Genome structural variation discovery and genotyping

Alkan, Can; Coe, Bradley P.; Eichler, Evan E.

doi:10.1038/nrg2958

Cited by 1,360 publications

(1,360 citation statements)

References 116 publications

(185 reference statements)

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“…We posit that this reflects both a technological limitation and an ascertainment bias as a result of the mutation severity. Affordable whole-genome sequencing [29] has revealed a plethora of uncharacterized genetic variation below the lower limits of arrayCGH and SNP array platforms, which rapidly lose genome-wide sensitivity below 50 kbp for most commercial arrays [30]. The number of CNVs per individual increases linearly as sizes approach 100 kbp (closely related to the de novo rate and matching observations of selection against large CNVs), but then begins to increase exponentially for CNVs less than 10 kbp in size ( Figure 1B) [31].…”

Section: Size Spectrum Of Copy Number Variationmentioning

confidence: 99%

“…The analysis of copy number and structural variation is frequently an afterthought requiring specialized and computationally intensive methods [4, 29,30]. No method is comprehensive and each differs in its sensitivity as a function of size and class of CNVs.…”

Section: Size Spectrum Of Copy Number Variationmentioning

confidence: 99%

“…No method is comprehensive and each differs in its sensitivity as a function of size and class of CNVs. Read-pair methodologies, for example, are most sensitive to events between 40 bp and 1 kbp (depending on library insert sizes and consistency) [30][31][32][33]. Readdepth methodologies are powerful for detecting copy number changes greater than 10 kbp and are dependent on sequence coverage, which limits the number of genomes that can be analyzed [29,30,[34][35][36][37].…”

Section: Size Spectrum Of Copy Number Variationmentioning

confidence: 99%

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A genetic model for neurodevelopmental disease

Coe¹,

Girirajan²,

Eichler³

2012

Current Opinion in Neurobiology

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The genetic basis of neurodevelopmental and neuropsychiatric diseases has been advanced by the discovery of large and recurrent copy number variants significantly enriched in cases when compared to controls. The pattern of this variation strongly implies that rare variants contribute significantly to neurological disease; that different genes will be responsible for similar diseases in different families; and that the same "primary" genetic lesions can result in a different disease outcome depending potentially on the genetic background. Next-generation sequencing technologies are beginning to broaden the spectrum of disease-causing variation and provide specificity by pinpointing both genes and pathways for future diagnostics and therapeutics.

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Section: Size Spectrum Of Copy Number Variationmentioning

confidence: 99%

Section: Size Spectrum Of Copy Number Variationmentioning

confidence: 99%

Section: Size Spectrum Of Copy Number Variationmentioning

confidence: 99%

See 1 more Smart Citation

A genetic model for neurodevelopmental disease

Coe¹,

Girirajan²,

Eichler³

2012

Current Opinion in Neurobiology

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“…Besides different experimental techniques, there are many computational approaches for structural variation detection [24]. A straight forward idea to detect mutations would be to fully assemble the genome under consideration, the so-called donor genome , and to align it to a reference sequence.…”

Section: Introductionmentioning

confidence: 99%

“…Basically, there are three classes of methods to identify structural variations from those mappings. (See [24] or [29] for reviews.) (1) Significant fluctuations of the coverage of the reference by mappings can indicate copy number changes.…”

Section: Introductionmentioning

confidence: 99%

Unraveling overlapping deletions by agglomerative clustering

Wittler

2013

BMC Genomics

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BackgroundStructural variations in human genomes, such as deletions, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Methods like paired-end mapping allow to simultaneously analyze data from several samples in order to, e.g., distinguish tumor from patient specific variations. However, it has been shown that, especially in this setting, there is a need to explicitly take overlapping deletions into consideration. Existing tools have only minor capabilities to call overlapping deletions, unable to unravel complex signals to obtain consistent predictions.ResultWe present a first approach specifically designed to cluster short-read paired-end data into possibly overlapping deletion predictions. The method does not make any assumptions on the composition of the data, such as the number of samples, heterogeneity, polyploidy, etc. Taking paired ends mapped to a reference genome as input, it iteratively merges mappings to clusters based on a similarity score that takes both the putative location and size of a deletion into account.ConclusionWe demonstrate that agglomerative clustering is suitable to predict deletions. Analyzing real data from three samples of a cancer patient, we found putatively overlapping deletions and observed that, as a side-effect, erroneous mappings are mostly identified as singleton clusters. An evaluation on simulated data shows, compared to other methods which can output overlapping clusters, high accuracy in separating overlapping from single deletions.

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Introduction and Historical Overview of DNA Sequencing

Nelson

Snyder

Gardner

et al. 2011

CP Molecular Biology

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The process of DNA sequencing has made tremendous strides in throughput, improved accuracy, ease of production, and lowered cost. As the practice of DNA sequencing has improved, so has the downstream data analysis with sophisticated databases and bioinformatics tools. Together, these advances have enlarged the number of applications upon which DNA sequencing can be brought to bear. This introductory unit provides a description of DNA sequencing with a focus on current and "NextGen" (second and third generation) automated technologies and applications. Supplement 96 Figure 7.0.2 General strategy for DNA sequencing.To sequence a fragment of DNA, a set of radiolabeled single-stranded oligonucleotides is generated in four separate reactions. In each of the four reactions, the oligonucleotides have one fixed end and one end that terminates sequentially at each A, T, G, or C, respectively. The products of each reaction are fractionated by electrophoresis on adjacent lanes of a high-resolution polyacrylamide gel. After autoradiography, the DNA sequence can be "read" directly from the gel.

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Genome structural variation discovery and genotyping

Cited by 1,360 publications

References 116 publications

A genetic model for neurodevelopmental disease

A genetic model for neurodevelopmental disease

Unraveling overlapping deletions by agglomerative clustering

Introduction and Historical Overview of DNA Sequencing

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