Integrating Genome

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Integrating Genome

劉恕維洪士堯李宗燁林修竹田耕豪廖奎達

D. R. Zerbino, B. Paten, and D. Haussler Science 13 April 2012: 179-182.

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 Introduction

 Obtaining genome sequence

 Modeling the evolution of genotype

 From genotype to phenotype

 Looking ahead to application

Outline

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Introduction

劉恕維

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1970 Walter Fiers

History – The Pioneer

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History – The technique

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History – Hardware

3000 4000 5000 6000 7000 8000 9000 10000

Moor's law 欄 1

16

10000

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Genotype & Phenotype

http://big5.ifeng.com/gate/big5 /baby.ifeng.com/yuer/special/de tail_2011_01/27/4481382_0.shtml

Autism

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Genotype & Phenotype

A A

A O

B B B O

Phenotype

A B

O O

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 Genome evolution

 Model molecular phenotype as consequen ce of genotype

 Predict organismal phenotype

Challenge research

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Obtaining Genomic Sequences

洪士堯

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 Process of reconstructing an entire ge nome from relatively short random DNA fragments, called reads.

 Detect read overlaps and thereby progr essively reconstitute most of the geno me sequence.

Genome assembly

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Genome assembly

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 PCR ： Polymerase Chain Reactio n.

 Procedure ：

 Denaturation step

 Annealing step

 Extension/elongation step

PCR

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 DNA replication in the pres ence of both dNTPs and ddNT Ps will terminate the growi ng DNA strand at each base.

 In the presence of 5% ddTTP s and 95% dTTPs Taq polymer ase will incorporate a term inating ddTTP at each ‘T’

position in the growing DNA

Chain-termination method

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 Gel Electrophoresis se parates DNA by fragmen t size. The larger the DNA piece the slower i t will progress throug h the gel matrix towar d the positive cathode .

Chain-termination method (co

nt)

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 genomes commonly contain large redunda nt regions (repeats).

 regions where the statistical distribu tion of bases is significantly biased (lowcomplexity DNA)

Problems

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 new genomes from that spe cies or closely related s pecies are generally not assembled de novo.

 Using the reference genom e as a template.

After the first complete

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Modeling the evolution of genot ype

李宗燁

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 Alignment and Assembly

 Phylogenetic analysis

 Evolutionary relationships between DNA

Modeling the Evolution of Genot

ype

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 Genomes are compared by alignment

 Large scale

 Indicate changes in segment order and copy number

 Small scale

 Indicate specific base substitutions

Alignment and Assembly

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 Alignment

Alignment and Assembly

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 Assembly

 Primary challenge

 Distinguish spurious sequence similaritie s from those due to common ancestry

Alignment and Assembly

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 Regions of genomes

 Subject to purifying selection

 Similarity of sequence is conserved

 Orthologous protein-coding regions

 Reliably aligned across great evolutionary distance s

 Between vertebrates and invertebrates

Alignment and Assembly

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 Regions of genomes

 Therefore, common to distinguish alignments of subregions

 Local alignment

 Used between conserved functional regions of mor e distantly related genomes

 Full genome alighnments

Alignment and Assembly

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 Applied to

 more than two species

 or to multiple gene copies within a species

 NP-hard.

 Considerable effort has been devoted

Phylogenetic analysis

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 Complicated by homologous recombinatio n

 Creates DNA molecules whose parts have diff erent evolutionary histories

Phylogenetic analysis

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 Balanced structural rearrangements

 Change the order and the orientation of the bases in the genome

 substitutions

 Segmental duplications/gains/losses

 Alter the number of copies of homologous bases

 Short indels

Evolutionary relationships between DNA

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 Construction of a mathematically and a lgorithmically tractable unified theor y

 remains a major challenge for the field.

Evolutionary relationships between DNA

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FROM GENOTYPE TO

PHENOTYPE

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Gregor Mendel

• Austrian Monk who experimented with pea plants

• He noticed that not all peas are the same:

• Green vs. yellow

• Tall vs. short

• Round vs. wrinkled

• He discovered that crossing peas depended on the genes of the plant rather than only the outward

appearance of the plant

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Phenotype vs. Genotype

 Phenotype: the physic al appearance of a pl ant or animal because of its genetic makeup (genotype)

 Genotype: genetic con stitution (makeup) of

www.ansi.okstate.edu/breeds/s wine/

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The Punnett Square

 A way for determining the genotype and phenotype of offspring

 Capital letters are assigned to domina nt genes and lower-case letters are as signed to recessive genes

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Using the Punnet Square

T T

 Purebred (homozygous) dominant – the genes only have the dominant trait in its code.

 Example – Dominant Tall -- TT

 Purebred (homozygous) recessive – the genes only have the recessive trait in its code.

 Example – Recessive short – tt

 Hybrid (heterozygous) – the genes are

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Massive increase in Sequencing Sp

eed

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New Methods of Exploring

 Cross Species

 History and dive rsity of life

 Climate, competi tor, disease

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More Studies will be Derived From E xperimental Data

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Single Specie

Human Genome Studies

Number of

Genes Single Multiple

Frequency of genetic defects

Rare (<

1%) Common (>

1%)

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Association studies are critical to the study of complex diseases

Association

Tag, or genotype, SNPs on the basis of Linkage Disequilibrium patterns.

Select tags to provide as much information about surrounding region based on association with

untagged SNPs.

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Genome-Wide Association (GWA) addresses

some of these issues.

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GWA has multiple advantages

Discovery

Studies not limited to current biological knowledge

Quantitative

Better characterize complex, quantitative traits

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GWA has multiple advantages

Discovery

Studies not limited to current biological knowledge Coronary Heart Disease (CHD)

Type 2 Diabetes

Recent GWA studies discovered:

Associated regions containing no annotated genes

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GWA has multiple advantages

Cardiac Arrhythm

ias

Quantitative

Better characterize complex, quantitative traits

Identification of polymorphism accounting for variance of quantitative trait

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Going forward

Association Studies

Cannot provide unambiguous identification of causal genes

But

can highlight pathways and mechanisms of particular interest.

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Leading to systems-level understand ing of genetics and disease

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And Better Medicine!

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Databases

• ENCODE

• Epigenetics roadmap

• modENCODE

• EMSEMBL

• UCSC Gene Browser

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Epigenetics, RNA, Protein

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Epigenetics, RNA, Protein

 Can’t be directly measured

 Inferred by mathematical model

 Markov Models

 Factor Graphs

 Bayesian Networks

 Markov Random Fields

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Classification and Regression mod el

 Classification of Epigenetic, Transcripit ional, Proteomic state to predict phenoty pe to genotype

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MDS

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Clustering analysis

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Cox Regression

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Looking ahead to applicatio n

田耕豪廖奎達

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 Medicine a. Cancer b. Vaccine c. Stem cell

 Agriculture

Applications

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 Cancer

Genomic modifications are the source o f nearly all cancers.

Applications

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Applications

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 Acute Myeloid Leukemia ( 急性骨髓性白血病 )

 骨髓性造血芽細胞異常

增殖的血液惡性腫瘤

Applications

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Applications

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 Coding mutations identified in eight p rimary tumor–relapse pairs

Applications

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Applications

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Applications

 High-throughput genomics data

 Vaccine design

 Treatment of disease

 Infectious disease

 Autoimmune diseases

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Applications

 Vaccine

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Applications

 Vaccine

 Every year in February…

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Application

 Stem-cell

 Genomic variants

 Epigenetic state

 Expression pattern

 Induced pluripotent stem (iPS) cel

ls and lineage-specific directly r

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Application

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Application

 Next step…

 Integrating advances of different res earch fields

 Combining above into mathematical mod els

 Build comprehensive and computable mo dels

 So we can…

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Integrating Genome