BMD645. Integration of Omics

Transcription

1 BMD645 Integration of Omics Shu-Jen Chen, Chang Gung University Dec. 11,

2 Traditional Biology vs. Systems Biology Traditional biology : Single genes or proteins Systems biology: Simultaneously study the complex interaction of many levels of biological information to understand how they work together Data typically generated using high-throughput technology, i.e. omics 2

3 Systems biology, why? Using computational strategy to study how the parts were assembled into working wholes using the comprehensive catalogs of genomic and cellular constituents 3

4 The realities in living cells: signaling pathways 4

5 The realities in living cells: cellular metabolisms Glycan biosynthesis Nucleotide metabolism Carbohydrate metabolism Amino acid metabolism Energy metabolism TCA cycle Lipid metabolism 5

6 The realities in living cells: chemical reactions Glycolysis pathways 6

7 Data are Becoming More Plentiful and More Complex 7

8 Biological systems & data Sequence data Protein folding and 3D structure Taxonomic data Literature Protein families and domains Pathways and networks Small molecules Whole genome data Biological systems 8

9 Systems biology, how? 9

10 Integrated Genomics Introduction Gene Ontology : GO Pathway Database : KEGG Pathway Mapping & Network building : MetaCore 10

11 The Genome is similar to a jigsaw puzzle 11

12 The pieces can be grouped into classes Blue Gold/Grey Brown 12

13 Groups are then organized by interaction Sky Compass Cliff/Trees 13

14 Ultimately a global picture of all interactions can be assembled 14

15 Data from high-throughput analysis randomly list thousands of genes 15

16 Genes can be grouped by biological function G protein signaling Fatty acid degradation Apoptosis 16

17 Fatty acid degradation 17

18 Fatty acid degradation 18

19 Integrated Genomics Introduction Gene Ontology : GO Pathway Database : KEGG Public domain tool : DAVID 19

20 GO Structure 20

21 How do you describe a gene? How do you identify a group of genes with related biological functions? How do you know a biological function is conserved from one species to another? 21

22 In biology Tactition Taction Tactile sense? Adopted from 22

23 Tactition Tactio n Tactile sense perception of touch ; GO: Adopted from 23

24 Bud initiation? Adopted from 24

25 = tooth bud initiation = cellular bud initiation = flower bud initiation Adopted from 25

26 GO : Gene Ontology Go Tools 26

27 What is the Gene Ontology? Genes are linked, or associated, with GO terms by trained curators at genome databases known as gene associations or GO annotations Some GO annotations created automatically genome and protein databases gene -> GO term associated genes GO database 25th June 2007 Jane Lomax 27

28 What is Gene Ontology Gene Ontology (GO) A set of controlled vocabulary that classify concepts and define the relationship between genes GO uses three hierarchical terms to describe different aspects of every protein: where, what, and why? Cellular components (where) : the location of protein activity Molecular function (what) : the biochemical activity the protein accomplishes Biological processes (why) : the overall objective toward which this protein contributions 28

29 Gene Ontology (GO) Gene Ontology (GO) Provide controlled vocabulary to describe biological knowledge for gene and gene products. Three components / subontologies in GO Example : BRCA1 Cellular component (where) Its location Cellular component Nucleus Molecular function (what) Tasks performed on the molecular level Biological process (why) How it pertains to the organism Molecular function Protein-binding Biological process DNA replication and chromosome cycle 29

30 Evidence Code of GO 30

31 The Gene Ontology is like a dictionary Each concept has: a name a definition an ID number term: transcription initiation id: GO: definition: Processes involved in the assembly of the RNA polymerase complex at the promoter region of a DNA template resulting in the subsequent synthesis of RNA from that promoter. Adopted from 31

32 There are also relationships between them. Nucleic acid binding is a type of binding. is_a is_a DNA binding is a type of nucleic acid binding. Adopted from 32

33 Ontology Structure Terms are linked by two relationships is-a part-of Adopted from 33

34 Ontology Structure cell is-a part-of membrane chloroplast mitochondrial chloroplast membrane membrane Adopted from 34

35 Ontology Structure Ontologies are structured as a hierarchical directed acyclic graph (DAG) Terms can have more than one parent and zero, one or more children Adopted from 35

36 Ontology Structure cell Directed Acyclic Graph (DAG) - multiple parentage allowed membrane chloroplast mitochondrial chloroplast membrane membrane Adopted from 36

37 GO structure GO terms divided into three parts: cellular component molecular function biological process 25th June 2007 Jane Lomax 37

38 where a gene product acts Cellular Component 25th June 2007 Jane Lomax 38

39 Cellular Component 25th June 2007 Jane Lomax 39

40 Cellular Component 25th June 2007 Jane Lomax 40

41 Cellular Component Enzyme complexes in the component ontology refer to places, not activities. 25th June 2007 Jane Lomax 41

42 Molecular Function activities or jobs of a gene product glucose-6-phosphate isomerase activity 25th June 2007 Jane Lomax 42

43 Molecular Function insulin binding insulin receptor activity 25th June 2007 Jane Lomax 43

44 Molecular Function A gene product may have several functions Sets of functions make up a biological process. 25th June 2007 Jane Lomax 44

45 Biological Process a commonly recognized series of events cell division 25th June 2007 Jane Lomax 45

46 Biological Process transcription 25th June 2007 Jane Lomax 46

47 Biological Process regulation of gluconeogenesis 25th June 2007 Jane Lomax 47

48 Biological Process limb development 25th June 2007 Jane Lomax 48

49 Biological Process courtship behavior 25th June 2007 Jane Lomax 49

50 GO for microarray analysis Annotations give function label to genes Ask meaningful questions of microarray data e.g. Do genes involved in the same process show similar or different expression patterns? 25th June 2007 Jane Lomax 50

51 Traditional analysis of omics data Gene 1 Apoptosis Cell-cell signaling Protein phosphorylation Mitosis Gene 2 Growth control Mitosis Oncogenesis Protein phosphorylation Gene 3 Growth control Mitosis Gene 4 Oncogenesis Nervous system Protein phosphorylation Pregnancy Oncogenesis Mitosis Gene 100 Positive ctrl. of cell prolif Mitosis Oncogenesis Glucose transport 25th June 2007 Jane Lomax 51

52 Using GO annotations But by using GO annotations, this work has already been done for you! GO: : apoptosis 25th June 2007 Jane Lomax 52

53 Grouping by process Apoptosis Gene 1 Gene 53 Mitosis Gene 2 Gene 5 Gene45 Gene 7 Gene 35 Glucose transport Gene 7 Gene 3 Gene 6 Positive ctrl. of cell prolif. Gene 7 Gene 3 Gene 12 Growth Gene 5 Gene 2 Gene 6 25th June 2007 Jane Lomax 53

54 Using GO in practice Statistical measure how likely your differentially regulated genes fall into that category by chance? microarray 1000 genes experiment 100 genes differentiall y regulated 10 0 mitosis apoptosis positive control of cell proliferation glucose transport mitosis 80/100 apoptosis 40/100 p. ctrl. cell prol. 30/100 glucose transp. 20/100 25th June 2007 Jane Lomax 54

55 Using GO in practice However, when you look at the distribution of all genes on the microarray: Process Genes on array # genes expected in occurred 100 random genes mitosis 800/ apoptosis 400/ p. ctrl. cell prol. 100/ glucose transp. 50/ th June 2007 Jane Lomax 55

56 Integrated Genomics Introduction Gene Ontology : GO Pathway Database : KEGG Pathway Mapping & Network building : MetaCore 56

57 Pathway Database : KEGG Kyoto Encyclopedia of Genes and Genomes 57

58 KEGG: Kyoto Encyclopedia of Genes and Genomes KEGG is a database of biological systems which contains: Genetic building blocks of genes and proteins (KEGG GENES) Chemical building blocks of both endogenous and exogenous substances (KEGG LIGAND), Molecular wiring diagrams of interaction and reaction networks (KEGG PATHWAY), Hierarchies and relationships of various biological objects (KEGG BRITE). KEGG provides a reference knowledge base for linking genomes to biological systems and also to environments by the processes of PATHWAY mapping and BRITE mapping. 58

59 KEGG database: Table of contents Total 19 databases subdivided into three categories 59

60 KEGG database: Identifier KEGG Objects 60

61 KEGG database: current statistics 61

62 Main entry sites Main database entry sites 62

63 Search by key words Search by K or ko number 63

64 Drop-down List: Metabolism pathways Total 204 metabolic pathways 64

65 Pathway description Glycolysis; fatty acid oxidation From Lehninger Biochem 65

66 Pyruvate dehydrogenase Enzymes 66

67 Pyruvate dehydrogenase 67

68 Search pathway by gene name Search by Gene Name 68

69 Object ID CDK2 related pathways 69

70 Cell Cycle Pathway 70

71 Description BRITE PATHWAY Ortholog Module 71

72 Integration 72

73 DAVID Bioinformatics Resources DAVID web server : 73

74 Analytic tools/modules in DAVID 74

75 DAVID analytic modules 75

76 Start analysis wizard Click Start Analysis from anywhere within the website 76

77 Submit gene list or use built-in demo gene lists 77

78 Select one of the DAVID Tools Gene List Manager Panel 78

79 Gene Name Batch Viewer Uer s input gene IDs Gene name translated by DAVID Click on gene name will lead to more detail info RG means Related Genes search fucntion 79

80 Gene Functional Classification Parameter panel Gene functional groups are separated by the blue rows A set of functions provided in the blue row for area for each group Gene Clusters identified by DAVID User s gene IDs & Names 80

81 2D View of Gene Function Classification Green color represents the positive association of the pair of term and gene Blank color represents the negative or no association of the pair of term and gene 81

82 Select annotation category and run Functional Annotation Chart 82

83 Select annotation category and run Functional Annotation Chart Parameter Panel Enrichment annotation Enrichment p-value Click on term name lead to details Click on blue bar to list all associated genes Click on RT to list other related terms Sort results by different columns 83

84 Select annotation category and run Functional Clustering Parameter Panel Annotation Clusters identified by DAVID Term clusters are separated by the blue rows A set of functions provided in the blue row area for each cluster 84

85 Functional Table Annotation Categories Annotation contents Header for each gene Each block separated by blue rows contains the contents for one gene A set of hyperlinks lead to more detailed descrptions 85

86 DAVID Bioinformatics Resources DAVID web server : 86

87 Databases and Tools Database Gene Ontology at KEGG pathway at BioCarta at Oncomine at PharmGKB at REACTOME at Tools for gene classification, pathway and network analysis BABELOMICS at PANTHER at GSEA at DAVID at GenMapp at Cytoscape at MetaCore at Ingenuity Pathway at 87