A classification model for distinguishing copy number variants from cancer-related alterations.

ABSTRACT:

Background

Both somatic copy number alterations (CNAs) and germline copy number variants (CNVs) that are prevalent in healthy individuals can appear as recurrent changes in comparative genomic hybridization (CGH) analyses of tumors. In order to identify important cancer genes CNAs and CNVs must be distinguished. Although the Database of Genomic Variants (DGV) contains a list of all known CNVs, there is no standard methodology to use the database effectively.

Results

We develop a prediction model that distinguishes CNVs from CNAs based on the information contained in the DGV and several other variables, including segment's length, height, closeness to a telomere or centromere and occurrence in other patients. The models are fitted on data from glioblastoma and their correspon

SUBMITTER: Ostrovnaya I 

PROVIDER: S-EPMC2897829 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature