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Old 02-10-2015, 09:30 AM   #1
Location: Paris

Join Date: Sep 2008
Posts: 69
Default ONCOCNV: a method to extract CNAs from amplicon (or targeted) sequencing data

We are happy to present ONCOCNV, a method to detect copy number alterations in amplicon or targeted sequencing data. The method can be applied to exome-seq data as well, but it will not adjust the profiles for contamination by normal cells or evaluate genotypes (LOH).

ONCOCNV was developed by OncoDNA with the collaboration with the Bioinformatics Laboratory of Institut Curie (Paris). It automatically computes, normalizes, segments copy number profiles, then calls copy number alterations. The user can provide any number of control samples in order to construct the baseline. However, we recommend to use at least three control samples. The more the better

Publication: Boeva,V. et al. (2014) Multi-factor data normalization enables the detection of copy number aberrations in amplicon sequencing data. Bioinformatics, 30(24):3443-3450. Link

Input for CNA detection: aligned single-end or paired-end data in the BAM format.
Output: Annotation of genes with copy number changes + visualization of the profile (.png).

Paper abstract:
Because of its low cost, amplicon sequencing, also known as ultra-deep targeted sequencing, is now becoming widely used in oncology for detection of actionable mutations, i.e. mutations influencing cell sensitivity to targeted therapies. Amplicon sequencing is based on the polymerase chain reaction amplification of the regions of interest, a process that considerably distorts the information on copy numbers initially present in the tumor DNA. Therefore, additional experiments such as single nucleotide polymorphism (SNP) or comparative genomic hybridization (CGH) arrays often complement amplicon sequencing in clinics to identify copy number status of genes whose amplification or deletion has direct consequences on the efficacy of a particular cancer treatment. So far, there has been no proven method to extract the information on gene copy number aberrations based solely on amplicon sequencing.
Here we present ONCOCNV, a method that includes a multifactor normalization and annotation technique enabling the detection of large copy number changes from amplicon sequencing data. We validated our approach on high and low amplicon density datasets and demonstrated that ONCOCNV can achieve a precision comparable with that of array CGH techniques in detecting copy number aberrations. Thus, ONCOCNV applied on amplicon sequencing data would make the use of additional array CGH or SNP array experiments unnecessary.

Last edited by valeu; 02-10-2015 at 09:34 AM.
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