111/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics BCB 444/544 Lecture 35 A bit more Comparative Genomics Functional Genomics (Microarrays) #35_Nov14 211/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Mon Nov 12 - Lecture 34 Comparative Genomics Chp 17 Wed Nov 14 - Lecture 35 Functional Genomics Chp 18 Thurs Nov 15 - Lab 11 Microarray Analysis Fri Nov 16 - Lecture 36 Proteomics Chp 19 Required Reading (before lecture) 311/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Assignments & Announcements Mon Nov 12 - HW#6 (was finally posted on MON) HW#6 - Fun with SNPs, Comparative Genomics & Gene Annotation!! Due: whenever (sometime before 5 PM Mon Nov 26) 411/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Seminars this Week BCB List of URLs for Seminars related to Bioinformatics:Nov 12 Mon - Math Seminar 4:10 in 294 Carver Trachette Jackson Univ of Michigan Mathematical Modeling of Angiogenesis in Cancer Nov 14 Wed - ISU ADVANCE Brown Bag Lunch noon 240 Bessey Making a Career in STEM: Three Women's Stories Nov 15 Thurs - Center for Excellence in Arts & Humanities Symposium 9:30-11:30 & 3-5 Cardinal Room, MU L Andrews,T Duster, J Murray & K Taussig Ethical, Philosophical, and Legal Issues of Genomic Research Nov 16 Fri - BCB Faculty Seminar 2:10 in 102 SciI Karin Dorman ISU Modeling HIV Recombination - Hotspots? 511/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics In the News: Bioinformatics/Genomics 1. REWARD: X Prize Foundation $10 MillionX Prize Foundation for sequencing 100 human genomes in 10 days 2.Science cover article this week:Science cover article Capillary sequencing of tumor cell genomes (65 of 4 million sequencing reactions are shown) compare all genes in specific tumor cell types Result? lots of SNPs and other mutations Surprise: many mutations in genes not normally considered "oncogenes" or "tumor suppressors" or "cell cycle" or "apoptosis"-related 3. DNA Computing - Interesting papers provided by Erin Interesting papers provided by Erin (see class website 2006 for additional links) 611/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Chp 17 Comparative Genomics SECTION V GENOMICS & PROTEOMICS Xiong: Chp 17 Genome Mapping, Assembly & Comparison Genome Mapping Genome Sequencing Genome Sequence Assembly Genome Annotation Comparative Genomics 711/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Genomics - for excellent overview lectures, see these posted by NHGRI & Pevsner :NHGRIPevsner 1- Genomic sequencing Mapping and Sequencing CTGA2005Lecture1.pdfCTGA2005Lecture1.pdf Eric Green, NHGRI 2- Human genome project The Human Genome _ch17.pdf _ch17.pdf Jonathan Pevsner, Kennedy Krieger Institute 3- SNPs Studying Genetic Variation II: Computational Techniques Jim Mullikin, NHGRI TGA2005Lecture13.pdfTGA2005Lecture13.pdf 4- Comparative Genomics Comparative Sequence Analysis Elliott Margulies, NHGRI CTGA2005Lecture8.pdfCTGA2005Lecture8.pdf 811/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Recent technologies? Pyro-Sequencing 911/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Massively Parallel Sequencing: 454 1011/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Massively Parallel Sequencing: 454 at ISU? 1111/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Genome Assembly at ISU? Huang (ComS) & Chou (ComS/GDCB) - designed assembly software used at Celera, TIGR, etc. Aluru (ECprE) & Schnable (Agron/GDCB) - parallel implementations of assembly software Dickerson (ECprE), Wise (PlPath/USDA) - & many others = ISU computational & experimental experts with large scale genome assembly research focus Kalyanaraman A, Emrich SJ, Schnable PS, Aluru S (2007) Assembling genomes on large-scale parallel computers. Journal of Parallel and Distributed Computing. in press Emrich SJ, Kalyanaraman A, Aluru S (2005) Algorithms for large-scale clustering and assembly of biological sequence data. Handbook of Computational Molecular Biology, Chapman & Hall/CRC press. 1211/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics ENCODE - Results? June 2007 1311/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Was It Really Worth all that $$? & Who Owns it Now??? 1411/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Speaking of Craig Venter - Where is the Cutting-Edge in Sequencing Technology? 1511/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Pevsner 2005 Human Genome Project: What have we learned? 20, , 1611/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Pevsner 2005 Lots of SNPs: single nucleotide polymorphisms 1711/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Mullikin 2005 SNPs: Single Nucleotide Polymorphisms 1811/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Mullikin 2005 SNPs: Single Nucleotide Polymorphisms 1911/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Mullikin 2005 SNP Discovery Methods & 454 Sequencing! 2011/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Mullikin 2005 SNPs: Access via 3 Major Genome Browsers 2111/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Mullikin 2005 Haplotype - What is it? 2211/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Mullikin 2005 Haplotypes: an example 2311/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Haplotypes: Two definitions 2411/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Haplotypes: a better explanation! 2511/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Hapmap Project 2611/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Mullikin 2005 HapMap Project Goals 2711/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics HapMap Results: 2811/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Many human traits & diseases are polygenic = determined by multiple genes QTL = Quantitative Trait Locus - genetic locus (gene) that contributes to a polygenic trait & that can be measure in some quantitative manner Examples? Obesity - (in pigs & humans!) Intelligence Schizophrenia Alcoholism So - understanding such traits requires understanding "natural" variation at multiple loci - it is complex! Why are SNPs/HapMap Important? (for humans?) 2911/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Fig 4.10 Lung cancer drug Iressa cures only 10% of treated patients - but it saves those! CAT scans of a single patient over 2 years Copyright 2006 A. Malcolm Campbell An example from Pharmocogenomics 3011/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Fig 4.15 Continuum of Utility of a Particular Genetic Test Developing Effective Treatments Requires Balance between Efficacy & Toxicity & these depend both on genetics and environment & tough Ethical Issues arise 3111/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics J Mullikin 2005 Significance of SNP Analyses 3211/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Fig 4.1 Diatom Bloom Study Copyright 2006 A. Malcolm Campbell Light micrographs of two Ditylum brightwellii cells Other implications? What is extent of diatom genetic diversity in oceans - and what effect might this have on global CO 2 levels & global warming? 3311/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics The Claim: "Give me half a tanker of iron & I'll give you an ice age" Rationale: Iron is limiting in the ocean; give diatoms lots of it & cause a diatom "bloom," this will increase CO 2 fixation (lots removed from atmosphere), resulting in decrease in global temperature >> Global warming cured! Test: Spring authors, international effort: Iron-induced bloom lasted only 18 days Much of sequestered carbon did not sink to deep ocean, but was recycled through predation or decomposition by bacteria, which could actually lead to increase in atmospheric CO 2 !! Moral: Perhaps we should understand the dynamics of oceans before conducting such experiments on a global scale!! We must be cautious when devising solutions to complex problems such as global warming!! Copyright 2006 A. Malcolm Campbell Diatoms & Global Warming? 3411/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics 4- Comparative Genomics Many thanks to: Elliott Margulies, NHGRI for the following slides extracted from his lecture on: Comparative Sequence Analysis CTGA2005Lecture8.pdf CTGA2005Lecture8.pdf 3511/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics E Margulies 2005 3611/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics E Margulies 2005 Comparative Genomics 3711/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics E Margulies 2005 Comparative Genomics Provides Important Clues re: Biological Function of Genes 3811/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics E Margulies 2005 Different Terms are used to Describe Different types of Conserved Sequences 3911/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics E Margulies 2005 Sequence Comparisons Whole Genome Alignments! 4011/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics E Margulies 2005 Two Major Visualization Tools: 4111/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics E Margulies 2005 zPicture: Best of Both Tools: 4211/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Comparing Multiple Species with zPicture 4311/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics The Comparative Genomics Company? 4411/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics E Margulies 2005 What Have We Learned from Comparative Genomics? An early example: 4511/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics What Have We Learned from Comparative Genomics? A more recent example: Re: Pollard KS, Haussler D. (2006) An RNA gene expressed during cortical development evolved rapidly in humans.Re: Pollard KS, Haussler D. (2006) An RNA gene expressed during cortical development evolved rapidly in humans. Nature 443: PDFPDF 4611/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics ISU Resources & Experts (a few of them) Genomic sequencing, Genotyping, Comparative genomics Facilities: ISU Biotech DNA Facility PSI Carver Co-Lab Experiments: Microbial: Minion, others Plant: Schnable, Wise, Bogdonave, many others Animal: Rothschild, Tuggle, Reecy, Lamont, many others Assembly & Analysis: Huang, Chou, Brendel, Proulx, Gu 4711/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Chp 18 Functional Genomics SECTION V GENOMICS & PROTEOMICS Xiong: Chp 18 Functional Genomics Sequence-based Approaches Microarray-based Approaches Comparison of SAGE & DNA Microarrays 4811/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Transcriptome = complete collection of all RNAs in a cell at a given time High-throughput analysis of RNA expression: Microarrays - "Gene Chips" most popular Other related methods: SAGE = Serial Analysis of Gene Expression MPSS = Massively Parallel Signature Sequencing Transcriptome Analysis 4911/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Very powerful technology to evaluate global changes in gene expression Applications in medicine, genetics, evolution, ecology, animal breeding, plant stress, homeland security! Many recent developments & variations: DNA chips protein chips carbohydrate chips antibody chips,antigen chips cell chips whole body chips?? Microarray Analysis - What's the big deal? 5011/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Which RNAs are detected? mRNAs (& pre-RNAs) alternatively spliced mRNAs rRNAs, tRNAs miRNAs, siRNAs, other regulatory RNAs 2 Major Types of Microarrays: cDNA = "spotted" = low density, glass slides = Southern blot on a slide oligo = "DNA chip" = high density, photolithography "Affy" chip; computationally designed Both types can be made here, in ISU facilities Microarray Analysis 5111/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics A cDNA Microarray Each purple spot = one PCR product; on a real microarray each spot is ~100 microns in diameter Copyright 2006 A. Malcolm Campbell 5211/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Production of cDNA probes for a DNA chip a) From populations of cells grown under two different conditions, mRNA is isolated and copied into cDNA (left= Red; right = Green) b) Red & Green cDNAs are mixed, placed on the chip, covered by a glass coverslip and incubated overnight with the DNA microarray Copyright 2006 A. Malcolm Campbell 5311/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Measuring fluorescence on a cDNA chip 3 different genes out of 6,200 available on this chip are shown. Top spot shows the merged image (ratio of 635 nm:532 nm) Middle spot shows the red (635 nm) channel only Bottom spot shows the green (532 nm) channel only Some merged images will look a) more red than green, b) more green than red, c) about equal red and green Copyright 2006 A. Malcolm Campbell 5411/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Results from a single DNA chip a)Red transcriptome b)Green transcriptome both (yellow) c)Genes expressed in both (yellow) transcriptomes Genes not expressed in either condition (gray) Copyright 2006 A. Malcolm Campbell 5511/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Green-red color scale for changes in transcription Copyright 2006 A. Malcolm Campbell Black = Genes transcribed equally in both conditions Red = Induced genes (transcription increased) Green= Repressed genes (transcription decreased) Hmmm, I think this color scheme seems "backwards" 5611/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Comparison of Northern Blots with cDNA Microarray Data Copyright 2006 A. Malcolm Campbell a) 4 individual Northern blots for 4 different genes, measuring mRNA accumulation over time b) A series of microarray results for the same 4 genes of interest. Scale on the bottom indicates a 20-fold repression (bright green) and 20-fold induction (bright red). Black indicates no change in transcription (i.e., the merged microarray spot would have appeared yellow). 5711/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics Microarray Facilities: Center for Plant GenomicsCenter for Plant Genomics (ISU PSI) - Pat Schnable in Carver Co-Lab GeneChip Facility (ISU Biotech & PSI) - Steve WhithamGeneChip Facility in MBB Research Labs: Pat Schnable (Agron/GDCB) - Facilities for cDNA microarrays Steve Whitham (PlPath) - Facilities for oligo microarrays Google "microarrays" from ISU website>>> Lots more: Jo Anne Powell-Coffman, GDCB: genes induced under oxidative stress Roger Wise, Rico Caldo, Plant Pathology: interaction between multiple isolates of powdery mildew and multiple genotypes of barley Chris Tuggle, Animal Science: genes controlling mammalian embryo development ISU Microarray Researchers & Facilities 5811/14/07BCB 444/544 F07 ISU Dobbs #35 - Functional Genomics ISU Microarray Design & Analysis Experimental Design is critical (ISU Course: Statistical Design & Analysis of Microarray Experiments) Hui-Hsien Chou (Com S) - "Picky" software for designing oligos Dan Nettleton (Stat) - Experimental design & statistical analyses Di Cook (Stat) "exploRase" software for high-dimensional data analysis & visualization for systems biology Tools from Statistics & Machine Learning are needed ISU Experts: Dan Nettleton & Di Cook, Stat Vasant Honavar, Com S Statistics: ANOVA (Analysis of Variance) R Statistics package ML: Clustering & Classification Algorithms WEKA package GEPAS Many additional resources & tools available online ISU has several Microarray Analysis SuiteS