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Tools for Maximising the Value of Genomic Data

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Title: Tools for Maximising the Value of Genomic Data


1
Tools for Maximising the Value of Genomic Data
WEHI Postgraduate Seminar Series 2003
  • Keith Satterley, Bioinformatics,
  • The Walter Eliza Hall Institute of Medical
    research
  • 2nd. June 2003
  • keith_at_wehi.edu.au
  • http//bioinf.wehi.edu.au/resources/presentations.
    html

2
Overview
  • Genomic data what is it, where is it
  • Gene Finding
  • GenScan
  • Comparitive Genomics
  • Gene Finding
  • Slam
  • Twinscan
  • Finding Regulatory Regions
  • rVista
  • Consite
  • Toucan
  • Programming Tools
  • Languages
  • Perl
  • BioPerl
  • BioJava
  • Bio???

3
http//www.geneticscongress2003.com/index.php
4
  • 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
  • TOTAL1743

5
Eukaryota (completed chromosomes)
http//www.ebi.ac.uk/genomes/
6
gnn.tigr.org
http//gnn.tigr.org/sequenced_genomes/genome_guide
_p1.shtml
7
GOLD Genomes Online Database
http//www.genomesonline.org/
8
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9
Francis CollinsDirector, National Human Genome
Research Institute
25th. April 2003 Here in the very month of the 5
0th anniversary of the discovery of DNAs double
helix, I am pleased and honored perhaps I
should say exhilarated to declare the goals of
the Human Genome Project to be completed.
(04/25/03)
..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/
10
Most Recent Genomics News
BETHESDA, Md., May 20, 2003 By June, researche
rs 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 hig
h-quality draft of the dog genome sequence within
the next 12 months. After the genome of the bo
xer 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
11
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.

12
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/slid
    es/gene_finding.pdf

13
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/W
    ise2.
  • 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.gate
    ch.edu/GeneMark
  • Geneid. Recently updated to a new and faster
    version.
  • http//www1.imim.es/geneid.html

14
Gene Finders
15
Gene Finders
  • Overall performances are the best for HMMgene and
    GENSCAN.
  • Some programs accuracy depends on the GC
    content, except for HMMgene and GENSCAN, which
    use different parameters sets for different GC
    contents.
  • 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.
  • Internal exons are much more likely to be
    correctly predicted (weakness of the start/stop
    codon detection).
  • Initial and terminal exons are most likely to be
    missed completely.
  • Only HMMgene and GENSCAN have reliable scores
    for exon prediction.

16
Gene prediction limits
  • 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

17
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
18
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19
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20
Comparitive Genomics
21
Quotes from the 50/50 series of interviews by
Bio-IT World
Gene MyersProfessor, Dept. of Electrical
Engineering Computer SciencesUniversity of
California, Berkeley .
  • If you take a sequence and just run a gene
    prediction program on it, the programs dont
    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.

22
Quotes from the 50/50 series of interviews by
Bio-IT World
Richard DurbinHead 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.

23
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.

24
Comparative Genomics
Human
Mouse
Rat
Evolutionary relationship between metazoans that
are sequenced, or due for sequencing.

Evolutionary distances are in millions of years.
25
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/n
    rg/journal/v4/n4/full/nrg1043_fs.html

26
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/gra
phics/slides/ttmousehuman.html
27
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28
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.

29
Whole Genome Alignments
K-browser http//hanuman.math.berkeley.edu/cgi-bi
n/kbrowser A multiple genome browser, currently s
et up for human, mouse and rat
based on the MAVID alignments, UCSC genome
browser.
30
Comparative Gene Prediction
SLAM http//baboon.math.berkeley.edu/syntenic/
slam.html Example of a comparative genefinder
Employs a generalised pair hidden Markov model a
pproach for predicting gene structures within
syntenic genomic sequences Performing gene find
ing and alignment of the sequences simultaneously
31
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
  • 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.

32
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34
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 ge
ne_id "000001" transcript_id "000001.1" frame
"2" exontype "internal" seq2 SLAM CDS 2867 3
545 . 1 gene_id "000001" transcript_id
"000001.1" frame "2" exontype "internal
--------------------------------------------------
--------------------------------------------------
--------------------------------------------------
-------- Protein 1 (244,244) aa (incomplete pr
otein) Y 1 KCEAIASDCF LSGNVDIELK DHNNCISKIN
VEDQKNCALS WAFASIYHLE 50 CE IAS CF LSGNV
DIE K D C S I EQ NC LS W F S HLE
Z 1 TCERIASSCF LSGNVDIEWK DKSSCFSSIE
TEEQGNCNLS WLFTSKTHLE 50 ...
http//baboon.math.berkeley.edu/syntenic/slam.htm
l
35
TwinScan
  • One of the first gene predictors to substantially
    exceed the performance of GENSCAN on a genomic
    scale by using mousehuman comparison was
    TWINSCAN (Korf et al. 2001).

http//genes.cs.wustl.edu/query.html
36
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37
Other Comparative Gene Predicters
  • DoubleScan -http//www.sanger.ac.uk/cgibin/doubles
    can/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.

38
Regulatory Sequence
39
Regulatory Sequence
  • Leroy Hood brought out this point in
  • his talk at the Bio2001 meeting in San
  • Diego (2428 June 2001) with his statement
  • that
  • The difference between man and monkey is gene
    regulation.

40
Quotes from the 50/50 series of interviews by
Bio-IT World
Lincoln SteinAssociate Professor, Cold Spring
Harbor Laboratory .
  • I think the places that we should be looking at
    now are the non-repetitive, unique, non-coding
    DNA. If they are conserved, they must be
    important. There are discoveries in there.

41
Finding regulatory regions
  • rVISTA. . . . . . . . . . . . . . . . . . . . . .
    .
  • http//teapot.jgi-psf.org/ovcharen/rvista/index.h
    tml
  • Consite. . . . . . . . . . . . . . . . . . . . .
    . .
  • http//forkhead.cgb.ki.se/cgi-bin/consite
  • Footprinter. . . . . . . . . . . . . . . . . . .

  • http//abstract.cs.washington.edu/blanchem/FootP
    rinterWeb/FootprinterInput.pl
  • Toucan. . . . . . . . . . . . . . . . . . . . . .
    .
  • http//www.esat.kuleuven.ac.be/saerts/software/t
    oucan.php/
  • Trafac . . . . . . . . . . . . . . . . . . . . .
    . . .
  • http//trafac.chmcc.org/trafac/index.jsp

42
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.ht
ml
43
rVista http//teapot.jgi-psf.org/ovcharen/rvi
sta/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. 
44
Finding Regulatory Regions
rVista A program that combines transcriptio
n factor binding site (TFBS) searches with
comparative sequence analysis.

45
Identification of conserved regulatory elements
by comparative genome analysis
ConSitehttp//forkhead.cgb.ki.se/cgi-bin/consite
  • Boris Lenhard, Albin Sandelin, Luis
    Mendoza, Pär Engström,
  • Niclas Jareborg and Wyeth W Wasserman
  • BioMed Central - Open Access
  • Journal of Biology

46
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.

47
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.

48
http//www.phylofoot.org/
49
Sequence View
http//www.phylofoot.org/
50
http//www.phylofoot.org/
51
Toucanhttp//www.esat.kuleuven.ac.be/saerts/soft
ware/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

52
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.

53
Programming Tools
54
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.

55
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.

56
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

57
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.

58
SLiPPER
Sequence Length Polymorphism and Primer FindER
Programming Keith Satterley, Specifications
Grant Morahan Division of Bioinformatics Geneti
cs, The Walter Eliza Hall Institute of Medical
Research
59
Slipper
  • Masks Alu etc. repeats (using RepeatMasker)
  • Selects SSLRs with user-specified parameters
  • Designs primers (using Primer3)
  • Grant Morahan selects and tests chosen SSLRs 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.

60
UTILITY OF SLIPPER
Number of SSRs
Position on chromosome (bp)
61
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

62
OBF - 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.

63
Open Bioinformatics Foundation
  • PROJECTS
  • BioPerlBioJavaBioPythonBioRuby BioPipeBioSQL
    / OBDAMOBYDASBioPathwaysEMBOSS

64
Open Bioinformatics Foundation
  • June 27-28 2003 -- 4th Annual Bioinformatics
    Open Source Conference
  • www.open-bio.org/bosc/

65
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
66
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.

67
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.

68
BioJavahttp//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.

69
BioJavahttp//www.biojava.org/
  • Currently, there are objects for
  • Sequences and features
  • IO
  • Processing, storing, manipulating
  • Visualising
  • 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
  • GFF
  • Blast
  • Meme
  • Sequence Databases
  • BioCorba interoperability
  • ACeDB client
  • DAS client

70
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.

71
LINKS
72
Internet Resources
Prediction of exons and gene structure
SLAM....http//baboon.math.berkeley.edu/synteni
c/slam.html SPG-1....http//soft.ice.mpg.de/sgp-
1 TwinScan....http//genes.cs.wustl.edu Findi
ng regulatory regions by phylogenetic
footprinting Consite....http//forkhead.cgb.ki.
se/cgi-bin/consite rVISTA....http//teapot.jgi-p
sf.org/ovcharen/rvista/index.html
Toucan....http//www.esat.kuleuven.ac.be/saerts/s
oftware/toucan.php Whole-genome alignments in
genome browser ECR browser....http//nemo.lbl.g
ov/ecrBrowser Ensembl....http//www.ensembl.org
UCSC....http//genome.ucsc.edu A comprehensi
ve, straightforward Links Page one of the
best! http//apollo11.isto.unibo.it/
73
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/htm
    l/EnsemblDocs/Exonerate.html GLASS....http//cros
    sspecies.lcs.mit.edu LAGAN/MLAGAN....http//lagan
    .stanford.edu MegaBLAST....http//www.ncbi.nih.go
    v/blast/tracemb.html MUMmer....http//www.tigr.or
    g/software/mummer PatternHunter....http//www.bio
    informaticssolutions.com/products/ph.php
    WABA....http//www.cse.ucsc.edu/kent/xenoAli/ind
    ex.html
  • Prediction of exons or coding regions
  • DIALIGN2....http//bibiserv.techfak.uni-bielefeld
    .de/dialign ExoFish....http//www.genoscope.cns.f
    r/proxy/cgi-bin/exofish.cgi OrthoSeq....http//ww
    w.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/an
    alysis/doublescan SLAM....http//baboon.math.berk
    eley.edu/syntenic/slam.html SPG-1....http//soft
    .ice.mpg.de/sgp-1 TwinScan....http//genes.cs.wus
    tl.edu

74
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.

75
Image Gallery links
http//www.ornl.gov/TechResources/Human_Genome/edu
cation/images.html
76
http//www.ornl.gov/TechResources/Human_Genome/edu
cation/images.html
Image Gallery links
  • 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

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http//www.ornl.gov/TechResources/Human_Genome/edu
cation/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

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http//www.ornl.gov/TechResources/Human_Genome/edu
cation/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

79
http//www.ornl.gov/TechResources/Human_Genome/edu
cation/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

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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.

81
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, 1874-1965
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