array informatics mark gerstein

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Array Informatics

Mark Gerstein


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CEGS Informatics Developing Tools and Technical Analyses Related to

Genome Technologies

• Main Genome Technologies Tiling Arrays Next Generation Sequencing

• Main Applications Transcript mapping Protein-DNA Binding CGH

• Transitioning to Seq....


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Tools & Tech. Analyses for Processing of Genome Technology Data

• Normalizing Arrays and Measuring & Correcting Artifacts COP - Correcting positional artifacts [Yu et al. NAR '07] Efficient Pseudomedian Calculation - for Tiling Array Scoring

[Royce et al., BMC Bioinfo. '07] Measuring Mismatch Effects

[Seringhaus et al., BMC Genomics (submitted)] Removing Seq. Effects [Royce et al., Bioinfo. '07] NN Prediction of Probe Intensity - measuring & exploiting specific

cross-hyb [Royce et al. NAR '07]

• Simulating NextGen Sequencing ChipSeqSim - simulating ChIP Seq [Zhang et al., PLoS CB '08]


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Tools & Tech. Analyses for Genome Structural Variation

Breakptr - HMM-based Array Segmentation for CNV detection [Korbel et al., PNAS '07]

MSB - Mean-shift-based Array Segmentation for CNV detection with extension to sequencing [Wang et al. Gen. Res. (submitted)]

PEMer - Paired-end Mapping for SV Dectection with simulation calibration and breakpoint DB [Korbel et al., GenomeBiol. (submitted)]

Long-SV-Assembly Simulations [Du et al., Nat. Meth. (submitted)] SD-CNV-CORR - Approach for correlating the occurrence of CNVs

and SDs with genomic features (particularly repeats) [Kim et al., Genome Res. (submitted)]


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A Starting Point: Noisy Raw Signal from Tiling Arrays (Transcription)

Joh

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l. (2

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TIG

, 2

1,

93

-10

2.

Li et al., PLOS one (2007)


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Perfect Match Specific Cross-hyb. Non-specific Cross-hyb.

Specific & Non-specific Cross-Hyb.• Perfect match (PM):

probe binding intended target• Specific cross-hyb.: probes binding non-PM

targets with a small number of mismatches• Non-specific cross-hyb.: probes binding

targets with many mismatches, due to general stickiness of oligos


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Non-Specific Cross Hyb. (Sequence Effects)


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Creation of Standardized Datasets for

Quantifying Effect of Mismatches

[Seringhaus et al., BMC Genomics (in press)]

Types of Mispairs (probe on array is first)

No

rmal

ized

In

ten

sity

MM

M

M v

s. P

M

Yeast Human

GA

v A

G

CA

v A

C

GT

v T

G

CT

v T

C


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Sourc

e:

Royce

, T.E

., e

t al (2

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Bio

info

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cs, 23

, 9

88

-97

Avg. intensity of all background probes with a C at position 4

Avg. intensity of all background probes with a T at position 33

Observing Non-specific Cross-hyb. (Probe sequence effects)

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Iterated Quantile Normalization to

Correct for Non-specific Cross-hyb.

• Adapt Bolstad et al (2003) approach to tiling arrays

• Force distributions with a given nt at each position to be same

• Distributions at other positions now different so iterate

• Also, robust adaptation of Naef & Magnasco (2003)

T Royce et al (2007), Bioinformatics, 23, 988-97


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Measuring Specific Cross-Hyb

Source: Royce, T.E., et al (2007), Nucleic Acids Res., 23, 98-97


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Proof of principle test to “exploit” this

Figure from http://www.members.cox.net/amgough/Fanconi-genetics-genetics-primer.htm

• Using Cheng et al. (2005), predict gene expression levels (and profiles across tissues) for genes on part of chr. #6

• ...Based on closest cross-hyb tiles on part of chr. #7

• Then compare to measured levels and profile on #6



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Nearest Nbr Search on Virtual Tiling

Royce, T.E., et al (2007), Nucleic Acids Res., 23, 98-97


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Agreement between predicted tile expression profile and actual one


• Correlated predicted profiles with the actual profiles of gene expression across cell lines

• Much more correlation than expected by chance (dist. centered on 0)


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Royce, T. E. et al. Nucl. Acids Res. 2007 35:e99

Very Strong ROC Curve: Most genes are accurately detected using

nearest-neighbor features' signals

• Illustrates great magnitude of cross-hyb. on hi-density arrays

• High feature density arrays inadvertently resurrecting generic n-mer concept (van Dam & Quake, 2003)

• Suggests that tiling arrays could be exploited to create universal arrays

• Gold std. set of known expressed genes. How well do we find. • A set of known positives was defined as the Refseq genes with at

least 75% transfrag coverage. A set of known negatives was constructed by permuting the sequences in the set of known positives. For various thresholds, sensitivity and specificity were computed and then plotted.


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CEGS Informatics Credits

• Array Corrections J Rozowsky T Royce M Seringhaus

• PEMer, SD-CNV, BreakPtr P Kim J Korbel J Du X Mu A Abyzov N Carriero

• Experimental M Snyder S Weissman A Urban


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Computational Methods for SV Characterization

Mark Gerstein


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Computational Methods for SV Characterization

Segmenting Array CGH data

Building a PEM pipeline

Correlating SVs and SDs with Repeats


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http://breakptr.gersteinlab.orgKorbel*, Urban* et al., PNAS (2007)

-0.5

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NO T TO SCALEACGTGACAC ATAAGCACACCA ATTGCTTGAGGGACCT TAGGCACAGT TAAC ATGATAAGCACACCA ATTGCTTGAGGTGACDNA

sequence

BreakPtr HMM

• To get highest resolution on breakpoints need to smooth & segment the signal

• BreakPtr: prediction of breakpoints, dosage and cross-hybridization using a system based on Hidden Markov Models


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High resolution of tiling arrays allows statistical integration of nucleotide sequence patterns

>4-fold enrichment of the breakpoints of copy number variants near segmental duplications (SDs)[e.g. Sharp et al., Am. J. Hum. Genet. 2005; 77:78-88].


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BreakPtr statistically integrates array signal and DNA sequence signatures (using a discrete-valued bivariate HMM)

Korbel*, Urban* et al., PNAS (2007)

Transition A

Transition A’

Transition B

Transition B’

Duplication DeletionNormal

Sequence

Arr

ayva

lues

Sequence

Arra

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lues


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Gold standards

Training data

Parameter optimization

Model parameter estimation

Breakpointvalidation

[sequencing]

log (number of CNVs available for parameter estimation)10

10[core]

304[intermediate A]

1003[intermediate B]

2503[full]

SDs

norm

aliz

ed f

luor

esce

nt

inte

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log

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2

Max

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num

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of p

aram

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s pe

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ansi

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stat

e

‘Active’ approach for breakpoint identification: initial scoring with preliminary model, targeted validation (with sequencing),

retraining, and rescoring

HMM optimized iteratively (using Expectation Maximization, EM) Korbel*, Urban* et al., PNAS (2007)

CNV breakpoints sequenced in ~10 cases following BreakPtr analysis;

Median resolution <300 bp

No improvement in accuracy with higher resolution (9nt tiling)


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Moving Beyond Arrays, Computational Methods for Next-

Generation Sequencing:Paired End Mapping to Find SVs


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Overall Strategy for Analysis of

NextGen Seq. Data to Detect Structural Variants

[Korbel et al., Science ('07); Korbel et al., GenomeBiol. (submitted)]


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Simulation strategy

Simulation

Experiment454 sequencing [Korbel et al., GenomeBiol. (submitted)]


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Reconstruction efficiency at different coverage

[Korbel et al., GenomeBiol. (submitted)]


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Building a Database of Variants: Complexities

[Korbel et al., GenomeBiol. (submitted)]


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Analyzing Duplications in the Genome (SDs & CNVs)

pers. photo, see streams.gerstein.info

080907_SD_CNV_Slides_MBG_CEGS_PMK

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SEGMENTAL DUPLCATIONS AND COPY NUMBER VARIANTS ARE RELATED PHENOMENA AND HAVE BEEN CREATED BY SEVERAL DIFFERENT MECHANISMS

Copy Number Variants (CNV) Segmental Duplications (SD)

Fixation

Intra-species variation Fixed mutations(differences to other species)

NAHR (Non-allelic homologous recombination)

Flanking repeat(e.g. Alu, LINE…)

NHEJ (Non-homologous-end-joining)

No (flanking) repeats. In some cases <4bp microhomologies


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PERFORM LARGE SCALE CORRELATION ANALYSIS TO DETECT REPEAT SIGNATURES OF SDs AND CNVs

Survey a range of genomic features

Count the number of features in each genomic bin (100kb)

Calculate correlations / enrichments using robust stats

1

2

3

…ATCAAGG CCGGAA…

Exact match

Local environment

If exact CNV breakpoints are known, we can calculate the enrichment of repeat elements relative to the genome or relative to the local environment

[Kim et al. Gen. Res. (submitted, '08), arxiv.org/abs/0709.4200v1 ]


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SDs ARE CORRELATED WITH ALUS AND OTHER SDs

0.080.09

0.120.14 0.14 0.13

Alu association with SDs by age

90-92% 92-94% 94-96% 96-98% 98-99% >99%

• The co-localization of Alu elements with SDs is highly significant.

• Older SDs have a much higher association with Alus than younger SDs.

• SDs can mediate NAHR and lead to the formation of CNVs

• Such mechanisms (“preferential attachment”) are well studied in physics and should leads a very skewed (“power-law”) distribution of SDs.

•Hotspots[Kim et al. Gen. Res. (submitted, '08), arxiv.org/abs/0709.4200v1 ]

f

Number of SDs in Genomic Bin

Oc

cu

rre

nc

e


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0.0480.0006

0.07390.0466

ASSOCIATIONS ARE DIFFERENT FOR SDs AND CNVs

Alu

0.92

CNV association with repeats

Microsatellite

<0.001

Pseudogenes

0.046

LINE

0.001

0.07

0.27

0.0940.21

Alu

<0.001

SD association with repeats

Microsatellite

<0.001

Pseudogenes

0.046

LINE

0.001


CNVAssociationwith SDs

>99% SDs* CNVs

0.31

0.11

CNVs ARE LESS ASSOCIATED WITH SDs THAN THE GENERAL SD TREND


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uOldLow seq-ID (%)

YoungHigh seq-ID (%)

CNVs

Fixation Aging (~40Mya)

NAHR

NHEJ

SDs

AluSD

LINEMicrosatellite

SubtelomeresFragile sites

Alu Burst (40 MYA)

AFTER THE ALU BURST, THE IMPORTANCE OF ALU ELEMENTS FOR GENOME REARRANGEMENT DECLINED RAPIDLY

• About 40 million years ago there was a burst in retrotransposon activity

• The majority of Alu elements stem from that time

• This, in turn, led to rapid genome rearrangement via NAHR

• The resulting SDs, could create more SDs, but with Alu activity decaying, their creation slowed



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Future Directions

• Simulations of SV Assembly• Analysis of Split Reads• Detailed Analysis of SV and CNVs

with Genomic Features


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CEGS Informatics Credits

• Array Corrections J Rozowsky T Royce M Seringhaus

• PEMer, SD-CNV, BreakPtr P Kim J Korbel J Du X Mu A Abyzov N Carriero

• Experimental M Snyder S Weissman A Urban

array informatics mark gerstein

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