High-resolution mapping of copy-number alterations with massively parallel sequencing

By Derek Y. Chiang, Gad Getz, David B. Jaffe, Michael J. T. O’Kelly, Xiaojun Zhao, Scott L. Carter, C. Russ, Chad Nusbaum, Matthew Meyerson, Eric S. Lander. Nature Methods, 2008

550 citations recorded by the source corpus.

Cancer results from somatic alterations in key genes, including point mutations, copy-number alterations and structural rearrangements. A powerful way to discover cancer-causing genes is to identify genomic regions that show recurrent copy-number alterations (gains and losses) in tumor genomes. Recent advances in sequencing technologies suggest that massively parallel sequencing may provide a feasible alternative to DNA microarrays for detecting copy-number alterations. Here we present: (i) a statistical analysis of the power to detect copy-number alterations of a given size; (ii) SegSeq, an algorithm to segment equal copy numbers from massively parallel sequence data; and (iii) analysis of experimental data from three matched pairs of tumor and normal cell lines. We show that a collection of ∼14 million aligned sequence reads from human cell lines has comparable power to detect events as the current generation of DNA microarrays and has over twofold better precision for localizing breakpoints (typically, to within ∼1 kilobase).

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2ff31eb4b7658ad9fe7aa0ae910af6f87d2e0c68

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Citations in the corpus

Next-Generation Genomics: an Integrative Approach (2010, unknown): “NGS can also detect copy-number variants and gene-fusion event”
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments (2010, methodology): “These high-throughput sequencing technologies have already been applied to monitor genome-wide transcription levels (mRNA-Seq), DNA-protein interactions (ChIP-Seq), chromatin structure, and DNA methylation [ 1-9 ].”
Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV (2011, methodology): “SegSeq (Chiang et al., 2009), for example, merges windows of a fixed length based on a log-ratio difference statistic.”
Personalized Copy-Number and Segmental Duplication Maps using Next-Generation Sequencing (2009, unknown): “Most of these approaches, however, assay only the “unique” regions of the genome 21,23,24.”
Advances in understanding cancer genomes through second-generation sequencing (2010, background): “and to resolve copy number alterations at very high resolutio”
Copy number variation detection and genotyping from exome sequence data (2012, background): “These biases, as well as the sparse nature of the capture, make exome sequencing unsuitable for ‘‘traditional’’ CNV detection algorithms, such as raw read-depth (Alkan et al. 2009; Chiang et al. 2008; Yoon et al. 2009), read-pair alignment (Korbel et al.”
GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers (2011, methodology): “Tools for estimating and segmenting copy-number values from sequencing coverage data already exist [5], and these segmented copy-number profiles can, with only slight modification, be run through the GISTIC 2.”
Absolute quantification of somatic DNA alterations in human cancer (2012, unknown): “(7) (4) then becomes h x h g i q i ( ) ( ( )) , = + m Œ and the corresponding likelihood calculations are performed under these transformations.”
DELLY: structural variant discovery by integrated paired-end and split-read analysis (2012, methodology): “Availability: DELLY is available at www.korbel.embl.de/software.html Contact: tobias.rausch@embl.de”
VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. (2012, background): “Most of these approaches (Campbell et al. 2008; Alkan et al. 2009; Chiang et al. 2009; Yoon et al. 2009; Abyzov et al. 2011) would be confounded by exome data sets, because of the biases introduced by hybridization and the sparse and uneven coverages throughout the genome.”
Genome Sequencing of Pediatric Medulloblastoma Links Catastrophic DNA Rearrangements with TP53 Mutations (2012, unknown): “, 2009), and read-depth analysis (Abyzov et al., 2011; Chiang et al., 2009; Waszak et al., 2010).”
Prospective derivation of a Living Organoid Biobank of colorectal cancer patients (2015, methodology): “The frequency of hypermutated organoid cultures in our patient panel (20%; 4 of 20) agreed with the reported frequency in a much larger cohort of clinical samples and display comparable somatic copy number alterations (SCNAs) (Figure 2B; Table S1D) (Bass et al., 2011; Cancer Genome Atlas Network, 20”
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives (2013, methodology): “…disease and normal tissues taken from the same patient and sequenced on the same platform, the effect of GC bias can be effectively cancelled out by comparing the paired genomes directly at each region [31,33,41], thereby taking advantage of the similarity of GC bias between the two paired samples.”
cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate (2012, methodology): “SeqSeg (7) is a class (b) method that was designed to identify copy number aberrations (CNAs) in tumor samples by comparing them to references, that is, their matched controls.”
Genome structural variation discovery and genotyping (2011, methodology): “Read-depth approaches using NGS data were first applied to define rearrangements in cance”