scholarly journals ClinCNV: novel method for allele-specific somatic copy-number alterations detection

2019 ◽  
Author(s):  
German Demidov ◽  
Stephan Ossowski
Keyword(s):  
Copy Number ◽  
Read Depth ◽  
Copy Number Alterations ◽  
Single Nucleotide Variants ◽  
Small Indels ◽  
Whole Exome ◽  
Allele Specific ◽  
Copy Number Changes ◽  
Ngs Data ◽  
Somatic Copy Number Alterations

AbstractMotivationLarge somatic copy number alterations (CNA), short indels and single nucleotide variants (SNVs) are playing important role in cancer development and can serve as a predictor for targeted therapy selection as well as prognostic factor. Genomic microarrays, FISH, MLPA and many other technologies are widely used for detection of CNAs. Whole-genome sequencing (WGS), whole-exome sequencing (WES) and targeted panel sequencing (TPS) are well established, highly accurate tools for detection of SNVs and small indels, but detection of larger structural variants using WGS, WES and TPS data remains challenging. We developed a tool for high-resolution allele-specific detection of somatic CNAs in NGS data using statistical approach.ResultsWe have developed a new method for read-depth and B-allele frequency (BAF) based multi-sample detection of copy-number changes in paired normal-tumor NGS data and showed its performance using large cohorts of WES and TPS sequenced samples.AvailabilityClinCNV is freely available on https://github.com/imgag/ClinCNV.

scholarly journals sCNAphase: using haplotype resolved read depth to genotype somatic copy number alterations from low cellularity aneuploid tumors

2016 ◽  
Vol 45 (5) ◽  
pp. e34-e34 ◽  
Author(s):  
Wenhan Chen ◽  
Alan J. Robertson ◽  
Devika Ganesamoorthy ◽  
Lachlan J.M. Coin
Keyword(s):  
Copy Number ◽  
Read Depth ◽  
Copy Number Alterations ◽  
Somatic Copy Number Alterations

scholarly journals Low-cost and clinically applicable copy number profiling using repeat DNA

2018 ◽  
Author(s):  
S Abujudeh ◽  
SS Zeki ◽  
MCV van Lanschot ◽  
M Pusung ◽  
JMJ Weaver ◽  
...  
Keyword(s):  
Copy Number ◽  
Large Scale ◽  
Low Cost ◽  
Clinical Use ◽  
Copy Number Alterations ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Repeat Dna ◽  
Gene Panels ◽  
Somatic Copy Number Alterations

AbstractLarge-scale cancer genome studies suggest that tumors are driven by somatic copy number alterations (SCNAs) or single-nucleotide variants (SNVs). Due to the low-cost, the clinical use of genomics assays is biased towards targeted gene panels, which identify SNVs. There is a need for a comparably low-cost and simple assay for high-resolution SCNA profiling. Here we present our method, conliga, which infers SCNA profiles from a low-cost and simple assay.

scholarly journals Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny

2017 ◽  
Author(s):  
Eugene Urrutia ◽  
Hao Chen ◽  
Zilu Zhou ◽  
Nancy R Zhang ◽  
Yuchao Jiang
Keyword(s):  
Copy Number ◽  
Cancer Genomics ◽  
Supplementary Information ◽  
Single Nucleotide Variants ◽  
Genetic Studies ◽  
Number Variation ◽  
Allele Specific ◽  
Copy Number Changes ◽  
Fine Resolution ◽  
Tumor Phylogeny

AbstractSummaryCopy number variation is an important and abundant source of variation in the human genome, which has been associated with a number of diseases, especially cancer. Massively parallel next-generation sequencing allows copy number profiling with fine resolution. Such efforts, however, have met with mixed successes, with setbacks arising partly from the lack of reliable analytical methods to meet the diverse and unique challenges arising from the myriad experimental designs and study goals in genetic studies. In cancer genomics, detection of somatic copy number changes and profiling of allele-specific copy number (ASCN) are complicated by experimental biases and artifacts as well as normal cell contamination and cancer subclone admixture. Furthermore, careful statistical modeling is warranted to reconstruct tumor phylogeny by both somatic ASCN changes and single nucleotide variants. Here we describe a flexible computational pipeline, MARATHON, which integrates multiple related statistical software for copy number profiling and downstream analyses in disease genetic studies.Availability and implementationMARATHON is publicly available at https://github.com/yuchaojiang/[email protected] informationSupplementary data are available at Bioinformatics online.

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