ArticleBioinformatics (Oxford, England)2010
CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 64 citations in OpenAlex.
- Shall genomic correlation structure be considered in copy number variants detection?Briefings in bioinformatics · 2021Article
- SIns: A Novel Insertion Detection Approach Based on Soft-Clipped Reads.Frontiers in genetics · 2021Article
- Statistical Considerations on NGS Data for Inferring Copy Number Variations.Methods in molecular biology (Clifton, N.J.) · 2021Review
- EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.Nucleic acids research · 2019Article
- MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples.Scientific reports · 2018Article
- A Total-variation Constrained Permutation Model for Revealing Common Copy Number Patterns.Scientific reports · 2017Article
- SegCorr a statistical procedure for the detection of genomic regions of correlated expression.BMC bioinformatics · 2017Article
- PSE-HMM: genome-wide CNV detection from NGS data using an HMM with Position-Specific Emission probabilities.BMC bioinformatics · 2016Article
- Copy number alterations detected by whole-exome and whole-genome sequencing of esophageal adenocarcinoma.Human genomics · 2015Article
- Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm.BMC medical genomics · 2015Article
- Identification of copy number variants in whole-genome data using Reference Coverage Profiles.Frontiers in genetics · 2015Article
- CRCDA--Comprehensive resources for cancer NGS data analysis.Database : the journal of biological databases and curation · 2015Article
- CNV-CH: A Convex Hull Based Segmentation Approach to Detect Copy Number Variations (CNV) Using Next-Generation Sequencing Data.PloS one · 2015Article
- Computational characterisation of cancer molecular profiles derived using next generation sequencing.Contemporary oncology (Poznan, Poland) · 2015Review
- Expanding the computational toolbox for mining cancer genomes.Nature reviews. Genetics · 2014Review
- Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine.Genome medicine · 2014Review
- Next-generation sequencing in the clinic: promises and challenges.Cancer letters · 2013Review
- Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives.BMC bioinformatics · 2013Article
- Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma.Nature genetics · 2012Article
- Genome-wide identification of significant aberrations in cancer genome.BMC genomics · 2012Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
motivationDNA copy number aberration (CNA) is a hallmark of genomic abnormality in tumor cells. Recurrent CNA (RCNA) occurs in multiple cancer samples across the same chromosomal region and has greater implication in tumorigenesis. Current commonly used methods for RCNA identification require CNA calling for individual samples before cross-sample analysis. This two-step strategy may result in a heavy computational burden, as well as a loss of the overall statistical power due to segmentation and discretization of individual sample's data. We propose a population-based approach for RCNA detection with no need of single-sample analysis, which is statistically powerful, computationally efficient and particularly suitable for high-resolution and large-population studies.
resultsOur approach, correlation matrix diagonal segmentation (CMDS), identifies RCNAs based on a between-chromosomal-site correlation analysis. Directly using the raw intensity ratio data from all samples and adopting a diagonal transformation strategy, CMDS substantially reduces computational burden and can obtain results very quickly from large datasets. Our simulation indicates that the statistical power of CMDS is higher than that of single-sample CNA calling based two-step approaches. We applied CMDS to two real datasets of lung cancer and brain cancer from Affymetrix and Illumina array platforms, respectively, and successfully identified known regions of CNA associated with EGFR, KRAS and other important oncogenes. CMDS provides a fast, powerful and easily implemented tool for the RCNA analysis of large-scale data from cancer genomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.