no traceback) Gapped extensions (traceback; alignment details) X dropoff (X 1 ) X dropoff (X 2 ) X dropoff (X 3 )

30. NCBI FieldGuide Nucleotide Words GTACTGGACATGGACCCTACAGGAA Query : GTACTGGACAT TACTGGACATG ACTGGACATGG CTGGACATGGA TGGACATGGAC GGACATGGACC GACATGGACCC ACATGGACCCT Make a lookup table of words 11-mer... 828 megablast 711 blastn minimumdefaultWORD SIZE

31. NCBI FieldGuide Protein Words GTQITVEDLFYNIATRRKALKN Query : Neighborhood Words LTV, MTV, ISV, LSV, etc. GTQ TQI QIT ITV TVE VED EDL DLF... Make a lookup table of words Word size = 3 (default) Word size can only be 2 or 3 [ -f 11 = blastp default ]

32. NCBI FieldGuide Minimum Requirements for a Hit Nucleotide BLAST requires one exact match Protein BLAST requires two neighboring matches within 40 aa GTQITVEDLFYNI SEI YYN ATCGCCATGCTTAATTGGGCTT CATGCTTAATT neighborhood words one exact match two matches [ -A 40 = blastp default ]

33. NCBI FieldGuide BLASTP Summary YLS HFL Sbjct 287 LEETYAKYLHKGASYFVYLSLNMSPEQLDVNVHPSKRIVHFLYDQEI 333 Query 1 IETVYAAYLPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHFLHEESI 47 Gapped extension with trace back Query 1 IETVYAAYLPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHFLHEESI-LEV… 50 +E YA YL K F+YLSL +SP+ +DVNVHP+K VHFL+++ I + + Sbjct 287 LEETYAKYLHKGASYFVYLSLNMSPEQLDVNVHPSKRIVHFLYDQEIATSI… 337 Final HSP +E YA YL K F+YLSL +SP+ +DVNVHP+K VHFL+++ I Sbjct 287 LEETYAKYLHKGASYFVYLSLNMSPEQLDVNVHPSKRIVHFLYDQEI 333 High-scoring pair (HSP) HFL 18 HFV 15 HFS 14 HWL 13 NFL 13 DFL 12 HWV 10 etc … YLS 15 YLT 12 YVS 12 YIT 10 etc … Neighborhood words Neighborhood score threshold T (-f) =11 Query: IETVYAAYLPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHFLHEESILEV… example query words

34. NCBI FieldGuide Scoring Systems - Nucleotides A G C T A +1 –3 –3 -3 G –3 +1 –3 -3 C –3 –3 +1 -3 T –3 –3 –3 +1 Identity matrix CAGGTAGCAAGCTTGCATGTCA || |||||||||||| ||||| raw score = 19-9 = 10 CACGTAGCAAGCTTG-GTGTCA [ -r 1 -q -3 ]

35. NCBI FieldGuide Scoring Systems - Proteins Position Independent Matrices PAM Matrices (Percent Accepted Mutation) Derived from observation; small dataset of alignments Implicit model of evolution All calculated from PAM1 PAM250 widely used BLOSUM Matrices (BLOck SUbstitution Matrices) Derived from observation; large dataset of highly conserved blocks Each matrix derived separately from blocks with a defined percent identity cutoff BLOSUM62 - default matrix for BLAST Position Specific Score Matrices (PSSMs) PSI- and RPS-BLAST

36. NCBI FieldGuide A 4 R -1 5 N -2 0 6 D -2 -2 1 6 C 0 -3 -3 -3 9 Q -1 1 0 0 -3 5 E -1 0 0 2 -4 2 5 G 0 -2 0 -1 -3 -2 -2 6 H -2 0 1 -1 -3 0 0 -2 8 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 A R N D C Q E G H I L K M F P S T W Y V X BLOSUM62 Common amino acids have low weightsRare amino acids have high weights D F Negative for less likely substitutions D Y F Positive for more likely substitutions

37. NCBI FieldGuide Position-Specific Score Matrix DAF-1 Serine/Threonine protein kinases catalytic loop 174 PSSM scores 5 4

38. NCBI FieldGuide A R N D C Q E G H I L K M F P S T W Y V 435 K -1 0 0 -1 -2 3 0 3 0 -2 -2 1 -1 -1 -1 -1 -1 -1 -1 -2 436 E 0 1 0 2 -1 0 2 -1 0 -1 -1 0 0 0 -1 0 0 -1 -1 -1 437 S 0 0 -1 0 1 1 0 1 1 0 -1 0 0 0 2 0 -1 -1 0 -1 438 N -1 0 -1 -1 1 0 -1 3 3 -1 -1 1 -1 0 0 -1 -1 1 1 -1 439 K -2 1 1 -1 -2 0 -1 -2 -2 -1 -2 5 1 -2 -2 -1 -1 -2 -2 -1 440 P -2 -2 -2 -2 -3 -2 -2 -2 -2 -1 -2 -1 0 -3 7 -1 -2 -3 -1 -1 441 A 3 -2 1 -2 0 -1 0 1 -2 -2 -2 0 -1 -2 3 1 0 -3 -3 0 442 M -3 -4 -4 -4 -3 -4 -4 -5 -4 7 0 -4 1 0 -4 -4 -2 -4 -1 2 443 A 4 -4 -4 -4 0 -4 -4 -3 -4 4 -1 -4 -2 -3 -4 -1 -2 -4 -3 4 444 H -4 -2 -1 -3 -5 -2 -2 -4 10 -6 -5 -3 -4 -3 -2 -3 -4 -5 0 -5 445 R -4 8 -3 -4 0 -1 -2 -3 -2 -5 -4 0 -3 -2 -4 -3 -3 0 -4 -5 446 D -4 -4 -1 8 -6 -2 0 -3 -3 -5 -6 -3 -5 -6 -4 -2 -3 -7 -5 -5 447 I -4 -5 -6 -6 -3 -4 -5 -6 -5 3 5 -5 1 1 -5 -5 -3 -4 -3 1 448 K 0 0 1 -3 -5 -1 -1 -3 -3 -5 -5 7 -4 -5 -3 -1 -2 -5 -4 -4 449 S 0 -3 -2 -3 0 -2 -2 -3 -3 -4 -4 -2 -4 -5 2 6 2 -5 -4 -4 450 K 0 3 0 1 -5 0 0 -4 -1 -4 -3 4 -3 -2 2 1 -1 -5 -4 -4 451 N -4 -3 8 -1 -5 -2 -2 -3 -1 -6 -6 -2 -4 -5 -4 -1 -2 -6 -4 -5 452 I -3 -5 -5 -6 0 -5 -5 -6 -5 6 2 -5 2 -2 -5 -4 -3 -5 -3 3 453 M -4 -4 -6 -6 -3 -4 -5 -6 -5 0 6 -5 1 0 -5 -4 -3 -4 -3 0 454 V -3 -3 -5 -6 -3 -4 -5 -6 -5 3 3 -4 2 -2 -5 -4 -3 -5 -3 5 455 K -2 1 1 4 -5 0 -1 -2 1 -4 -2 4 -3 -2 -3 0 -1 -5 -2 -3 456 N 1 1 3 0 -4 -1 1 0 -3 -4 -4 3 -2 -5 -2 2 -2 -5 -4 -4 457 D -3 -2 5 5 -1 -1 1 -1 0 -5 -4 0 -2 -5 -1 0 -2 -6 -4 -5 458 L -3 -1 0 -3 0 -3 -2 3 -4 -2 3 0 1 1 -2 -2 -3 5 -1 -3 Position-Specific Score Matrix catalytic loop [ >./blastpgp -i NP_499868.2 -d nr -j 3 -Q NP_499868.pssm ]

39. NCBI FieldGuide Local Alignment Statistics High scores of local alignments between two random sequences follow the Extreme Value Distribution Score (S) Alignments (applies to ungapped alignments) E = Kmne - S or E = mn2 -S’ K = scale for search space = scale for scoring system S’ = bitscore = ( S - lnK)/ln2 Expect Value E = number of database hits you expect to find by chance, ≥ S your score expected number of random hits More info: www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschul-1.html www.ncbi.nlm.nih.gov/BLAST/tutorial/Altschul-1.html

40. NCBI FieldGuide Gapped Alignments  Gapping provides more biologically realistic alignments  Gapped BLAST parameters are simulated for each scoring matrix  Affine gap costs = -(a+bk) a = gap open penalty b = gap extend penalty A gap of length 1 receives the score -(a+b)

41. NCBI FieldGuide An Alignment BLAST Cannot Make 1 GAATATATGAAGACCAAGATTGCAGTCCTGCTGGCCTGAACCACGCTATTCTTGCTGTTG || | || || || | || || || || | ||| |||||| | | || | ||| | 1 GAGTGTACGATGAGCCCGAGTGTAGCAGTGAAGATCTGGACCACGGTGTACTCGTTGTCG 61 GTTACGGAACCGAGAATGGTAAAGACTACTGGATCATTAAGAACTCCTGGGGAGCCAGTT | || || || ||| || | |||||| || | |||||| ||||| | | 61 GCTATGGTGTTAAGGGTGGGAAGAAGTACTGGCTCGTCAAGAACAGCTGGGCTGAATCCT 121 GGGGTGAACAAGGTTATTTCAGGCTTGCTCGTGGTAAAAAC |||| || ||||| || || | | |||| || ||| 121 GGGGAGACCAAGGCTACATCCTTATGTCCCGTGACAACAAC 1 GAATATATGAAGACCAAGATTGCAGTCCTGCTGGCCTGAACCACGCTATTCTTGCTGTTG || | || || || | || || || || | ||| |||||| | | || | ||| | 1 GAGTGTACGATGAGCCCGAGTGTAGCAGTGAAGATCTGGACCACGGTGTACTCGTTGTCG 61 GTTACGGAACCGAGAATGGTAAAGACTACTGGATCATTAAGAACTCCTGGGGAGCCAGTT | || || || ||| || | |||||| || | |||||| ||||| | | 61 GCTATGGTGTTAAGGGTGGGAAGAAGTACTGGCTCGTCAAGAACAGCTGGGCTGAATCCT 121 GGGGTGAACAAGGTTATTTCAGGCTTGCTCGTGGTAAAAAC |||| || ||||| || || | | |||| || ||| 121 GGGGAGACCAAGGCTACATCCTTATGTCCCGTGACAACAAC Reason: no contiguous exact match of 7 bp.

42. NCBI FieldGuide BLAST 2 Sequences (blastx) output: An Alignment BLAST Can Make Solution: compare protein sequences; BLASTX Score = 290 bits (741), Expect = 7e-77 Identities = 147/331 (44%), Positives = 206/331 (61%), Gaps = 8/331 (2%) Frame = +3

43. NCBI FieldGuide Other BLAST Algorithms Megablast Discontiguous Megablast PSI-BLAST

44. NCBI FieldGuide Megablast: NCBI’s Genome Annotator Long alignments of similar DNA sequences Greedy algorithm Concatenation of query sequences Faster than blastn; less sensitive

45. NCBI FieldGuide Discontiguous Megablast Uses discontiguous word matches Better for cross-species comparisons

46. NCBI FieldGuide Discontiguous (Cross-species) MegaBLAST

47. NCBI FieldGuide Discontiguous Word Options

48. NCBI FieldGuide Templates for Discontiguous Words W = 11, t = 16, coding: 1101101101101101 W = 11, t = 16, non-coding: 1110010110110111 W = 12, t = 16, coding: 1111101101101101 W = 12, t = 16, non-coding: 1110110110110111 W = 11, t = 18, coding: 101101100101101101 W = 11, t = 18, non-coding: 111010010110010111 W = 12, t = 18, coding: 101101101101101101 W = 12, t = 18, non-coding: 111010110010110111 W = 11, t = 21, coding: 100101100101100101101 W = 11, t = 21, non-coding: 111010010100010010111 W = 12, t = 21, coding: 100101101101100101101 W = 12, t = 21, non-coding: 111010010110010010111 Reference: Ma, B, Tromp, J, Li, M. PatternHunter: faster and more sensitive homology search. Bioinformatics March, 2002; 18(3):440-5 W = word size; # matches in template t = template length

49. NCBI FieldGuide BLAST Databases: Nucleic Acid  nr (nt) traditional GenBank divisions NM_ and XM_ RefSeqs  dbest EST division  htgs HTG division  gss GSS division  chromosome NC_ RefSeqs  env_nr environmental sample[filter] e.g., 16S rRNA

50. NCBI FieldGuide BLAST Databases: Protein nr (non-redundant protein sequences)  GenBank CDS translations  NP_ RefSeqs  Outside databases PIR, Swiss-Prot, PRF  PDB (sequences from structures) env_nr (environmental sample[filter])

51. NCBI FieldGuide

53. Web BLAST: BLASTP >Mutated in Colon Cancer IETVYAAYLPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHFLHEESILE VQQHIESKLLGSNSSRMYFTQTLLPGLAGPSGEMVKSTTSLTSSSTSGS DKVYAHQMVRTDSREQKLDAFLQPLSKPLSS 1 1. Paste in the query sequence 2 2. Select the appropriate db 3. BLAST 3

54. NCBI FieldGuide Format Options

55. NCBI FieldGuide BLAST Formatting Page 102347584-927-19372.BLASTQ3

56. NCBI FieldGuide RPS-BLAST (CD search) Results Summary partial sequence partial domain

57. NCBI FieldGuide RPS-BLAST Results (CDD) DNA_mis_repair complete sequence

58. NCBI FieldGuide BLAST Output: Graphic Overview Sort results by taxonomy same database sequence

59. NCBI FieldGuide BLAST Output: Descriptions sorted by e values 8 X 10 -58 Bacterial mismatch repair proteins Linkouts E value cutoff GEO UniGene Structure

60. NCBI FieldGuide BLAST Output: Alignments >gi|127552|sp|P23367|MUTL_ECOLI DNA mismatch repair protein mutL Length = 615 Score = 44.3 bits (103), Expect = 5e-05 Identities = 25/59 (42%), Positives = 33/59 (55%), Gaps = 8/59 (13%) Query: 9 LPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHF-----LHE---ESILERVQQHIESKL 59 L + P L LEI P VDVNVHP KHEV F +H+ + +L +QQ +E+ L Sbjct: 280 LGADQQPAFVLYLEIDPHQVDVNVHPAKHEVRFHQSRLVHDFIYQGVLSVLQQQLETPL 338 >gi|127552|sp|P23367|MUTL_ECOLI DNA mismatch repair protein mutL Length = 615 Score = 44.3 bits (103), Expect = 5e-05 Identities = 25/59 (42%), Positives = 33/59 (55%), Gaps = 8/59 (13%) Query: 9 LPKNTHPFLYLSLEISPQNVDVNVHPTKHEVHF-----LHE---ESILERVQQHIESKL 59 L + P L LEI P VDVNVHP KHEV F +H+ + +L +QQ +E+ L Sbjct: 280 LGADQQPAFVLYLEIDPHQVDVNVHPAKHEVRFHQSRLVHDFIYQGVLSVLQQQLETPL 338 positive (conservative) substitution negative substitution gap

61. NCBI FieldGuide BLAST Output: Alignments & Filter low complexity sequence filtered

62. NCBI FieldGuide Advanced Options Limit to Organism protein all[filter] A Example Entrez Queries proteins all[Filter] NOT mammalia[Organism] ray finned fishes[Organism] srcdb refseq[Properties] Nucleotide only: biomol mrna[Properties] biomol genomic[Properties] OtherAdvanced –e 10000expect value -v 2000descriptions -b 2000alignments Example Entrez Queries proteins all[Filter] NOT mammalia[Organism] ray finned fishes[Organism] srcdb refseq[Properties] Nucleotide only: biomol mrna[Properties] biomol genomic[Properties] OtherAdvanced –e 10000expect value -v 2000descriptions -b 2000alignments Filter options -e 10000 -v 2000

63. NCBI FieldGuide PSI-BLAST Example: Confirming relationships of purine nucleotide metabolism proteins Position-specific Iterated BLAST

64. NCBI FieldGuide >gi|113340|sp|P03958|ADA_MOUSE ADENOSINE DEAMINASE (ADENOSINE MAQTPAFNKPKVELHVHLDGAIKPETILYFGKKRGIALPADTVEELRNIIGMDKPLSLPGF VIAGCREAIKRIAYEFVEMKAKEGVVYVEVRYSPHLLANSKVDPMPWNQTEGDVTPDDVVD EQAFGIKVRSILCCMRHQPSWSLEVLELCKKYNQKTVVAMDLAGDETIEGSSLFPGHVEAY RTVHAGEVGSPEVVREAVDILKTERVGHGYHTIEDEALYNRLLKENMHFEVCPWSSYLTGA VRFKNDKANYSLNTDDPLIFKSTLDTDYQMTKKDMGFTEEEFKRLNINAAKSSFLPEEEKK PSI-BLAST 0.005 E value cutoff for PSSM

65. NCBI FieldGuide RESULTS: Initial BLASTP Same results as protein-protein BLAST; different format

66. NCBI FieldGuide Results of First PSSM Search Other purine nucleotide metabolizing enzymes not found by ordinary BLAST

67. NCBI FieldGuide Tenth PSSM Search: Convergence Just below threshold, another nucleotide metabolism enzyme Check to add to PSSM

68. NCBI FieldGuide Reverse PSI-BLAST (RPS)-BLAST

69. NCBI FieldGuide Adenosine/AMP Deaminase Domain AMP Deaminases......

70. NCBI FieldGuide PHI-BLAST >gi|231729|sp|P30429|CED4_CAEEL CELL DEATH PROTEIN 4 MLCEIECRALSTAHTRLIHDFEPRDALTYLEGKNIFTEDHSELISKMSTRLERIANFLRIYRRQASE LIDFFNYNNQSHLADFLEDYIDFAINEPDLLRPVVIAPQFSRQMLDRKLLLGNVPKQMTCYIREYHV IKKLDEMCDLDSFFLFLHGRAGSGKSVIASQALSKSDQLIGINYDSIVWLKDSGTAPKSTFDLFTDI LKSEDDLLNFPSVEHVTSVVLKRMICNALIDRPNTLFVFDDVVQEETIRWAQELRLRCLVTTRDVEI ASQTCEFIEVTSLEIDECYDFLEAYGMPMPVGEKEEDVLNKTIELSSGNPATLMMFFKSCEPKTFEK [GA]xxxxGK[ST]

71. NCBI FieldGuide MegaBLAST vs Discontiguous MegaBLAST NM_017460 Homo sapiens cytochrome P450, family 3, subfamily A, polypeptide 4 (CYP3A4), transcript variant 1, mRNA (2768 letters) vs Drosophila

72. NCBI FieldGuide MegaBLAST vs Discontiguous MegaBLAST  MegaBLAST = “No significant similarity found.”  Discontiguous megaBLAST =

73. NCBI FieldGuide Genome BLAST

74. NCBI FieldGuide What is an HMM? Hidden Markov Model Important to know: it's a generalization of the profile in terms of statistical weights, rather than scores. At each position, the profile HMM gives the probability of finding a particular amino acid, an insertion, or a deletion HMMs are very popular in molecular data analysis but are not specific to this field

75. NCBI FieldGuide A Characterization Example How could we characterize this (hypothetical) family of nucleotide sequences? –Keep the Multiple Alignment –Try a regular expression [AT] [CG] [AC] [ACTG]* A [TG] [GC] But what about? – T G C T - - A G G vrs – A C A C - - A T C –Try a consensus sequence: A C A - - - A T C Depends on distance measure Example borrowed from Salzberg, 1998

76. NCBI FieldGuide HMMs to the rescue! Transition probabilities Emission Probabilities

77. NCBI FieldGuide Insert (Loop) States

78. NCBI FieldGuide Scoring our simple HMM #1 - “T G C T - - A G G” vrs: #2 - “A C A C - - A T C” –Regular Expression ([AT] [CG] [AC] [ACTG]* A [TG] [GC]): #1 = Member #2: Member –HMM: #1 = Score of 0.0023% #2 Score of 4.7% (Probability) #1 = Score of -0.97 #2 Score of 6.7 (Log odds)

79. NCBI FieldGuide Standard Profile HMM Architecture Three types of states: –Match –Insert –Delete One delete and one match per position in model One insert per transition in model Start and end “dummy” states Example borrowed from Cline, 1999

80. NCBI FieldGuide Aligning and Training HMMs Training from a Multiple Alignment Aligning a sequence to a model –Can be used to create an alignment –Can be used to score a sequence –Can be used to interpret a sequence Training from unaligned sequences (not included in current HMMer package)

81. NCBI FieldGuide Training from an existing alignment This process what we’ve been seeing up to this point. –Start with a predetermined number of states in your HMM. –For each position in the model, assign a column in the multiple alignment that is relatively conserved. –Emission probabilities are set according to amino acid counts in columns. –Transition probabilities are set according to how many sequences make use of a given delete or insert state.

82. NCBI FieldGuide Remember the simple example Chose six positions in model. Highlighted area was selected to be modeled by an insert due to variability.

83. NCBI FieldGuide Aligning sequences to a model Now that we have a profile model, let’s use it! Try every possible path through the model that would produce the target sequence –Keep the best one and its probability.

84. NCBI FieldGuide A T C T C - C G A A G C T - - T G G T G T T C T C T A A A C T C - C G A A G C T C - C G A Profile HMMs A 0.8 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.8 T 0.2 0.2 0.2 1.0 0.0 0.2 0.2 0.2 0.0 G 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.8 0.2 C 0.0 0.0 0.8 0.0 0.8 0.0 0.8 0.0 0.0 Probability

85. NCBI FieldGuide A.8 C 0 G 0 T.2 A.2 C 0 G.6 T.2 A 0 C.8 G 0 T.2 A 0 C 0 G 0 T 1 A 0 C.8 G 0 T.2 A 0 C 0 G.8 T.2 A.8 C 0 G.2 T 0 A 0 C.8 G 0 T.2 1.0 0.8 0.2 T T T T - T T T G........2.2102102110802080210102  TTTG TTTT Score = 8.2 x 10 -6 Consensus score = 0.1 Scores generally calculated with base e logarithms

86. NCBI FieldGuide The HMM must first be “trained” using a database of known signals. Consensus sequences for all signals are needed. Compositional rules (i.e., emission probabilities) and length distributions are necessary for content sensors. Transition probabilities between all connected states must be estimated. Pseudocounts prevent the “regular expression” problem of non-matching or zero probability of a given amino acid…

87. NCBI FieldGuide Gene Finding Software GENSCAN HMMGENE GENMARK GRAIL HMMs Neural Net

88. NCBI FieldGuide HMM resources UC Santa Cruz (David Haussler group) –SAM-02 server. Returns alignments, secondary structure predictions, HMM parameters, etc. etc. –SAM HMM building program (requires free academic license) Washington U. St. Louis (Sean Eddy group) –Pfam. Large database of precomputed HMM-based alignments of proteins –HMMer, program for building HMMs Gene finders and other HMMs (more later)

89. NCBI FieldGuide http://www.cse.ucsc.edu/research/compbio/HMM-apps/HMM-applications.html

90. NCBI FieldGuide http://hmmer.janelia.org/

91. NCBI FieldGuide http://pfam.janelia.org/