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X4mO4KPdtEM. Genomics and Bioinformatics.

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Presentation on theme: "X4mO4KPdtEM. Genomics and Bioinformatics."— Presentation transcript:

1 X4mO4KPdtEM

2 Genomics and Bioinformatics

3 What is Functional Genomics? Functional genomics refers to the development and application of global (genome-wide or system-wide) experimental approaches to assess gene function by making use of the information and reagents provided by structural genomics. (Hieter and Boguski 1997) Functional genomics as a means of assessing phenotype differs from more classical approaches primarily with respect to the scale and automation of biological investigations. (UCDavis Genome Center)

4 Functional Genomics Hunt & Livesey (eds.) cDNA Libraries Differential Display Representational Difference Analysis Suppression Subtractive Hybridization cDNA Microarrays Serial Analysis of Gene Expression 2-D Gel Electrophoresis

5 Functional Genomics Differential Gene expression –SAGE/MPSS –*Open systems* Identifying the Function of Genes –Functional Complementation –RNA interference/RNA silencing

6 Why We Need Functional Genomics Organism# genes % of genes with inferred function Completion date of genome E. coli yeast6, C. elegans19, Drosophila12-14K Arabidopsis25, mouse~30,000? human~30,000?

7 What is the limitation of functional genomics? 5P

8 Questions Functional genomics will not replace the time- honored use of genetics, biochemistry, cell biology and structural studies in gaining a detailed understanding of biological mechanisms.

9 SAGE & MPSS Serial Analysis of Gene Expression Massively Parallel Signature Sequencing Start from mRNA (euks) Generate a short sequence tag (9-21 nt) for each mRNA ‘species’ in a cell

10 SAGE Described by Velculescu et al. (1995) Originally 9 bp tags, now LongSAGE 21 bp tags in a clone Only requires a sequencer (and some time)

11 MPSS Proprietary technology; published 2000 Generates 17 nt “signature sequence” Collects >1,000,000 signatures per sample Requires 2 µg of mRNA and $$

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22 Kamath et al ,757 strains = 86% of predicted ORFs Looked for sterility or lethality(Nonv), slow growth (Gro) or defects (Vpep) 1,722 strains (10.3% had such phenotypes)

23 Genes involved in basic metabolism & cell maintenance are enriched for Nonv phenotype Genes involved in more complex ‘metazoan’ processes (signal transduction, transcriptional regulation) are enriched for Vpep phenotype Nonv phenotypes highly underrepresented on the X chromosome X chromosome is enriched for Vpep phenotypes

24 Basal functions of eukaryotes are shared : - lethal (Nonv) genes tended to be of ancient origin - ‘animal-specific’ genes tended to be non-lethal (Vpep) - almost no ‘worm-specific’ genes were lethal

25 Interactome: proteome scale data sets of protein- protein interaction. Protein network. Methods: Yeast two hybrid, protein microarray, gene disruption phenotype, protein subcellular localization, mRNA expression profile, immunoprecipitation/mass spectrometry Problem: false positive, false negative Protein-Protein Interaction

26 Network Topology

27 Traveling Salesman Network (or Conference Site Map) Columbia, SC Boston, MA New York, NY San Francisco, LA Fort Lauderdale, FL Lincoln, NE Dallas, TX Washington DC, MD Wichita, KS Lake of the Ozarks, MS Sioux Falls, SD Orlando, FL Iowa city, Iowa Honolulu, HI Anchorage, AL Moab, UT Steamboat, CO Seattle, WA Chicago, IL Denver, CO Atlanta, GA

28 Protein-Protein Interaction: Extended Neighborhood

29 Protein-Protein Interaction: Assigning Weight on the Hub

30 Total score: n p ; maximum path length  l ; the weighting coefficient for paths of different length n l ; number of paths with length l P li ; nodes along the ith path of length l including the start and end nodes d j ; degree of the nodes

31 Database Molecular Interaction (MINT) database (Zanzoni et al., 2002) Datasets by Gavin et al (2002) and Ho et al (2002) Database of Interacting Proteins (DIP) by Salwinski et al.,

32 1.RNA polymerase II transcription process 2.To bridge between gene-specific transcription factors and the core RNAP II machinery 3.25 subunits 4.Computational and 3D structural analysis Mediator Complex

33 1.Carbon and energy source 2.Adaptation of their metabolism based on the available nutrients 3.Regulate gene expression 4.Glucose homeostasis regulates its lifespan and aging in all eukayotes 5.Snf1 protein kinase complex: key components of the glucose repression and derepression pathway Glucose Metabolism

34 1.The ultimate causes of aging are unknown 2.Multifactorial process 3.Mutation accumulation and oxidation Aging

35 SRB1 SRB9SRB10 SRB8 MED3 MED2 NUT1 MED10 MED1 MED4 MED7 SRB7 SRB2 MED3 GAL11 SIN4 RGR1 ROX3 SRB6 MED11 SRB4 SRB5 MED6 Mediator Closed Conformation

36 SRB6 ROX3 SRB4 MED6 MED11SRB2 SRB9 SRB10 SRB11 SRB8 MED3 GAL1 SIN4 MED2 RGR1 MED4 MED10 NUT1 MED7 MED8 SRB5 SRB7 MED1 Mediator Open Conformation

37 ELM1 GCN5 TUP1 DMC1 CYCB GLC7 SDS22 PAK1 TOS3 REG1 SNF4 SIP1 SNF1 SIP2 GALB3 SIP4 CAT8 MSN5 MIG1 SIT4 ACC1 SRB10 MED11 MED3 RB4 RGR1 MED1 SRB8 SRB7 MED4 SET1 SGS1 SLT2 SWI4 CRC1 SIP1 RAP1 ZDS2 ZDS1 SIR2 NET1 RAD50 SCD1 HOG1 TRK2 CYR1 HDA1 CDC25 RAS2 GPA2 GPR1 RAS1 ADA1 ESCB CDC14 HAP4 SIR3 SIR4 GAL4 SRB2 MED10 GAL11 NUT1 ROX3 MED6 MED2 SRB5 MED8 MED7 SRB9 SRB4 SIN4 SRB6 SRB11 Mediator, Glucose, and Aging Network

38 Oxidative stress Unknown Kinase Activity Tup1 Repressor Activity PAU gene Expression Plasma membrane Stress Aging Stress (cell senescence) Tor Activity Slt2 Activity Slt4 Activity Sir3 Regulated Longevity Calorie restriction Respiration Fermentation Sir2 Activity Longevity Hog1 Activity Gre2 Gene Expression Cell Wall Remodeling Stress Response pSir3 Slt2 Sit4 Osmotic stress


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