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

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Statistical vs. Biological SignifcanceAssignmentFirst, for each search (MyHits, InterPro and BLAST hit), I would like you toreport some signifcance hits and describe why you think they aresignifcant both statistically and biologically; also report some statisticallyinsignifcant hits (and why) and are any of your statistically insignifcanthits, still signifcant biologically). To remind you what I said in class: astatistically signifcant fnd in the database search is always biologicallysignifcant, but a biologically signifcant result in the search is notnecessarily always statistically signifcant.Statistical signifcance and expectation values.Statistical signifcance is determined by the expectation value which gives youa measure of how likely this fnding is based on pure chance. A fndingwith an E-value of 1 or greater is not signifcant because it could occur bypure chance. A fnding with an E-value less than 10 -3 (one chance in athousand) is generally considered statistically signifcant (unless of courseyou are doing a 1,000 searches!). So the lower the expectation value, themore signifcant the fnding. Findings between 10 -3 and 1 are in the socalled twilight zone and require some further analysis or experiments todetermine their validity.
Statistical vs. Biological Signifcance (cont)InterProUnlike most of the other methods, InterPro sets a very high level ofsignifcance for a fnding before it will report it. This means thatyou will usually not fnd any statistically insignifcant hits for thisparticular search.Biological SignifcanceIn order to determine biological signifcance you must read thebiological properties (ontology terms are the most useful) of yourprotein and the biological properties of your fndings. Thefndings may be signifcant because the fnding defnes a veryclosely related protein family (opsins for example) or a very broadfamily (G-coupled protein receptors or 7-transmembrane proteins)or a common structure (protein fold) or a specifc function (retinalbinding site) or a very specifc catalytic activity. You shoulddescribe in words the level of the biological signifcance.
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BioinformaticsGenomics& Medicinehtt
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Computational Goals of Bioinformati
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Central Paradigm of MedicineDNA RNA
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Soybean Leghemoglobin andSperm Whal
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Challenges Understanding Genetic In
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Dayhoff’s PAM 250Amino Acid Repla
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Discovering Function from Protein S
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A Typical Motif:Zinc Finger DNA Bin
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Discovering Function from Protein S
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Position-Specifc Scoring Matrix for
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Blocks or Finger Prints fromMultipl
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Hidden Markov Models fromMultiple S
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Data Mining:The Seach for Buried Tr
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PROSITE Patternshttp://expasy.org/p
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Expasy Bioinformatics Resource Port
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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 and 42: NCBI BLAST Home Pagehttp://blast.nc
Page 43 and 44: Discovering Function from Protein S
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: Bioinformatics Homeworkhttp://bioch
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: pFAM at Sanger Center (UK)http://pf
Page 111: pFAM at Sanger Center (UK)http://pf
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