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Functional Genomics

Functional Genomics. http://www.ruf.rice.edu/~metabol/images/genotype.jpg. Winter/Spring 2011. Carol Bult, Ph.D. Course coordinator carol.bult@jax.org The Jackson Laboratory. Keith Hutchison, Ph.D. Course co-coordinator keithh@umaine.edu University of Maine. What is Functional Genomics?.

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Functional Genomics

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  1. Functional Genomics http://www.ruf.rice.edu/~metabol/images/genotype.jpg Winter/Spring 2011 Carol Bult, Ph.D. Course coordinator carol.bult@jax.org The Jackson Laboratory Keith Hutchison, Ph.D. Course co-coordinator keithh@umaine.edu University of Maine

  2. What is Functional Genomics? • A field of molecular genetics that uses genome-wide, high-throughput measurement technologies to understanding the relationships between genotype and phenotype • Genomics, epigenomics, transcriptomics, proteomics • Computational genomics (data mining) • Transgenics, targeted mutations, etc. http://en.wikipedia.org/wiki/Functional_genomics

  3. What topics will this course cover? • Primary focus: • Transcriptional profiling using microarrays • Microarray data analysis • Use of the R statistical programming language/environment • Other topics: • Genome structure and sequence variation • Epigenomics • Bio-ontologies • Proteomics • Metabolomics

  4. How will this course be structured? • Lectures and readings assigned by instructors • Assignments and discussion • Student project • Choose a microarray data set to analyze from the Gene Expression Omnibus (GEO) resource at NCBI • Do some background research on the data set • Perform an analysis of the data • Write up the analysis in the format of a scientific manuscript as if you were submitting the manuscript to PLOS Computational Biology • http://www.ploscompbiol.org/home.action • Oral presentation on the project • 15 minutes • Scheduled for April 19-28

  5. Who are the instructors? • Carol Bult (JAX), course coordinator • Microarrays, Using R • Keith Hutchison (UM), co-coordinator • Genome structure/variation • Doug Hinerfeld (JAX) • next generation sequencing and proteomics • Judith Blake (JAX) • bio-ontologies • Matt Hibbs (JAX) • mining expression data • Joel Graber (JAX) • RNA processing “In the event of disruption of normal classroom activities due to an H1N1 swine flu outbreak, the format for this course may be modified to enable completion of the course.  In that event, you will be provided an addendum to the syllabus that will supersede this version.”

  6. What resources will be used for this course? • Class Web Site • functionalgenomics.wordpress.com • R Project for Statistical Computing • http://www.r-project.org/ • Gene Expression Omnibus (GEO) @ NCBI • http://www.ncbi.nlm.nih.gov/geo/ • Gene Ontology web site • http://www.geneontology.org/ • Maine Innovation Cloud • http://www.cloud.target.maine.edu/

  7. For next time • Read about R • http://www.r-project.org/ • You might find the following link to Dr. Karl Broman’s into to R useful: • http://www.biostat.wisc.edu/~kbroman/Rintro/ • In the next week you will be given an account on the Maine Innovation Cloud which will give you access to R • Next time…Keith Hutchison will lecture on • Genome Structure/Sequence Variation

  8. Measuring Gene Expression Idea: measure the amount ofmRNAto see whichgenesare beingexpressedin (used by) the cell. Measuringproteinmight be more direct, but is currently harder.

  9. Central Assumption of Gene Expression Microarrays • The level of a given mRNA is positively correlated with the expression of the associated protein. • Higher mRNA levels mean higher protein expression, lower mRNA means lower protein expression • Other factors: • Protein degradation, mRNA degradation, polyadenylation, codon preference, translation rates, alternative splicing, translation lag…

  10. Principal Uses of Microarrays • Genome-scale gene expression analysis • Differential gene expression between two (or more) sample types • Responses to environmental factors • Disease processes (e.g. cancer) • Effects of drugs • Identification of genes associated with clinical outcomes (e.g. survival)

  11. Biological question Differentially expressed genes Sample class prediction etc. Experimental design Microarray experiment Image analysis Normalization Estimation Testing Clustering Discrimination Biological verification and interpretation

  12. Microarray example: Biomarker identification - lung cancer Samples Genes Garber, Troyanskaya et al. Diversity of gene expression in adenocarcinoma of the lung. PNAS 2001, 98(24):13784-9.

  13. Data partitioning clinically important: Patient survival for lung cancer subgroups 1 Cum. Survival (Group 1) .8 Cum. Survival (Group 2) Cum. Survival (Group 3) .6 Cum. Survival .4 .2 0 0 10 20 30 40 50 60 Time (months) p = 0.002 for Gr. 1 vs. Gr. 3 Garber, Troyanskaya et al. Diversity of gene expression in adenocarcinoma of the lung. PNAS 2001, 98(24):13784-9.

  14. Technology basics • Microarrays are composed of short, specific DNA sequences attached to a glass or silicon slide at high density • A microarray works by exploiting the ability of an mRNA molecule to bind specifically to, or hybridize, the DNA template from which it originated • RNA or DNA from the sample of interest is fluorescently-labeled so that relative or absolute abundances can be quantitatively measured

  15. Two color vs single color Bakel and Holstege. 2007. http://www.cell-press.com/misc/page?page=ETBR

  16. Other applications of microarray technology (besides measuring gene expression) • DNA copy number analysis • SNP analysis • chIP-chip (interaction data) • Competitive growth assays • …

  17. Major technologies • cDNA probes (> 200 nt), usually produced by PCR, attached to either nylon or glass supports • Oligonucleotides (25-80 nt) attached to glass support • Oligonucleotides (25-30 nt) synthesized in situ on silica wafers (Affymetrix) • Probes attached to tagged beads

  18. Probe selection Non-redundant set of probes Includes genes of interest to project Corresponds to physically available clones Chip layout Grouping of probes by function Correspondence between wells in microtiter plates and spots on the chip cDNA Microarray Design

  19. Building the chip Ngai Lab arrayer , UC Berkeley Print-tip head

  20. http://transcriptome.ens.fr/sgdb/presentation/principle.php

  21. Example dual channel cDNA array results

  22. Probes are oligos synthesized in situ using a photolithographic approach Typically there are multiple oligos per cDNA, plus an equal number of negative controls The apparatus requires a fluidics station for hybridization and a special scanner Only a single fluorochrome is used per hybridization Affymetrix GeneChips

  23. Affy There may be 5,000-100,000 probe sets per chip A probe set = 11-20 PM, MM pairs

  24. http://www.weizmann.ac.il/home/ligivol/pictures/system.jpg

  25. Interpreting Affymetrix OutputPerfect Match/Mismatch Strategy Each probe designed to be perfectly complementary to a target sequence, a partner probe is generated that is identical except for a single base mismatch in its center. These probe pairs, called the Perfect Match probe (PM) and the Mismatch probe (MM), allow the quantitation and subtraction of signals caused by non-specific cross-hybridization. The difference in hybridization signals between the partners serve as indicators of specific target abundance

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