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Computational Molecular Biology Biochem 218 – BioMedical Informatics 231 http://biochem218.stanford.edu/

Computational Molecular Biology Biochem 218 – BioMedical Informatics 231 http://biochem218.stanford.edu/. Multiple Sequence Alignment. Doug Brutlag Professor Emeritus Biochemistry & Medicine (by courtesy). Homework 4: Sequence Alignment & Search.

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Computational Molecular Biology Biochem 218 – BioMedical Informatics 231 http://biochem218.stanford.edu/

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  1. Computational Molecular BiologyBiochem 218 – BioMedical Informatics 231http://biochem218.stanford.edu/ Multiple Sequence Alignment Doug Brutlag Professor Emeritus Biochemistry & Medicine (by courtesy)

  2. Homework 4: Sequence Alignment & Search • Part 1) Examining effect of gap penalties on sequence alignment • Please choose two proteins of interest which are only 30 to 50% identical and which have gaps in their alignment. The easiest way to find two such proteins is to choose one protein of interest to you and use it as a query in a BLAST search (either with UniProt or with NCBI BLAST) and then choose the second protein with a score 30 to 50% of the maximal score. Ensure that the alignment of these two proteins contains a few gaps. • Align your two protein sequences using SeqWeb's Bestfit or the SIM Alignment tool. Repeat the alignment with the same two sequences using the gap penalties 4, 8, 16, 32, and 64 . Keep the ratio of the gap penalty to the gap extension penalty the same in all cases (a 4 to 1 ratio is fine). • Examine the alignments and describe the effect of raising the gap penalty on the number and arrangements of gaps seen in these alignments. Mention which of the alignments has the highest overall bit score (or quality) and comment if the gaps disrupt a biologically important site (i.e. do they interrupt known functional motifs or structural features revealed by InterPro or MyHits?). Which gap penalties give the most biological alignment in your opinion. • Please show the alignments to support your conclusions.

  3. Homework 4: Sequence Alignment & Search • Part 2) Comparing Smith-Waterman, UNGAPPED and GAPPED BLAST searches. • Find a protein-family containing only 50-100 members by examining different motifs in the PROSITE database.. You should print out and keep your "gold standard family list from from UniProt. • Choose one sequence of the family as a query and use the Decypher supercomputer to perform UnGAPPED Tera-BLAST, Banded SW Tera-BLAST and standard Smith-Waterman algorithm searches of the UniProt/SwissProt database. Make sure that in each case you collect at least 100 sequences in your result set (or twice the expected number of family members). Also be sure to turn query filtering OFF (the default is ON). • Now, compare the three searches using the Receiver-Operator-Characteristic (ROC) curve. For each search, draw a line across each output list after every 10th sequence and count the number of true positives above that line and the number of false positives above that line. Remember that the gold standard determines whether a sequence is a true positive. Continue until you have collected at least 50 false positives (ROC50 curve). Finally, plot the number of True Positive sequences versus the number of False Positive sequences on a two dimensional graph for each search. • Do the three searches have identical shaped curves? Is one curve higher than the other? If so, which search is the best for your protein family? Please send the actual ROC curves (either a graphics file or an EXCEL spreadsheet with the graphs in place) to support your conclusions. • Send the results to homework218@cmgm.Stanford.EDU.

  4. Negatives Sensitivity= TP/(TP+FN) Specificity= TN/(TN+FP) Positives TN TP FN FP Evaluation of Search Algorithms

  5. 100 Area Under Curve (AUC) Number True Positives 0 0 Number False Positives 200 Evaluation of Search Algorithms withReceiver-Operator Characteristic Curve

  6. Pyruvate Dehydrogenase E1 Family (EC 1.2.4.1) http://uniprot.org/

  7. Decypher Home Pagehttp://decypher.stanford.edu/

  8. Decypher Search Inputhttp://decypher.stanford.edu/

  9. Vary the Similarity Threshold

  10. General DNA Similarity Search Principles • Search both Strands • Translate ORFs and cDNAs • Use most sensitive search possible • UnGapped BLAST for infinite gap penalty (PCR & CHIP oligos) • Gapped BLAST for most searches • Smith Waterman or megaBLAST or discontinuous MegaBLAST for cDNA/genome comparisons • cDNA =>Zero gap-length penalty • Consider using transition matrices • Ensure that expected value of score is negative • Examine results with exp. between 0.05 and 10 • Reevaluate results of borderline significance using limited query

  11. General Protein Similarity Search Principles • Chose between local or global search algorithm • Use most sensitive search algorithm available • Original BLAST for no gaps • Smith-Waterman for most flexibility • Gapped BLAST for well delimited regions • PSI-BLAST for families • Initially BLOSUM62 and default gap penalties • If no significant results, use BLOSUM30 and lower gap penalties • Examine results between exp. 0.05 and 10 for biological significance • Beware of long hits or those with unusual amino acid composition • Reevaluate results of borderline significance using limited query

  12. Goals of Multiple Sequence Alignment • Determine Consensus Sequences • Prosite Patterns • Building Gene Families • InterPro, Prints, ProDom, pFAM, DOMO, COGs, KOGs • Develop Relationships & Phylogenies • Clusters, COGs, KOGs, ClusTR • Relationships • Evolutionary Models • UPGMA, Neighbor Joining, Phylip, GrowTree, PAUP • Model Protein Structures for Threading and Fold Prediction • Profiles, Templates, HSSP, FSSP, SwissModel • Hidden Markov Models, pFAM, SAM, SuperFamily • Network Models, Neural Nets, Bayesian Networks • Statistical Models, Generalized Linear Models

  13. Consensus Sequence From aMultiple Sequence Alignment

  14. Block Maker Makes a Multiple Sequence Alignmenthttp://blocks.fhcrc.org/blocks/blockmkr/make_blocks.html PLLLWLNGGPGCSSIGYGASEEIG PLVLWFNGGPGCSSVGFGAFEELG PLMIWLTGGPGCSGLSSFVYEIGP PLMIWLTGGPGCSGLSTFLYEFGP PLLLWLSGGPGCSSLTGLLFENGP PLVLWLNGGPGCSSVAYGAAEEIG PVVIWLTGGPGCSSELALFYENGP PLVIWFNGGPGCSSLGGAFKELGP PLVIWFNGGPACSSLGGAFLELGP PLVLWLNGGPGCSSLYGAFQELGP PLVLWLNGGPGCSSIAYGASEEVG PLTLWLNGGPGCSSVGGGAFTELG NLQGYMLGNP NFMGYMVGNG NLKGFLVGNA NLKGILIGNA NLKGFAIGNG NFKGYLVGNG NLKGFIVGNP NIKGYIQGNA NLKGFMIGNA NLQGYILGNP NFKGFMVGNA NLQGYVLGNP TVKQWSGYMDYKDS GVNQYSGYLSVGSN SFAHYAGYVTVSED DFAQYAGYVTVDAA DLGHHAGYYKLPKS SVESYSGFMTVDAK GVKSYTGYLLANAT NFKQYSGYYNVGTK NFKSYSGYVDANAN NFKHYSGFFQVSDN DFFHYSGYLRAWTD TVKQYTGYLDVEDD 10-45 25-55 40

  15. Sequence Profileshttp://seqweb.stanford.edu:81/gcg-bin/analysis.cgi?program=hmmerpfam

  16. SeqWeb Sequence Profile Searchhttp://seqweb.stanford.edu:81/gcg-bin/analysis.cgi?program=hmmerpfam

  17. D 2 D 3 D 4 D 5 I 1 I 2 I 3 I 4 I 5 AA1 AA2 AA3 AA4 AA5 AA6 Hidden Markov Modelshttp://www.cse.ucsc.edu/research/compbio/sam.html

  18. 3 1 X Y 4 2 R2 Z 3 1 5 Y Z R1 4 2 X 5 1 2 X R1 R2 Z 3 Y 5 4 Evolutionary Trees

  19. Challenges Aligning Multiple Sequences • Computational complexity O(nk) for k sequences n long • Space requirements O(nk) for k sequences n long • Sequence clusters require weighting function • Weighted alignments tend to overweight erroneous sequences • Approximations must be used for real world data • Linked lists used to find exact words shared between k sequences • BLAST can find inexact shared words between k sequences • FASTA can be used to do progressive pair-wise alignments • HMM Pair models find best overall alignment probabilistically • Pairwise comparisons followed by Progressive Alignments • Final alignment is often dependent on order data presented • Gaps make alignment unnaturally long

  20. Three Protein Alignment(Murata, Richardson & Sussman)

  21. One Pairwise Alignment from theThree-Way Alignment

  22. All Pairwise Alignments from theThree-Way Alignment

  23. Carrillo-Lipman Limits for MSAhttp://searchlauncher.bcm.tmc.edu/multi-align/multi-align.html

  24. Clustal Progressive Alignment (Step 1)

  25. Clustal Progressive Alignment (Step 2)

  26. Gaps Are Propagated To Make Alignment SEQ2: MQQL SEQ1: MGQF SEQ2: MQQL SEQ3: MKQL DNPYIVRMIGICEAE-SWM DHPNIIRLEGVVTKSRPVM DNPYIVRMIGICEAESWM QHPRLVRLYAVVTQEPIY DNPYIVRMIGICEAE-SWM DHPNIIRLEGVVTKSRPVM DNPYIVRMIGICEAESWM QHPRLVRLYAVVTQEPIY SWM PIY SEQ2: MQQL-DNPYIVRMIGICEAE-SWM SEQ4: MKMIGKHKNIINLLGACTQDGPLY

  27. Clustal Procedure

  28. Clustal Dendrogram

  29. Clustal Globin Alignment

  30. ClustalW Step 1: BLOSUM Distance Matrix ClustalW Step 2: Dendrogram

  31. ClustalW Sequence Weighting

  32. ClustalW Residue Specific Gap Penalties

  33. Position Specific Gap Penalties

  34. ClustalW Step 3: Progressive Alignment

  35. T-Coffee Procedure

  36. Regular Progressive Alignment

  37. T-Coffee Primary Alignment Library

  38. T-Coffee Extended Alignment Libraryand Progressive Alignment

  39. Comparison of T-Coffee to Other MSAs

  40. MUSCLEEdgar (2004) NAR 32, 1792-1797

  41. MUSCLEEdgar (2004) NAR 32, 1792-1797

  42. MUSCLE BaliBase TestEdgar (2004) NAR 32, 1792-1797

  43. ProbConsDo, et al. Genome Research 15, 330-340

  44. ProbConsDo, et al. Genome Research 15, 330-340 • Step 1: Compute posterior probability matrices of each pair of aligned sequences from the pair-HMM model • Step 2: Compute expected accuracies of pairwise alignments. • Step 3: Probabilistic Consistency Transformation • Step 4: Calculate Guide Tree using UPGMA from measure of similarities of sequence pairs • Step 5: Progressive alignment • Step 6: refinment by dividing sequences into two groups and re-align. Repeat multiple times.

  45. ProbConsDo, et al. Genome Research 15, 330-340

  46. ProbConsDo, et al. Genome Research 15, 330-340

  47. ProbConsDo, et al. Genome Research 15, 330-340

  48. SeqWeb ClustalWhttp://seqweb.stanford.edu:81/gcg-bin/analysis.cgi?program=clustalw-prot

  49. SeqWeb ClustalW MSA Parametershttp://seqweb.stanford.edu:81/gcg-bin/analysis.cgi?program=clustalw-prot

  50. SeqWeb ClustalW Alignmenthttp://seqweb.stanford.edu:81/gcg-bin/analysis.cgi?program=clustalw-prot

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