Structural Variation Detection

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Structural Variation Detection


Review Paper digit

Table of contents

• Detection of structural DNA variation from next generationsequencing data: a review of informatic approaches

• The software pipeline digit


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Detection of structural DNA variation from next generation sequencingdata: a review of informatic approaches

Authors: Haley J. Abel1, Eric J. Duncavage2

(1) Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

(2) Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA


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Definition

Structural DNA variation is generally defined asvariation in a DNA region larger than 1 kb andincludes several classes such as translocations,inversions, insertions/deletions and copy numbervariations (CNVs).


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Methods

• Cytogenetics:unbiasedBUT limited resolution/sensitivity (350-500 band level)

• FISH - Fluorescence in situ hybridization:increased resolution, ability to test fixed interphase cells, faster turnaround time,greater sensitivityBUT evaluation of multiple loci requires multiple probes/assays ⇒ increasingcomplexity

• Microarrays:especially reliable for CNV and loss of heterozygosityBUT unable to detect balanced translocations

• Next Generation Sequencing:ability to detect full range of genetic variation ⇒ potential to streamline testing byusing a single analysis platformBUT dependent on coverage ⇒ susceptible to GC bias


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NGS - Methods

• Depth of coverage analysis

......

• Discordant read pair analysis

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• Split read analysis

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Tools


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Translocation and Inversion Detection


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Translocation and Inversion Detection

Discordant pair analysis:

• sensitive but low breakpoint resolution and low specificity• repetetive regions on top of beeing a source of false positives drivetranslocations (difficult to separate from false positives)

• Many methods try heuristic cut offs to improve specificity:• VariationHunter and Hydra consider multiple, high scoring mappings if

available• GASVPRO tries to improve specificity by combining discordant pair

and coverage analysis

Split read analysis: excellent breakpoint resolution (up to single baseresolution), but requires much higher coverages.


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Copy Number Variation Detection


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Discordant pair analysis:• performs best on large deletions. struggles with dublications• cannot detect large insertions with the usual strategy due to pairs notspanning the dublication

• cannot detect large insertions with the usual strategy due to pairs notspanning the dublication

• Pindel pieces translocation calls together via pattern growth algorithmto find large insertions


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Copy Number Variation DetectionDepth of coverage analysis:

• DNA• Main problem is accounting for factors that modify read depth like GC

bias• event-wise testing (EWT) algorithms rely purely on deviations in

coverage from the sample’s mean depth. GC content is adressed byanalysing the genome bin wise.

• SegSeq, CNVnator, CNAseg, CNV-seq compare the same region acrossmultiple samples (control samples). Methods make also use ofbins/partitions and rely on coverage ratios which permit finer CNVmapping.

• Exome• target-capture-data increases GC bias• small size of targets makes paired normals or population controls a

requirement


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• Exome methods calculate local CNV first and then merge themtogether with various strategies

• CONTRA: uses circular binary segmentation for merging• CoNVEX: denoises coverage ratios with a discrete wavelet transformand then uses a Hidden Markov Model to identify gains and losses

• ExomeCNV: models B-allele frequencies to detect loss ofheterozygosity

• Some methods try to find sporadic CNVs in population exome data bynormalizing read count with principal component analysis


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Insertion and Deletion Detection


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Insertion and Deletion Detection

• Alignment based:• offered by many packages: SAMtools, GATK, VarScan• usually rely on probabilistic models to make indel calls• Dindel and Stampy rely on this methods but employ filters to

differentiate common errors from true indels.• all of these methods require considerable validation• insertion detection is limited to 15% of total read length

• Split read based:• Suitable for medium sized indels• High false-positive rate, because no probabilistic models discriminate

between alignment errors and true events


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Conclusion

• There is currently no single informatic method capable of identifyingthe full range structural DNA variation.

• multiple complementary tools are required for robust variant detection

• Since methods can perform differently based on assay design,extensive validation is required for clinical use.


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digit - A tool for detection and identification of genomicinter-chromosomal translocations

Authors: Richard Meier1,4, Stefan Graw1,4, Julian R Molina3, PeterBeyerlein1, Devin Koestler2, Jeremy Chien4

(1) Technical University of Applied Sciences Wildau, 15745 Wildau, Germany(2) Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160(3) Department of Medical Oncology, Mayo Clinic, Rochester, MN 55905(4) Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS 66160


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Goals of the project

• Interchromosomal translocation detection utilizing mate-pairsequencing data

• Handle artifacts and robustly remove false positive calls

• Investigate translocation profiles of populations / trait associatedgroups


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Mate-pair sequencing

sequencing

adapter ligationfragmentation

circularisation

fragmentation

genome / chromosome

template

terminalfragment

read1 read2


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digit overview

MVM

Den

sity

01

23

4

1.0 1.5 2.0

rejected approved

chromosome_1

chromosome_2

read_1 read_2

preprocessed read pairs

retain discordantlymapping read pairs

find read pairclusters

cluster_Bcluster_A . . .. . .calculate MVMs foreach pair and filterout low value pairs

recluster remainingread pairs

compare samples and search forgroup associations

called translocations

chr14:1573290-158941 & chr22:2732247-2735312

chr2:11002738-11002738 & chr3:3763766-3766175

chr11:1573290-158941 & chr17:1147275-11149839

chr5:25819112-25821940 & chr9:5151006-5154147. . . . . . . . .. . .

sample_1

sample_4

sample_5

sample_9

discordant read pair cluster

group associated super cluster

concordant pairs

discordant pairs

threshold


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Mapping validity measure (MVM)

... ...

AC T GG G A CT A C T ACG TA C G T

AC T GG G A CT G C T ACG G AC CC A GG CT

G A CT A C T ACG

TA C G T

G AC CC A GG CT

2kb

mapper assignsread to region

mapper assignsread to region

chromosome A

chromosome B

G T A T C C CA A TC G C AT ......

......

but

• The two reads of a read pair are remapped to both regions the mapping softwareoriginally assigned them to.

• If a read maps equally well to both regions it is impossible to resolve the readpair’s origin and it is rejected.

• The MVM judges how ambiguous the mappability of a read pair is.

• The MVM distribution of concordant (well behaved) read pairs in a sample areused as internal standard to determine a filtering threshold.


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Simulated data

lStructural Variation Detection

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Real dataSamples achieved a good separation between ambiguous and distinct readpairs via MVM thresholds across the board.

concordantdiscordantthreshold

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sample LU526

N = 749 Bandwidth = 0.02034

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sample LU748

N = 461 Bandwidth = 0.04017

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02

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8

sample LU271

N = 641 Bandwidth = 0.01287

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sample LU820

N = 534 Bandwidth = 0.02189

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sample LU1160

N = 268 Bandwidth = 0.05798

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sample LU1184

N = 370 Bandwidth = 0.04009

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sample LU1434

N = 391 Bandwidth = 0.02477

Den

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1.0 1.5 2.0 2.50

24

68

sample LU1466

N = 585 Bandwidth = 0.01317

Den

sity


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Real data

• We processed 20 patient samples from a non-cancer background and35 patient samples with a lung cancer background.

• After comparing the two populations we retrieved 218 sample specificevents, 160 of which were from cancer.

• 328 translocation calls were shared between 2 or more samples

• 16 translocations were shared between cancer samples exclusively.

• 13 translocations shared between cancer and normal samples werelabeled potentially disease relevant.


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Translocations exclusively found in cancer


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Translocations enriched in cancer


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Conclusion

• The method sucessfully reduces the false positives rate.

• Group comparision and population analysis is working, but will requiremore samples to make reliable judgements in the future.

• Comparisions with other tools are running as we speak.

• Combining strategies from different tools might be valuable to lookinto in future projects.


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Questions

?Structural Variation Detection

Education

Structural Variation Detection