Computational Molecular Biology Biochem 218 BioMedical Informatics Genomics and Bioinformatics
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1 omputational Molecular Biology Biochem 218 BioMedical Informatics enomics and Bioinformatics Doug Brutlag rofessor meritus Biochemistry & Medicine (by courtesy)
2 aculty, Ts and taff Doug Brutlag ee Kozar Maeve O Huallachain Dan Davison
3 ourse and ideo vailability lway M114 Tuesdays & Thursdays 2:15-3:30 M ourse Web ite tanford enter for rofessional Development ideos available 24 hours/day, 7 days/week ourse offered utumn, Winter and pring quarters
4 ourse equirements ectures Theoretical background of current methods trengths and weaknesses of current approaches uture directions for improvements Demonstrations pplications (Mac,, Unix, Web) Web applications Illustrate homework ll homework and questions must be submitted by to everal homework assignments (35%) Due one week after assigned inal project (Due March 12th) critical or comparative review of computational approaches to any problem in computational molecular biology ropose new approach Implement a new approach xamples of previous projects for the class can be found at
5 David Mount Bioinformatics: equence and enome nalysis 2nd dition
6 Jin Xiong ssential Bioinformatics
7 ichard Durbin et al. Biological equence nalysis
8 Jones & evzner Bioinformatics lgorithms
9 Dan usfield lgorithms on trings, Trees & equences
10 Baldi & Brunak Bioinformatics: The Machine earning pproach
11 Higgins & Taylor Bioinformatics: equence, tructure & Databanks
12 BI Handbook
13 BI Handbook
14 MB-BI Home age
15 Berg, Tymoczko & tryer Biochemistry, ifth dition
16 Benjamin ewin enes IX
17 enomics, Bioinformatics & omputational Biology enomics tructural enomics Bioinformatics roteomics omputational Molecular Biology omputational Biology
18 enomics, Bioinformatics & omputational Biology enomics Bioinformatics ystems Biology tructural enomics roteomics omputational Molecular Biology omputational Biology
19 enomics, Bioinformatics & omputational Biology enomics Bioinformatics tructural enomics roteomics omputational Molecular Biology omputational Biology Machine earning rtificial Intelligence obotics Databases tatistics & robability lgorithms Information Theory raph Theory
20 What is Bioinformatics? Individuals rotein D henotype volution election opulations Biological Information
21 omputational oals of Bioinformatics earn & eneralize: Discover conserved patterns (models) of sequences, structures, interactions, metabolism & chemistries from well-studied examples. rediction: Infer function or structure of newly sequenced genes, genomes, proteins or proteomes from these generalizations. Organize & Integrate: Develop a systematic and genomic approach to molecular interactions, metabolism, cell signaling, gene expression imulate: Model gene expression, gene regulation, protein folding, protein-protein interaction, protein-ligand binding, catalytic function, metabolism ngineer: onstruct novel organisms or novel functions or novel regulation of genes and proteins. ene Therapy: Target specific genes, or mutations, i to change a disease phenotype.
22 entral aradigm of Molecular Biology D rotein henotype (ymptoms)
23 Molecular Biology of the ene 1965
24 entral aradigm of Bioinformatics enetic Information MHTKT WK D WT D DMK KHKK DHD KT HDKHD KTM K HKH Molecular tructure Biochemical unction henotype (ymptoms)
25 entral aradigm of Bioinformatics enetic Information MHTKT WK D WT D DMK KHKK DHD KT HDKHD KTM K HKH Molecular tructure Biochemical unction henotype (ymptoms)
26 hallenges Understanding enetic Information enetic Information Molecular tructure Biochemical unction henotype enetic information is redundant tructural information is redundant enes and proteins are meta-stable ingle genes have multiple functions enes are one dimensional but function depends on three-dimensional structure
27 edundancy in enomic & rotein equences D is double-stranded enetic code cceptable amino-acid replacements Intron-exon variation lternative splicing train variations (s) equencing errors
28 Using ontrolled ocabulary for iterature earch
29 ene Ontology Database
30 U enome Browser
31 xy roteomics erver
32 Inferring Biological unction from rotein equence onsensus equences or equence Motifs Zinc inger (2H2 type) x {2,4} x {12} H x {3,5} H equences of ommon tructure or unction equence imilarity uery DKTKWKHMTTKTH------DH : : : : : : : : : : : Match HTKTWK--DWTDTDM
33 Typical Motif: Zinc inger D Binding Motif...H...H
34 Inferring Biological unction from rotein equence Weight Matrices or coring Matrices osition-pecific D H I K M T W onsensus equences or equence Motifs Zinc inger (2H2 type) x {2,4} x {12} H x {3,5} H rofiles, I-BT equences of ommon Hidden Markov Models tructure or unction D2 D3 D4 D5 I1 I2 I3 I4 I equence imilarity uery DKTKWKHMTTKTH------DH : : : : : : : : : : : Match HTKTWK--DWTDTDM
35 Buried Treasure
36 Buried Treasure
37 Buried Treasure
38 lustal lobin lignment
39 onsensus equence rom a Multiple equence lignment lustalw Insulin lignments 10 I IDK ID IH I IBO I M WM W T M W I - T M W I M W W. W.. 40 I IDK ID IH I IBO I H H H H H H H D D T I IDK ID IH I IBO I D D D T K K I IDK ID IH I IBO I M I M. X X K K K K K K X X K - - I I I I I I I I D D D T M D T D T T H H H K T I T T I T T. K K K K K K K K D T. X X D D X X D D D D D D K K D D H H M H D I I H H H H H H H H H 120
40 HMM Model of Hemoglobins
41 rowtree eg eighbor Joining Tree
42 Human ene xpression ignatures T ells ignaling D Damage ibroblast timulation B ells ignaling M Infection noxia olio Infection Monocytes ignaling I4 Hormone
43 lustering ene xpression rofiles: omparison of Methods D'haeseleer (2005). at Biotechnol. 23,
44 TMO: Tools for the nalysis of Motifs
45 inding Transcription actor Binding ites Upstream egions expressed oenes ho 5 TTTTT...TTT ho 8 TTTT...T ho 81 TTTTT...TT ho 84 TTTTTT...T ho TTTTT...TT Transcription tart
46 inding Transcription actor Binding ites Upstream egions o-expressed enes TTTTT...TTT TTTT...T TTTTT...TT TTTTTT...T TTTTTT...TTT
47 inding Transcription actor Binding ites Upstream egions o-expressed enes TTTTTT...TTT TTTT...T TTTTT...TT TTTT...T TTTTTTT...TTT ho4 binding
48 Metabolic etworks: Bioyc
49 . crescentus ell ycle ene xpression
50 enome Wide ssociations in heumatoid rthritis earson, T.. et al. JM 2008;299:
51 everaging enomic Information in Medicine ovel Diagnostics Microchips & Microarrays - D ene xpression - roteomics - rotein ovel Therapeutics Drug Target Discovery ational Drug Design Molecular Docking ene Therapy tem ell Therapy Understanding Metabolism Understanding Disease Inherited Diseases - OMIM Infectious Diseases athogenic Bacteria iruses
Computational Molecular Biology (
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Multiple Sequence Alignment
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