Detecting copy number variation with mated short reads

Medvedev, Paul; Fiume, Marc; Dzamba, Misko; Smith, Ted; Brudno, Michael

doi:10.1101/gr.106344.110

Cited by 159 publications

(144 citation statements)

References 39 publications

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“…To correctly identify these events, two additional algorithms must be implemented. The first uses an approach[82] similar to that used for the analysis of array-based Comparative Genomic Hybridizatin (aCGH) data. Bins of a width defined by the user are constructed to span the genome, and the number of aligned reads in each bin is recorded.…”

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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“…Also tools exist which combine techniques already during the detection, e.g. inGAPsv [31], CNVer [32], GASVPro [33], SVseq2 [34], or the method by Nord et al [35]. The method presented here is based on paired-end mapping.…”

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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“…To overcome these limitations, we augment the tumor adjacency graph to a network circulation problem (Medvedev et al 2010). For a region e with respective read coverage n e and t e in normal and tumor sample, we reinterpret the convex negative log emission probabilities from our HMM as the cost of flow along the corresponding edge e. For tumor adjacency edges, denoted T E , we assign a constant cost S E (e∈T E ) per unit of flow that reflects the confidence of the edge based on breakpoint mappability.…”

Section: Using Maximum Likelihood For Counting Copiesmentioning

confidence: 99%

“…In this network, a unit of flow corresponds to an additional copy of a genomic region. Edges use a convex flow cost function (Medvedev et al 2010) that equals the negative log emission probabilities from our HMM.…”

Section: Overview Of the Cougar Algorithmmentioning

confidence: 99%

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Identification of complex genomic rearrangements in cancers using CouGaR

et al. 2016

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The genomic alterations associated with cancers are numerous and varied, involving both isolated and large-scale complex genomic rearrangements (CGRs). Although the underlying mechanisms are not well understood, CGRs have been implicated in tumorigenesis. Here, we introduce CouGaR, a novel method for characterizing the genomic structure of amplified CGRs, leveraging both depth of coverage (DOC) and discordant pair-end mapping techniques. We applied our method to whole-genome sequencing (WGS) samples from The Cancer Genome Atlas and identify amplified CGRs in at least 5.2% (10+ copies) to 17.8% (6+ copies) of the samples. Furthermore, ∼95% of these amplified CGRs contain genes previously implicated in tumorigenesis, indicating the importance and widespread occurrence of CGRs in cancers. Additionally, CouGaR identified the occurrence of ‘chromoplexy’ in nearly 63% of all prostate cancer samples and 30% of all bladder cancer samples. To further validate the accuracy of our method, we experimentally tested 17 predicted fusions in two pediatric glioma samples and validated 15 of these (88%) with precise resolution of the breakpoints via qPCR experiments and Sanger sequencing, with nearly perfect copy count concordance. Additionally, to further help display and understand the structure of CGRs, we have implemented CouGaR-viz, a generic stand-alone tool for visualization of the copy count of regions, breakpoints, and relevant genes.

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Detecting copy number variation with mated short reads

Cited by 159 publications

References 39 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

Identification of complex genomic rearrangements in cancers using CouGaR

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