UCSC Genome Browser

Содержание

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UCSC Genome Browser

UCSC Genome Browser

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UCSC Genome Browser

UCSC Genome Browser

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UCSC Genome Browser

UCSC Genome Browser

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UCSC Genome Browser

UCSC Genome Browser

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UCSC Genome Browser

UCSC Genome Browser

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Complete human genome

Complete human genome

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Transposable Elements

45% of the human genome is occupied by transposons and transposon-like

Transposable Elements 45% of the human genome is occupied by transposons and
repetitive elements.
Barbara McClintock (1902-1992) in 50s.
Nobel prize in 1983

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Схожесть с ретровирусами
Retrovirus reverse transcripiton

http://www.youtube.com/watch?v=eS1GODinO8w

Class II

Class I – retrotransposons (via RNA

Схожесть с ретровирусами Retrovirus reverse transcripiton http://www.youtube.com/watch?v=eS1GODinO8w Class II Class I – retrotransposons (via RNA intermediate)
intermediate)

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Active

Non-Active

Active Non-Active

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First Layer of Genome Annotation

First Layer of Genome Annotation

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Epigenetics

Epigenetics

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Second Layer of Genome Annotation

Second Layer of Genome Annotation

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Second Layer of Genome Annotation

Second Layer of Genome Annotation

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Second Layer of Genome Annotation

Second Layer of Genome Annotation

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Second Layer of Genome Annotation

Second Layer of Genome Annotation

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Second Layer of Genome Annotation

Second Layer of Genome Annotation

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Third Layer of Genome Annotation

Third Layer of Genome Annotation

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Data Accumulation

Data Accumulation

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ENCODE: Encyclopedia of DNA Elements

ENCODE: Encyclopedia of DNA Elements

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Digital Universe

Like the Physical Universe the Digital Universe is also expanding but

Digital Universe Like the Physical Universe the Digital Universe is also expanding
much faster doubling every two years – and by 2020 will be 44 zettabytes (10^ 21)
Every second a new 205 000 bytes come to being
At the end of this lecture the digital universe will grow by 2 214 000 000 bytes or 2.2 GB.

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Digital Universe

Data Universe Will Expand To 44 Trillion GBs By 2020

Digital Universe Data Universe Will Expand To 44 Trillion GBs By 2020

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Что делать?

Что делать?

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Что получилось? (Success Stories)

Что получилось? (Success Stories)

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СКРЫТЫЕ ЦЕПИ МАРКОВА

СКРЫТЫЕ ЦЕПИ МАРКОВА

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Gene Prediction

Gene Prediction

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E0

E1

E2

E2

E1

E0

N

P

Eterm

P

Einit

polyA

5’ UTR

I0

I1

I2

I0

I1

I2

Esngl

Esngl

Einit

Eterm

forward strand

backward strand

3’ UTR

5’ UTR

3’ UTR

polyA

E- exons
I- introns
single exon
5’ UTRs

E0 E1 E2 E2 E1 E0 N P Eterm P Einit polyA

3’ UTRs
P- promoter region polyA site N- intergenic region

GeneMark HMM

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Promoter prediction

Hidden Markov model with six interpolated Markov chain submodels
upstream 1

Promoter prediction Hidden Markov model with six interpolated Markov chain submodels upstream
and 2,
TATA box, spacer,
Initiator
downstream.
Gaussian densities of DNA physicochemical properties.
Neural network classifier

McPromoter

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predict tissue-dependent changes in alternative splicing for thousands of exons.
1,014 features:

predict tissue-dependent changes in alternative splicing for thousands of exons. 1,014 features:
known motifs, new motifs, short motifs and features describing transcript structure
trained on RNA-seq data
single-layer logistic Bayesian network or neural network, or a weighted combination of single-layer decision trees.

Nature 2010

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Genome intrinsic organization can explain ,50% of the in vivo nucleosome positions
Probabilistic

Genome intrinsic organization can explain ,50% of the in vivo nucleosome positions
nucleosome–DNA interaction model - built on dinucleotide distrubution
Thermodynamic model for predicting nucleosome positions genome-wide.

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Schematic overview of epigenetic regulatory mechanisms.

Yonggang Zhou et al. Circ Res.

Schematic overview of epigenetic regulatory mechanisms. Yonggang Zhou et al. Circ Res.
2011;109:1067-1081

Copyright © American Heart Association, Inc. All rights reserved.

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Random Forest model predicts cancer mutation densities from epigenomic mark ups

Random Forest model predicts cancer mutation densities from epigenomic mark ups

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We have many experimental genome-wide annotations

Question 1: Are different annottaions correlated? To

We have many experimental genome-wide annotations Question 1: Are different annottaions correlated?
what extent?

Question 2: Can we find patterns in annotations?
(Unsupervised learning)

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Annotations under different conditions

Annotations under different conditions

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Как много данных?

Roadmap Epigenomics
~ 3 000 полногеномных данных
ENCODE Encyclopedia of Genomic

Как много данных? Roadmap Epigenomics ~ 3 000 полногеномных данных ENCODE Encyclopedia
Elements
~ 9000 полногеномных данных
International Cancer Genome Consortium
~ 20 000 patients (~50 типов рака)
The Cancer Genome Atlas
~ patients 11 000 (~33 типа рака)

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Открытые вопросы

Какие участки кода работают одновременно?
Как переключать режимы работы клетки?
Как перепрограммируется

Открытые вопросы Какие участки кода работают одновременно? Как переключать режимы работы клетки?
код для разных типов тканей?
Сколько механизмов регуляции существует в клетках (надежда на универсальность)?
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