WEHI Postgraduate Seminar Series 2003 Tools for Maximising the Value of Genomic Data Keith Satterley, Bioinformatics, The Walter & Eliza Hall Institute of Medical research 2nd. June 2003 [email protected] http://bioinf.wehi.edu.au/resources/presentations.html Overview 1. Genomic data – what is it, where is it 1. Gene Finding 1. 2. GenScan Comparitive Genomics 1. Gene Finding 1. 2. 3. Finding Regulatory Regions 1. 2. 3. 2. rVista Consite Toucan Programming Tools 1. Languages 1. 2. 3. 4. 2. 3. 4. Slam Twinscan Perl BioPerl BioJava Bio??? Slipper-a Perl program & results Link References Aknowledgements 1953 2003 http://www.geneticscongress2003.com/index.php • Genomic data – Whole genome data sets. According to http://www.ebi.ac.uk/genomes/ as at 28-May-03 • • • • • • • Archea – 16 Bacteria – 107 Organelles – 308 Phages – 112 Plasmids – 280 Viroids – 40 Viruses – 880 • TOTAL:1743 Eukaryota (completed chromosomes) Description Chromosomes Anopheles gambiae: Ensembl project data 2L 2R 3L 3R X MUSTARD: Arabidopsis thaliana complete genome: I II III IV V I II III IV V Proteome pages WORM:Caenorhabditis elegans complete genome I II III IV V X FastA Proteome pages FLY: Drosophila melanogaster complete genome X,2-4,Y FastA Proteome pages Encephalitozoon cuniculi complete genome I II III IV V VI VII VIII IX X XI I II III IV V VI VII VIII IX X XI HUMAN:Homo sapiens complete genome: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X Y Proteome pages source MIPS Ensembl project data Homo sapiens complete genome parts: CON files 14 21q Leishmania major 1 MOUSE:Mus musculus complete genome: Ensembl 123456 7 8 9 10 11 12 13 14 15 16 17 18 19 X project data Oryza sativa 1 Plasmodium falciparum 1 2 3 4 5 6 7 8 9 10 11 12 13 14 RAT:Rattus norvegicus complete genome: 123456 7 8 9 10 11 12 13 14 15 16 17 18 19 20 X Ensembl project data YEAST:Saccharomyces cerevisiae strain S288C complete genome YEAST:Schizosaccharomyces pombe strain 972hcomplete genome Trypanosoma brucei http://www.ebi.ac.uk/genomes/ Proteins Proteome pages I-XVI FastA Proteome pages I-III I II III Proteome pages 1 http://gnn.tigr.org/sequenced_genomes/genome_guide_p1.shtml gnn.tigr.org GOLD – Genomes Online Database http://www.genomesonline.org/ It’s a Fact: Count @ 1 base per second, 24 hours a day, It would take you about 95 years to count the DNA in one cell. Francis Collins Director, National Human Genome Research Institute 25th. April 2003 “Here in the very month of the 50th anniversary of the discovery of DNA’s double helix, I am pleased and honored — perhaps I should say exhilarated — to declare the goals of the Human Genome Project to be completed.” 3.1 Million years To count to 100 Trillion! “..the information that will matter to you about your life is a fraction of your genetic code — probably less than 1 percent.” J.Craig Venter, 25-04-2003(Bio-IT World) http://www.genomesonline.org/ Most Recent Genomics News BETHESDA, Md., May 20, 2003 By June, researchers from the Whitehead/MIT Center and the Genome Sequencing Center at Washington University School of Medicine expect to complete the sequencing work (approximately four-fold coverage) necessary to create an initial working draft of the genome of the chimpanzee (Pan troglodytes). The Whitehead/MIT team expects to complete a high-quality draft of the dog genome sequence within the next 12 months. After the genome of the boxer is sequenced, researchers plan to sample and analyze DNA from 10 to 20 other dog breeds, including the beagle, to study genetic variation within the canine species. http://www.genome.gov/11007358 Gene Finding • Gene finding is about detecting coding regions and inferring gene structure. • Gene finding is difficult. • DNA sequence signals have low information content (degenerated and highly unspecific) • It is difficult to discriminate real signals • Sequencing errors • Prokaryotes: High gene density and simple gene structure, Short genes have little information, Overlapping genes. • Eukaryotes: Low gene density and complex gene structure Alternative splicing, Pseudo-genes. Gene Finding A Good Gene Finding Review has been prepared by Lorenzo Cerutti of the Swiss Institute of Bioinformatics. It is an EMBNet course, (September 2002) entitled “Gene Finding”. It is at: http://www.ch.embnet.org/CoursEMBnet/Pages02/slides/gene_finding.pdf Gene Finders • GenScan - Uses generalized hidden Markov models to predict complete gene structure http://genes.mit.edu/GENSCAN.html • MZEF - Designed to predict only internal coding exons. • http://www.cshl.org/genefinder FGENES – Uses linear discriminant analysis. http://genomic.sanger.ac.uk/gf/gf.shtml • GeneFinder: http://www.cshl.org/genefinder • GRAIL 1,1a,2 http://compbio.ornl.gov • HMMgene - Designed to predict complete gene structure. http://genome.cbs.dtu.dk/services/HMMgene • Genewise - Uses HMMs. Genewise is part of the Wise2 package: http://www.sanger.ac.uk/Software/Wise2. • Procrustes - Predicts gene structure from homology found in proteins. http://hto-13.usc.edu/software/procrustes/index.html • GeneMark.hmm. Recently modified to predict gene structure in eukaryotes. http://opal.biology.gatech.edu/GeneMark • Geneid. Recently updated to a new and faster version. http://www1.imim.es/geneid.html Gene Finders Gene Finders 1. Overall performances are the best for HMMgene and GENSCAN. 2. Some program’s accuracy depends on the G+C content, except for HMMgene and GENSCAN, which use different parameters sets for different G+C contents. 3. For almost all the tested programs, ”medium” exons (70-200 nucleotides long), are most accurately predicted. Accuracy decrease for shorter and longer exons, except for HMMgene. 4. Internal exons are much more likely to be correctly predicted (weakness of the start/stop codon detection). 5. Initial and terminal exons are most likely to be missed completely. 6. Only HMMgene and GENSCAN have reliable scores for exon prediction. Gene prediction limits 1. 2. 3. 4. 5. 6. 7. Existing predictors are for protein coding regions Non-coding areas are not detected (5’ and 3’ UTR) Non-coding RNA genes are missed Predictions are for ”typical” genes Partial genes are often missed Training sets may be biased Atypical genes use other grammars GenScan • GENSCAN was developed by Chris Burge and Samuel Karlin, Department of Mathematics, Stanford University • Genscan is a general probabilistic model of the gene structure of human genomic sequences. • Genscan identifies complete exon/intron structures of genes in both strands of genomic DNA. • The new Genscan Web Server is at http://genes.mit.edu/GENSCAN.html • Genscan is also available for WEHI people at http://www.wehi.edu.au/resources/PBC/index.html with a greater choice of options. Prediction of Complete Gene Structures in Human Genomic DNA. J. Mol. Biol. (1997) 268, 78-94 Comparitive Genomics Quotes from the 50/50 series of interviews by Bio-IT World Gene Myers Professor, Dept. of Electrical Engineering & Computer Sciences University of California, Berkeley . “If you take a sequence and just run a gene prediction program on it, the programs don’t usually do very well. But if you take human and mouse sequence, and compare them against each other — looking for similar regions — you get better predictions. And the more genomes we have, the better it will get.” Quotes from the 50/50 series of interviews by Bio-IT World Richard Durbin Head of Informatics, Wellcome Trust Sanger Institute. • “Looking at the similarity between the human genome and other species is a really powerful way to get at functional sequences and to allow us to work on them in different species.” • “Several groups, including ours, have gene-finding methods for comparative genomics. This is an active area where we will see significant advances in the next few years.” Comparative Genomics • The Assumption that underlies comparitive genomics is that the two genomes had a common ancestor and that each organism is a combination of the ancestor and the action of evolution. • Evolution can be broadly thought of as the combination of two processes: mutational forces that generate random mutations in the genome sequence, and selection pressures that 1. Eliminate random mutations (negative selection), 2. Have no effect on mutations (neutral selection) or, 2. Increase the frequency of mutant alleles in the population as a result of a gain in fitness (positive selection). • The combined action of mutation and selection is represented generally by a RATE MATRIX of base-pair changes between the two observed genomes. Human Comparative Genomics Mouse Rat Evolutionary relationship between metazoans that are sequenced, or due for sequencing. Evolutionary distances are in millions of years. C.Elegans Comparitive Genomics • Comparative genomics may be defined as the derivation of genomic information following comparison of the information content of 2 or more species genome sequences • There is a good article in Nature Genetics Reviews, April 2003 Vol 4 No 4,pp251-262. “Comparative Genomics: Genomice-Wide analysis in Metazoan Eukaryotes”, Ureta-Vidal, A. Laurence Ettwiller & Ewan Birney 2003 • http://www.nature.com/cgi-taf/DynaPage.taf?file=/nrg/journal/v4/n4/full/nrg1043_fs.html The similarity is such that human chromosomes can be cut (schematically at least) into about 150 pieces (only about 100 are large enough to appear here), then reassembled into a reasonable approximation of the mouse genome. http://www.ornl.gov/TechResources/Human_Genome/graphics/slides/ttmousehuman.html Comparitive Genomics • …there has been an explosion in the availability of tools which may make it difficult to decide which tool is most suitable for your research. • Indeed, to interpret these resources, you must be aware of the differences between them and between their underlying assumptions. Whole Genome Alignments K-browser http://hanuman.math.berkeley.edu/cgi-bin/kbrowser A multiple genome browser, currently set up for human, mouse and rat based on the MAVID alignments, UCSC genome browser. Comparative Gene Prediction SLAM http://baboon.math.berkeley.edu/~syntenic/slam.html Example of a comparative genefinder Employs a generalised pair hidden Markov model approach for predicting gene structures within syntenic genomic sequences Performing gene finding and alignment of the sequences simultaneously SLAM SLAM has been used for whole genome annotation projects. For the Mouse/Human analysis, SLAM used a human/mouse sytenny map, giving segments which are further broken up into 300kb pieces. These pieces are aligned by AVID . SLAM then ran on all syntenic pieces using AVID alignments as guides. Coding lengths < 120 were discarded. SLAM also predicted conserved non coding regions(CNS), the first de novo prediction of CNS in the human and mouse genome. The results are available at http://bio.math.berkeley.edu/slam/mouse/ A similar result is available for Human/Rat. seq1 SLAM CDS 2421 2478 . + 2 gene_id "000001"; transcript_id "000001.1"; frame "1"; exontype "internal" seq1 SLAM CDS 3127 3805 . + 1 gene_id "000001"; transcript_id "000001.1"; frame "1"; exontype "internal" -------------------------------------------------------------------------------------------------------------------------------------------------------------seq2 SLAM CDS 2134 2191 . + 2 gene_id "000001"; transcript_id "000001.1"; frame "2"; exontype "internal" seq2 SLAM CDS 2867 3545 . + 1 gene_id "000001"; transcript_id "000001.1"; frame "2"; exontype "internal“ -------------------------------------------------------------------------------------------------------------------------------------------------------------> Protein 1: (244,244) aa (incomplete protein) Y Z ... 1 KCEAIASDCF LSGNVDIELK DHNNCISKIN VEDQKNCALS WAFASIYHLE CE IAS CF LSGNVDIE K D ++C S I E+Q NC LS W F S HLE 1 TCERIASSCF LSGNVDIEWK DKSSCFSSIE TEEQGNCNLS WLFTSKTHLE http://baboon.math.berkeley.edu/~syntenic/slam.html 50 50 TwinScan • One of the first gene predictors to substantially exceed the performance of GENSCAN on a genomic scale by using mouse–human comparison was TWINSCAN (Korf et al. 2001). http://genes.cs.wustl.edu/query.html Other Comparative Gene Predicters • DoubleScan http://www.sanger.ac.uk/cgibin/doublescan/submit It is a program for comparative ab initio prediction of protein coding genes in mouse and human DNA. Generates exon candidates in both sequences. • SPG-1....http://soft.ice.mpg.de/sgp-1 SGP-1 is a similarity based gene prediction program. Given two genomic DNA sequences it post-processes the pairwise local alignment to predict single or multiple gene models of protein coding genes in forward and reverse strands. Regulatory Sequence Regulatory Sequence … Leroy Hood brought out this point in his talk at the Bio2001 meeting in San Diego (24–28 June 2001) with his statement that “The difference between man and monkey is gene regulation.” Quotes from the 50/50 series of interviews by Bio-IT World Lincoln Stein Associate Professor, Cold Spring Harbor Laboratory . “I think the places that we should be looking at now are the non-repetitive, unique, noncoding DNA. … If they are conserved, they must be important. There are discoveries in there.” Finding regulatory regions rVISTA. . . . . . . . . . . . . . . . . . . . . . . http://teapot.jgi-psf.org/ovcharen/rvista/index.html Consite. . . . . . . . . . . . . . . . . . . . . . . http://forkhead.cgb.ki.se/cgi-bin/consite Footprinter. . . . . . . . . . . . . . . . . . . http://abstract.cs.washington.edu/~blanchem/FootPrinterWeb/FootprinterInput.pl Toucan. . . . . . . . . . . . . . . . . . . . . . . http://www.esat.kuleuven.ac.be/~saerts/software/toucan.php/ Trafac . . . . . . . . . . . . . . . . . . . . . . . . http://trafac.chmcc.org/trafac/index.jsp VISTA is a set of tools for comparative genomics. It was designed to visualize long sequence alignments of DNA from two or more species with annotation information. The alignment engine behind VISTA. AVID is a program for globally aligning DNA sequences of arbitrary length. mVISTA (main VISTA) A program for visualizing alignments of an arbitrary number of genomic sequences from different species rVISTA (regulatory VISTA) combines transcription factor binding sites database search with a comparative sequence analysis. http://teapot.jgi-psf.org/ovcharen/rvista/index.html rVista http://teapot.jgi-psf.org/ovcharen/rvista/index.html A program that combines transcription factor binding site (TFBS) searches with comparative sequence analysis. At the first step, human and mouse sequences are aligned using the global alignment program MAVID. At the second step, potential transcription factor binding sites were predicted by Match™ program based on TRANSFAC Professional 5.3 library. At the third step, the human-mouse sequence conservation of a DNA region spanning a transcription factor binding site was assessed using a novel strategy. Human and/or mouse annotation determine the genomic location of each predicted transcription factor hit. Finding Regulatory Regions rVista A program that combines transcription factor binding site (TFBS) searches with comparative sequence analysis. ConSite http://forkhead.cgb.ki.se/cgi-bin/consite “Identification of conserved regulatory elements by comparative genome analysis” Boris Lenhard*†, Albin Sandelin*†, Luis Mendoza*‡, Pär Engström*, Niclas Jareborg*§ and Wyeth W Wasserman*¶ BioMed Central - Open Access Journal of Biology ConSite - Identification of conserved regulatory elements by comparative genome analysis • Consite is a web-based tool for detecting transcription factor binding sites in genomic sequences using phylogenetic footprinting. • Two orthologous genomic sequences are aligned, and transcription factor binding sites are only reported for those regions in the alignment which transcend a certain treshold of conservation. ConSite • The method is implemented as a graphical web application, ConSite, which is at: • http://forkhead.cgb.ki.se/cgi-bin/consite or • http://www.phylofoot.org/ • Various tools are made available at phylofoot.org. http://www.phylofoot.org/ Sequence View http://www.phylofoot.org/ http://www.phylofoot.org/ Toucan http://www.esat.kuleuven.ac.be/~saerts/software/toucan.php • Toucan is a workbench for regulatory sequence analysis on metazoan genomes : comparative genomics, detection of significant transcription factor binding sites, and detection of cis-regulatory modules (combinations of binding sites) in sets of coexpressed/coregulated genes. • Standalone Java application that is tightly linked with Ensembl, and was built using the BioJava package Perl – A Programming Language. • What is Perl? • Perl actually stands for • Practical Extraction and Report Language, and was invented by Larry Wall. • Perl is supported by its users and was all written by volunteers. Programming Tools Perl • Perl is remarkably good for slicing, dicing, twisting, wringing, smoothing, summarizing and otherwise mangling text! • Perl's powerful regular expression matching and string manipulation operators simplify this job in a way that is unequalled by any other modern language. Perl & Genome Data • Although genome informatics groups are constantly tinkering with other "high level" languages such as Python, Tcl and recently Java, nothing comes close to Perl's popularity. • “In short, when the genome project was foundering in a sea of incompatible data formats, rapidly-changing techniques, and monolithic data analysis programs that were already antiquated on the day of their release, Perl saved the day.” Lincoln Stein. Perl one-Liners! • Take a blast output and print all of the gi's(Genbank Identifiers) matched, one per line. • Solution one line of Perl. • perl -pe 'next unless ($_) = /^>gi\|(\d+)/;$_.="\n"' filename.blast Perl Modules/Programs • Perl can be used for complex programs. • The RepeatMasker program is written in Perl. It calls other programs written in other languages(Crossmatch written in C). • Slipper is a 4500 line program written in Perl. It calls Repeatmasker and Primer3 repeatedly and processes the output files from them, writing summarised results to disk. SLiPPER Sequence Length Polymorphism and Primer FindER Programming: Keith Satterley, Specifications: Grant Morahan Division of Bioinformatics & Genetics, The Walter & Eliza Hall Institute of Medical Research Slipper • • • • Masks Alu etc. repeats (using RepeatMasker); Selects SSLRs with user-specified parameters; Designs primers (using Primer3) Grant Morahan selects and tests chosen SSLR’s to become Microsatelite Markers on the Mouse Genome. • To derive a first generation systematic map of the mouse, with sub-centiMorgan (1Mb) resolution. • Extend to 10 times this density over 50 strains. UTILITY OF SLIPPER 40 Dimer repeat s Mult imer repeat s 35 O* = polymorphic O* B6 = NZO 30 Number of SSRs 25 O* 20 O* 15 10 O* O* O* O* O* O* O* 5 O* O* 0 0 200000 400000 600000 800000 1000000 1200000 Position on chromosome (bp) 1400000 Possible SLIPPER Data Analysis • STRAIN RELATEDNESS AND EVOLUTION -graphic depiction of allele sharing between strains -probability of IBD v allele convergence by mutation -comparison of close strain relatedness (eg B6 v b10; B6 v Ka; D1 v D2; NOD v NOR) -overall strain relatedness –> cladogram -pairwise strain dimorphism rate useful for choosing 2 strains to be used in a cross -comparison of results for reduced strains set with MIT markers - comparison of haplotypes with Phenome database O|B|F - Open Bioinformatics Foundation • The Open Bioinformatics Foundation is a non profit, volunteer run organization focused on supporting open source programming in bioinformatics. • The foundation grew out of the volunteer projects Bioperl, BioJava and Biopython. • Underwrites and supports the BOSC conferences. • Organizing and supporting developer-centric "hackathon" events. • Managing servers, bank account & other assets. Open Bioinformatics Foundation • PROJECTS • BioPerl BioJava BioPython BioRuby BioPipe BioSQL / OBDA MOBY DAS BioPathways† EMBOSS† Open Bioinformatics Foundation • June 27-28 2003 -- 4th Annual Bioinformatics Open Source Conference • www.open-bio.org/bosc/ ISMB 2003 - Brisbane • Normally held in Europe and Nth. America. • For 2 days beforehand – BOSC(Open Source conference) . – Biopathways, BioOntology, Text Mining & WEB03 • Tutorials on Sunday – choose 2 from 15 offered. • ISMB for 4 days – over 50 no parallel talks! http://www.iscb.org/ismb2003/index.shtml The bioperl project • Officially organized in 1995 and existing informally for several years prior, The Bioperl Project is an international association of developers of open source Perl tools for bioinformatics, genomics and life science research. What is BioPerl • Bioperl is a tookit of perl modules useful in building bioinformatics solutions in perl. • It is built in an object-oriented manner • The collection of modules can be used to run a large range of Bioinformatics programs and process their output files. • There are modules to carry out analyses, to graph data and to read many data formats. BioJava http://www.biojava.org/ • The BioJava Project is an open-source project dedicated to providing Java tools for processing biological data. • BioJava is a general bioinformatics toolkit. It provides a framework for building everything from simple scripts to complete applications. BioJava is designed to be used as a library. BioJava http://www.biojava.org/ • • Currently, there are objects for: Sequences and features – – – • Dynamic programming – – – – • Single-sequence and pair-wise HMMs Viterbi-path, Forward and Backward algorithms Training models Sampling sequences from models External file formats and programs – – – • IO Processing, storing, manipulating Visualising GFF Blast Meme Sequence Databases – – – BioCorba interoperability ACeDB client DAS client Other Open Source Projects. • BioDAS - Distributed Annotation System (DAS) - A server system for the sharing of Reference Sequences. • Biopython. tools for computational molecular biology. Python(excellent language for beginners, yet superb for experts). • BioRuby, BioSQL, MOBY,BioPathways and BioOpera. LINKS Internet Resources Prediction of exons and gene structure SLAM....http://baboon.math.berkeley.edu/~syntenic/slam.html SPG-1....http://soft.ice.mpg.de/sgp-1 TwinScan....http://genes.cs.wustl.edu Finding regulatory regions by phylogenetic footprinting Consite....http://forkhead.cgb.ki.se/cgi-bin/consite rVISTA....http://teapot.jgi-psf.org/ovcharen/rvista/index.html Toucan....http://www.esat.kuleuven.ac.be/~saerts/software/toucan.php Whole-genome alignments in genome browser ECR browser....http://nemo.lbl.gov/ecrBrowser Ensembl....http://www.ensembl.org UCSC....http://genome.ucsc.edu A comprehensive, straightforward Links Page – one of the best! http://apollo11.isto.unibo.it/ Genome Links from Ewan Birney et al. • Genome aligners AVID....http://www-gsd.lbl.gov/vista/details_avid.htm BLASTZ....http://bio.cse.psu.edu BLAT....http://genome.ucsc.edu Exonerate....http://www.ensembl.org/Docs/wiki/html/EnsemblDocs/Exonerate.html GLASS....http://crossspecies.lcs.mit.edu LAGAN/MLAGAN....http://lagan.stanford.edu MegaBLAST....http://www.ncbi.nih.gov/blast/tracemb.html MUMmer....http://www.tigr.org/software/mummer PatternHunter....http://www.bioinformaticssolutions.com/products/ph.php WABA....http://www.cse.ucsc.edu/~kent/xenoAli/index.html • Prediction of exons or coding regions DIALIGN2....http://bibiserv.techfak.uni-bielefeld.de/dialign ExoFish....http://www.genoscope.cns.fr/proxy/cgi-bin/exofish.cgi OrthoSeq....http://www.phylofoot.org/cgi-bin/orthoseq.cgi ROSETTA/GLASS....http://crossspecies.lcs.mit.edu • Prediction of exons and gene structure DoubleScan....http://www.sanger.ac.uk/Software/analysis/doublescan SLAM....http://baboon.math.berkeley.edu/~syntenic/slam.html SPG-1....http://soft.ice.mpg.de/sgp-1 TwinScan....http://genes.cs.wustl.edu Genomics Web sites • • • • • • • • • Functional and Comparative Genomics Research - More technical information on HGP involvement with comparative and functional genomics. Virtual Library of Genetics - Links to genetic and genomic information organized by organism. Microbial Genome Program - U.S. Department of Energy program to study the genetic material of microbes that may be useful in helping DOE fulfill its missions. DOE Joint Genome Institute - Consortium of U.S. Department of Energy researchers developing and exploiting new technologies as a means for discovering and characterizing the basic principles and relationships underlying living systems. A Quick Guide to Sequenced Genomes - Illustrated index of organisms that have had their genomes sequenced. From the Genome News Network. Model Organisms for Biomedical Research - Information on model organisms from the National Institutes of Health. Mouse Genome Resources - Gateway to mouse resources in and beyond National Center for Biotechnology Information (NCBI) resources. Functional Genomics - Gateway to functional genomics sources from Science. Ecce homology: A primer on comparative genomics - From Modern Drug Discovery, a publication of the American Chemical Society. Image Gallery – links http://www.ornl.gov/TechResources/Human_Genome/education/images.html Image Gallery – links http://www.ornl.gov/TechResources/Human_Geno me/education/images.html • • • • • Gallery 1: Genome Science Gallery 2: Genome Tools and Technologies Gallery 3: Genomes to Life Gallery 4: Human Genome Project Gallery 5: Ethical, Legal, and Social Issues; Genomic Medicine http://www.ornl.gov/TechResources/Human_Geno me/education/images.html • Other Website Image Galleries and Resources • NIH NHGRI Press Photos • CSHL Eugenics Archive • RasMol Protein Gallery • Photos of normal and abnormal chromosomes • Access Excellence Graphics Gallery • The Why Files Cool Image Gallery • Genetics Animation Gallery http://www.ornl.gov/TechResources/Human_Geno me/education/images.html • Molecular Expressions Photo Gallery • Gene Maps – 1999 Online Gene Map from NCBI. – Clickable 1996 Gene Map from Science magazine. You can click on any one of the 24 different human chromosomes and see examples of genes found. • Chromosome Maps – human chromosome 16 – human chromosome 19 http://www.ornl.gov/TechResources/Human_Geno me/education/images.html • • • • • • • • • • • • • • • U.S. Government Image Galleries Argonne National Laboratory Photo Gallery Brookhaven National Laboratory Image Library Fermi National Laboratory Photo Database Jefferson Laboratory Picture Exchange Lawrence Berkeley National Laboratory Image Gallery Lawrence Livermore National Laboratory Image Gallery Los Alamos National Laboratory Photo Gallery National Human Genome Research Institute Image Gallery National Renewable Energy Laboratory Photo Library Oak Ridge National Laboratory Image Gallery Pacific Northwest National Laboratory Photo Gallery Stanford Linear Accelerator Center Photo Archives Sandia National Laboratory Photo Gallery U.S. DOE Image Gallery Acknowledgements • WEHI Bioinformatics group – Tim Beissbarth – Alex Gout – Terry Speed – All the others in Bioinformatics who provide a great environment to work in and with. • Grant Morahan • WEHI ITS - who provide the best infrastructure of anywhere I know of. Year by year we are becoming better equipped to accomplish the things we are striving for. But what are we actually striving for? - Bertrand de Jouvenel, 1903-1987 Success is the ability to go from failure to failure without losing your enthusiasm. - Winston Churchill, 18741965
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