ArticlePLoS computational biology2008
Evaluation of paired-end sequencing strategies for detection of genome rearrangements in cancer.
Article in PLoS computational biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Double insertion of transposable elements provides a substrate for the evolution of satellite DNA.Genome research · 2018Article
- Assembly and diploid architecture of an individual human genome via single-molecule technologies.Nature methods · 2015Article
- Integrated sequence and expression analysis of ovarian cancer structural variants underscores the importance of gene fusion regulation.BMC medical genomics · 2015Article
- Inferring the global structure of chromosomes from structural variations.BMC genomics · 2015Article
- Expanding the computational toolbox for mining cancer genomes.Nature reviews. Genetics · 2014Review
- Open adjacencies and k-breaks: detecting simultaneous rearrangements in cancer genomes.BMC genomics · 2014Article
- Site-specific Genome Editing in PBMCs With PLGA Nanoparticle-delivered PNAs Confers HIV-1 Resistance in Humanized Mice.Molecular therapy. Nucleic acids · 2013Article
- THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data.Genome biology · 2013Article
- Reprever: resolving low-copy duplicated sequences using template driven assembly.Nucleic acids research · 2013Article
- Application of next generation sequencing to human gene fusion detection: computational tools, features and perspectives.Briefings in bioinformatics · 2013Review
- Identification of cancer fusion drivers using network fusion centrality.Bioinformatics (Oxford, England) · 2013Article
- nFuse: discovery of complex genomic rearrangements in cancer using high-throughput sequencing.Genome research · 2012Article
- ChopSticks: High-resolution analysis of homozygous deletions by exploiting concordant read pairs.BMC bioinformatics · 2012Article
- Article
- Massively parallel sequencing approaches for characterization of structural variation.Methods in molecular biology (Clifton, N.J.) · 2012Review
- Analysis of microsatellite variation in Drosophila melanogaster with population-scale genome sequencing.PloS one · 2012Article
- An integrative probabilistic model for identification of structural variation in sequencing data.Genome biology · 2012Article
- Review
- Simultaneous structural variation discovery among multiple paired-end sequenced genomes.Genome research · 2011Article
- Transcriptional consequences of genomic structural aberrations in breast cancer.Genome research · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Paired-end sequencing is emerging as a key technique for assessing genome rearrangements and structural variation on a genome-wide scale. This technique is particularly useful for detecting copy-neutral rearrangements, such as inversions and translocations, which are common in cancer and can produce novel fusion genes. We address the question of how much sequencing is required to detect rearrangement breakpoints and to localize them precisely using both theoretical models and simulation. We derive a formula for the probability that a fusion gene exists in a cancer genome given a collection of paired-end sequences from this genome. We use this formula to compute fusion gene probabilities in several breast cancer samples, and we find that we are able to accurately predict fusion genes in these samples with a relatively small number of fragments of large size. We further demonstrate how the ability to detect fusion genes depends on the distribution of gene lengths, and we evaluate how different parameters of a sequencing strategy impact breakpoint detection, breakpoint localization, and fusion gene detection, even in the presence of errors that suggest false rearrangements. These results will be useful in calibrating future cancer sequencing efforts, particularly large-scale studies of many cancer genomes that are enabled by next-generation sequencing technologies.
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