Bioinformatics Slides (PDF) - Genomics & Medicine - Stanford ...

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NCBI BLAST Parametershttp://blast.ncbi.nlm.nih.gov/
Discovering Function from Protein SequencePSSMs or Weight MatricesPosition1 2 3 4 5 6 7 8 9 10 11 12Consensus Sequencesor Sequence MotifsZinc Finger (C2H2 type)C X{2,4} C X{12} H X{3,5} HA 2 1 3 13 10 12 67 4 13 9 1 2R 7 5 8 9 4 0 1 16 7 0 1 0N 0 8 0 1 0 0 0 2 1 1 10 0D 0 1 0 1 13 0 0 12 1 0 4 0C 0 0 1 0 0 0 0 0 0 2 2 1Q 1 1 21 8 10 0 0 7 6 0 0 2E 2 0 0 9 21 0 0 15 7 3 3 0G 9 7 1 4 0 0 8 0 0 0 46 0H 4 3 1 1 2 0 0 2 2 0 5 0I 10 0 11 1 2 10 0 4 9 3 0 16L 16 1 17 0 1 31 0 3 11 24 0 14K 3 4 5 10 11 1 1 13 10 0 5 2M 7 1 1 0 0 0 0 0 5 7 1 8F 4 0 3 0 0 4 0 0 0 10 0 0P 0 6 0 1 0 0 0 0 0 0 0 0S 1 17 0 8 3 1 3 0 2 2 2 0T 5 22 3 11 1 5 0 2 2 2 0 5W 2 0 0 0 0 0 0 0 0 1 0 1Y 1 0 4 2 0 1 0 0 2 4 0 1V 6 3 1 1 2 15 0 0 2 12 0 28Sequences ofCommonStructure or FunctionProfles, PSI-BLASTHidden Markov ModelsD 2 D 3 D 4 D 5I 1 I 2 I 3 I 4 I 5AA1 AA2 AA3 AA4 AA5 AA6Sequence Similarity10 20 30 40 501 VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHF------DLSHGS|:| :|: | |:|||| | |:||| |: : :|:| :| | |: |2 HLTPEEKSAVTALWGKV--NVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGN10 20 30 40 50
Page 1 and 2: BioinformaticsGenomics& Medicinehtt
Page 3 and 4: Computational Goals of Bioinformati
Page 5 and 6: Central Paradigm of MedicineDNA RNA
Page 9 and 10: Soybean Leghemoglobin andSperm Whal
Page 11 and 12: Challenges Understanding Genetic In
Page 13 and 14: Dayhoff’s PAM 250Amino Acid Repla
Page 15 and 16: Discovering Function from Protein S
Page 17 and 18: A Typical Motif:Zinc Finger DNA Bin
Page 19 and 20: Discovering Function from Protein S
Page 21 and 22: Position-Specifc Scoring Matrix for
Page 23 and 24: Blocks or Finger Prints fromMultipl
Page 25 and 26: Hidden Markov Models fromMultiple S
Page 27 and 28: Data Mining:The Seach for Buried Tr
Page 29 and 30: PROSITE Patternshttp://expasy.org/p
Page 31 and 32: Expasy Bioinformatics Resource Port
Page 33 and 34: UniProt Knowledge Basehttp://www.un
Page 35 and 36: UniProt Human Opsin Advanced Search
Page 37 and 38: UniProt Human Opsin OPN1MW Entryhtt
Page 39 and 40: Blast UniProt Human OPN1MW Resultsh
Page 41: NCBI BLAST Home Pagehttp://blast.nc
Page 45 and 46: Entrez Gene search for Colorblindne
Page 47 and 48: Entrez Gene search for Opsins
Page 49 and 50: BLAST Similarity Searchhttp://www.n
Page 51 and 52: Paste Sequence, Choose SwissProt Da
Page 53 and 54: BLAST Conserved Domain Output
Page 55 and 56: Most Signifcant Similarity Hits
Page 57 and 58: Bovine Blue Opsin Similarity
Page 59 and 60: Evaluation of ProflesNegative Prote
Page 61 and 62: MyHits Local Motifs Searchhttp://my
Page 63 and 64: MyHits Local Motifs Searchhttp://my
Page 65 and 66: MyHits Local Motif Hitshttp://myhit
Page 67 and 68: MyHits Local Motifs Hist (Cont.)
Page 69 and 70: InterProhttp://www.ebi.ac.uk/interp
Page 71 and 72: InterPro Scanhttp://www.ebi.ac.uk/T
Page 73 and 74: InterPro Scan Resultshttp://www.ebi
Page 75 and 76: GO: Gene Ontology Databasehttp://ww
Page 77 and 78: GO: Gene Ontology for Opsin OPN1MWh
Page 79 and 80: GO: Annotations for OPN1MWhttp://ww
Page 81 and 82: GO: Gene Ontology Terms for OPN1MWh
Page 83 and 84: GO: Gene Ontology GCPR Termhttp://w
Page 85 and 86: Bioinformatics Homeworkhttp://bioch
Page 87 and 88: Statistical vs. Biological Signifca
Page 89 and 90: Hidden Markov Models fromMultiple S
Page 91 and 92: Structure of 5’ CAP© Michael W.
Page 93 and 94:
Intron Splicing Mechanism© Michael
Page 95 and 96:
GENSCAN Gene Modelhttp://genes.mit.
Page 97 and 98:
ESTs, Full Length cDNAUniGene & Ref
Page 99 and 100:
HiddenMarkov modelsof genestructure
Page 101 and 102:
Genomics, Bioinformatics &Computati
Page 103 and 104:
Genomics, Bioinformatics &Computati
Page 105 and 106:
Hidden Markov Models (after Haussle
Page 107 and 108:
pFAM at Sanger Center (UK)http://pf
Page 109 and 110:
Page 111:
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