A geometric approach for classification and comparison of structural variants

Sindi, Suzanne; Helman, Elena; Bashir, Ali Kashif; Raphael, Benjamin J.

doi:10.1093/bioinformatics/btp208

Cited by 147 publications

(148 citation statements)

References 38 publications

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“…The most basic algorithm is that applied by Breakdancer[77], Breakway[78], SVDetect[79], BreakSeq[80] and GASV[81]. These programs use mapping distance data provided through the paired-end alignment statistics to estimate the average fragment size of the library.…”

Section: Massively Parallel Sequencingmentioning

confidence: 99%

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Gundry

Vijg

2012

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

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DNA mutations are the source of genetic variation within populations. The majority of mutations with observable effects are deleterious. In humans mutations in the germ line can cause genetic disease. In somatic cells multiple rounds of mutations and selection lead to cancer. The study of genetic variation has progressed rapidly since the completion of the draft sequence of the human genome. Recent advances in sequencing technology, most importantly the introduction of massively parallel sequencing (MPS), have resulted in more than a hundred-fold reduction in the time and cost required for sequencing nucleic acids. These improvements have greatly expanded the use of sequencing as a practical tool for mutation analysis. While in the past the high cost of sequencing limited mutation analysis to selectable markers or small forward mutation targets assumed to be representative for the genome overall, current platforms allow whole genome sequencing for less than $5,000. This has already given rise to direct estimates of germline mutation rates in multiple organisms including humans by comparing whole genome sequences between parents and offspring. Here we present a brief history of the field of mutation research, with a focus on classical tools for the measurement of mutation rates. We then review MPS, how it is currently applied and the new insight into human and animal mutation frequencies and spectra that has been obtained from whole genome sequencing. While great progress has been made, we note that the single most important limitation of current MPS approaches for mutation analysis is the inability to address low-abundance mutations that turn somatic tissues into mosaics of cells. Such mutations are at the basis of intra-tumor heterogeneity, with important implications for clinical diagnosis, and could also contribute to somatic diseases other than cancer, including aging. Some possible approaches to gain access to low-abundance mutations are discussed, with a brief overview of new sequencing platforms that are currently waiting in the wings to advance this exploding field even further.

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Section: Massively Parallel Sequencingmentioning

confidence: 99%

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Gundry

Vijg

2012

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

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“…The similarity score used in our clustering approach follows an idea introduced by Dew et al [23] for the analysis of mate pairs in assemblies and used for structural variation detection by Sindi et al [22]. Based on the discretized approach explained in "Background" (minimum and maximum concordant mapping distance D min and D max ), mappings and the implied location and size of the deletion are represented as a trapezoid in the two-dimensional space: The right end of the left read defines the left boundary of the start coordinate of the deletion (on the x-axis), and the left end of the right read the right boundary of the end coordinate (on y-axis).…”

Section: Methodsmentioning

confidence: 99%

“…Or one can pool the data and afterwards only select those calls solely based on tumor data [22]. Only recently, joint analysis of several data sets have been proposed [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Agglomerative clustering is utilized to cluster mappings by similarity. The similarity score is based on a three-dimensional representation of mappings and deletions similar to the two-dimensional representation introduced by Dew et al [23] and used for structural variation detection by Sindi et al [22] in the tool GASV. With this representation, we obtain a score that captures both the putative location and size of the deletion.…”

Section: Introductionmentioning

confidence: 99%

“…[5,6,22,33,37,38], assign all given mappings to clusters, where each cluster corresponds to one deletion that is supported by all mappings in the cluster. Figure 2 shows an example for such a deletion cluster.…”

Section: Introductionmentioning

confidence: 99%

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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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A Survey of Copy‐Number Variation Detection Tools Based on High‐Throughput Sequencing Data

Lee

Park

2012

CP Human Genetics

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Copy-number variation (CNV) is a major class of genomic variation with potentially important functional consequences in both normal and diseased populations. Remarkable advances in development of next-generation sequencing (NGS) platforms provide an unprecedented opportunity for accurate, high-resolution characterization of CNVs. In this unit, we give an overview of available computational tools for detection of CNVs and discuss comparative advantages and disadvantages of different approaches.

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A geometric approach for classification and comparison of structural variants

Cited by 147 publications

References 38 publications

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Unraveling overlapping deletions by agglomerative clustering

A Survey of Copy‐Number Variation Detection Tools Based on High‐Throughput Sequencing Data

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