Genomics

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Genomics
2
The Human Genome Project

• Mapping and Sequencing the Genomes of Model
  Organisms
• Data Collection and Distribution
• Ethical, Legal, and Social Considerations
• Research Training
• Technology Development
• Technology Transfer

3
A Few Genome Resources

• NCBI Genome Resources
• UCSC Human Genome Browser
• Ensembl Human Genome Server

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Genome Sequencing Progress

• NCBI Genome Sequence Repository
• All organisms
• Eukaryotic genomes
• Prokaryotic genomes
• Archaea genomes
• Viruses

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Genome Sequencing
From NCBI, 5/2001
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Human Genome Sequencing 2/11/2001
From NCBI
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Human Genome Progress 2/11/2001
From NCBI
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Microbial Genomes

• Published complete microbial genomes
• Microbial genomes and chromosomes in progress

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Genome Informatics

• Annotation and Analysis
• Data Handling
• Metabolic Reconstruction
• Comparative Genomics
• Functional Genomics

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Genome Project Organization

• Cloning
• Mapping
• Sequencing
• Annotation
• Analysis

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Cloning and Mapping
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Cloning

• Large
• YACs
• 1 Mb
• BACs
• 100 - 200 Kb
• Intermediate
• Cosmids
• Lambda clones
• Small
• Plasmids M13

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Mapping

• Establishment of Guideposts
• Aids in Assembly
• Error Checking
• Useful in mapping of genetic disorders

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Genetic Maps

• Cytogenetic markers
• Linkage maps
• Polymorphic loci screened by PCR to determine
  inheritence patterns
• Produce linkage map with nearby loci

15
Physical Maps

• Radiation Hybrid/YACs/Cosmids
• Restriction Sites
• Sequence Tagged Sites
• 100 Kb resolution needed
• 30,000 STSs
• Expressed Sequence Tags
• Detection
• PCR
• Hybridization
• FISH
• Fluoresecent in situ Hybridization

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Human Genome STS Mapping Strategy

• STS Content Mapping
• Screen YACs by PCR
• Radiation Hybrid Mapping
• Screen RH Cell lines by PCR
• Genetic Mapping
• PCR Screening of polymorphic loci
• Combine above to produce an integrated map

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Mapping Resolution

• YAC mapping
• 1 Mb
• Radiation hybrid mapping
• 10 Mb
• Genetic map
• 30 Mb

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GeneMap98

• Integrated Human Genetic Map
• Over 30,000 unique gene-based markers
• 100 Kb resolution
• http//www.ncbi.nlm.nih.gov/genemap98/

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Map Integration
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Human Chromosome 1 Genetic Map
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Human Chromosome 1 Combination Map
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Sequencing
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Sequencing Methods

• Random Shotgun
• Ordered Shotgun
• Directed
• Primer Walking
• Direct genomic sequencing

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Random Shotgun Sequencing

• Randomly shear or cut DNA into small pieces
• 2-4 Kb
• Clone into M13, pUC or some other sequencing
  vector
• Sequence the clones from both ends
• Rely on the computer to assemble the sequences
  into one (or as few as possible) contigs

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Shotgun Sequencing Statistics

• Lander and Waterman equation
• poisson distribution
• Po e-m
• probability that a base is not sequenced where
  msequence coverage

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H. influenza Sequencing

• For 1X random sequence coverage 1.8 Mb
• P 0.37 (63 of the bases are sequenced)
• To get gt 99 of the bases sequenced
• 5X coverage 8.74 Mb of sequence
• Po e-5 0.0067
• This coverage would leave approx. 128 gaps of
  about 100 bp in size
• From Science 269496-512. 1995

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Ordered Sequencing

• Generate a set of large sequence clones in lambda
  phage
• May be subcloned from YACs or BACs as necessary
• End sequence the lambda clones and order the
  clones to produce a map of the genome
• Choose a minimal tiling path of the genome from
  the ordered lambda clones

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Ordered Sequencing...

• Shear and subclone the lambda inserts that
  comprise the minimal tiling set into sequencing
  vectors
• Shotgun sequence and assemble each of these
  lambda inserts individually
• Assemble all sequences into one, contiguous
  genome

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Directed Sequencing

• Process used for finishing following the shotgun
  sequencing phase
• Gap closure
• Use specific sequencing primers to extend
  appropriate clones into gap regions
• Use specific sequencing primers to sequence
  directly from genomic DNA

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Sequence Assembly
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Assembly of Shotgun Fragments

• For H. influenzae (TIGR) 1.8 Mb
• 24,304 Sequence fragments were generated for the
  random assembly phase
• 11,631,485 bases
• Generated 140 contigs
• Assembled using the TIGR Assembler
• 30 hours of cpu time

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phred/phrap/consed

• Widely used programs for sequence
• base calling (phred)
• assembly (phrap)
• editing (consed)
• Developed at the University of Washington
• Phil Green (phrap)
• Brent Ewing (phred)
• David Gordon (consed)

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Genome Annotation and Analysis

• Pattern Matching

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Sequence Annotation

• ORF identification
• Frameshift resolution
• Genome map construction
• Functional assignments
• Metabolic pathway assignment
• Metabolic pathway Reconstruction
• Comparative analysis

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Annotation Tools

• Semi-automated
• Manual

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MAGPIE

• Multipurpose Automated Genome Project
  Investigation Environment
• Terry Gaasterland et. al.
• http//genomes.rockefeller.edu/magpie/magpie.htmlA
  utomated
• Semi-automated analysis tool for microbial genome
  projects

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MAGPIE Example
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Non-Automated Analysis and Prediction

• The Ureaplasma urealyticum genome database
• Run analysis tool
• Parse results
• Dump results into the database
• View results
• Manually annotate

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Genomic Sequence Database

• Data Storage
• Sequence
• Gene Map
• Annotation
• User Interface
• Web browser
• Customizable

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The Ureaplasma urealyticum Genome Project

• Uu - 751,719 bp
• http//genome.microbio.uab.edu/uu/uugen.htm
• Web-based genome analysis tool

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Annotation Problems

• Problems with existing sequence databases
• Incomplete datasets
• Skewed datasets
• Incorrectly annotated records
• Annotations based on experimental vs. predicted
  data
• Nomenclature differences
• Transitive errors in gene function predictions
• Functional predictions for hypothetical genes

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Metabolic Pathway Reconstruction
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Metabolic Pathway Reconstruction

• Role assignment
• Extract metabolic pathways from genomes
• Navigation and analysis
• Pathway editing

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Metabolic Assignments

• Amino acid Biosynthesis
• Biosynthesis of cofactors, prosthetic groups, and
  carriers
• Cell envelope
• Cellular processes
• Central intermediary metabolism
• Energy metabolism
• Fatty acid and phospholipid metabolism
• Purines, pyrimidines, nucleosides, and
  nucleotides
• Regulatory functions
• Replication
• Transcription
• Translation
• Transport and binding proteins
• Other categories, Unassigned
• Hypothetical

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Ureaplasma urealyticum Gene Map
1
50,000
100,000
50,001
100,001
150,000
150,001
200,000
200,001
250,000
250,001
300,000
300,001
350,000
350,001
400,000
400,001
450,000
450,001
500,000
500,001
550,000
550,001
600,000
600,001
650,000
650,001
700,000
700,001
750,000
750,001
751,719
Other
Cofactor Biosynthesis
Energy Metabolism
Replication
Cell envelope
Fatty Acid Metabolism
Transcription
RNA
Cellular processes
Hypothetical
Translation
Central Intermediary Metabolism
Nucleotide Metabolism
Transport
tRNA
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Uu Genes
Mg Genes
Role
Amino acid Biosynthesis
1
0.2
0
0.0
Biosynthesis of cofactors
10
1.7
7
1.5
Cell envelope
19
3.1
26
5.4
Cellular processes
13
2.1
15
3.1
Central intermediary metabolism
15
2.5
7
1.5
Energy metabolism
23
3.8
30
6.3
Fatty acid - phospholipids
6
1.0
7
1.5
Hypothetical
293
48.3
169
35.3
Other categories
1
0.2
3
0.6
Purines, pyrimidines
18
3.0
20
4.2
Regulatory functions
4
0.7
4
0.8
Replication
45
7.4
31
6.5
Transcription
17
2.8
19
4.0
Translation
100
16.5
99
20.7
Transport and binding proteins
37
6.1
35
7.3
Unassigned
4
0.7
7
1.5
Total
606
100.0
479
100.0
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EcoCyc

• Peter D. Karp, PhD
• SRI International
• Menlo Park, CA
• http//ecocyc.pangeasystems.com/ecocyc/ecocyc.html

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Pathway Reconstruction
Cell
Annotated Genome
Adapted from P. Karp, Pangea Systems
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Glycolysis in Uu?
glucose-1-phosphate
?
phosphoglucomutase
glucose-6-phosphate
phosphoglucose isomerase
fructose-6-phosphate
6-phosphofructokinase
fructose-1,6-bisphosphate
fructose bisphosphate aldolase
glyceraldehyde-3-phosphate
glyceraldehyde 3-phosphatedehydrogenase1.2.1.12
glyceraldehyde-3-phosphatedehydrogenase 1.2.1.9
3-phospho-D-glyceroyl-phosphate
phosphoglycerate kinase
pyruvate
3-phosphoglycerate
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Uu Energy Metabolism

• Glycolysis
• Missing several components
• Pentose-phosphate pathway
• Only 2/8 enzyme complexes present
• Proton motive force - ATP synthase complex
• Urease Gene Complex
• Biologically relevant

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Comparative Genomics

• What makes one organism different from all other
  organisms?
• Molecular Biology
• Physiology
• Pathogenesis
• Epidemiology
• Genetics

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Ortholog Comparisons

• Uu to Mg genes 324
• 53 of Uu 67 of Mg
• 71 hypothetical
• Mh to Mg genes 314
• 41 of Mh 57 of Mg
• 55 hypothetical (2 unique hypothetical)
• Mh to Uu genes 330
• 47 of Uu 43 of Mh
• 82 hypothetical (19 unique hypothetical)

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M. genitalium - M. pneumoniae Gene Order
M. genitalium Gene Position
M. pneumoniae Gene Position
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M. genitalium - U. urealyticum Gene Order
M. genitalium Gene Position
U. urealyticum Gene Position
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Paralog Analysis

• Identification of conserved, paralogous groups
• All against All comparison
• Genes within one organism
• Identifies groups of related genes
• Primary sequence
• Structure
• Function

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Uu Paralogous Clusters gt3

• 4 tRNA synthetase
• 4 Translation factors
• 4 Hypothetical membrane lipoprotein
• 5 ATP synthase alpha, beta chains
• 6 MBA
• 7 Hypothetical membrane lipoprotein
• 8 Hypothetical
• 10 Iron transporters
• 13 Transporters

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Functional Genomics

• Gene Expression
• Gene Regulation
• Genome-wide Mutagenesis

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Expression Arrays

• Cell growth in different environments
• Isolate cDNAs
• Measure expression using array technology
• Create database of expression information
• Display information in an easy-to-use format
• Show ratio of expression under different
  conditions

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Putting it all together
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From F. Blattner, U. Wisc.
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Chromosome Views

• Ensembl view
• UC Santa Cruz view
• NCBI View

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A Final Caveat

• The difficulty of identifying genes in anonymous
  vertebrate sequences
• Claverie JM, Poirot O, Lopez F
• Comput Chem 199721(4)203-14

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The identification of genes in newly determined
vertebrate genomic sequences can range from a
trivial to an impossible task. In a statistical
preamble, we show how "insignificant" are the
individual features on which gene identification
can be rigorously based promoter signals, splice
sites, open reading frames, etc. The practical
identification of genes is thus ultimately a
tributary of their resemblance to those already
present in sequence databases, or incorporated
into training sets. The inherent conservatism of
the currently popular methods (database
similarity search, GRAIL) will greatly limit our
capacity for making unexpected biological
discoveries from increasingly abundant genomic
data. Beyond a very limited subset of trivial
cases, the automated interpretation (i.e. without
experimental validation) of genomic data, is
still a myth. On the other hand, characterizing
the 60,000 to 100,000 genes thought to be hidden
in the human genome by the mean of individual
experiments is not feasible. Thus, it appears
that our only hope of turning genome data into
genome information must rely on drastic
progresses in the way we identify and analyze
genes in silico.
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Only One Final Word of Wisdom...

• ...although the computer is a wonderful helpmate
  for the sequence searcher and comparer,
  biochemists and molecular biologists must guard
  against the blind acceptance of any algorithmic
  output given the choice, think like a biologist
  and not a statistician.
• - Russell F. Doolittle, 1990

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