IfCNV: A novel isolation-forest-based package to detect copy-number variations from various targeted NGS datasets.

ABSTRACT: Copy-number variations (CNVs) are an essential component of genetic variation distributed across large parts of the human genome. CNV detection from next-generation sequencing data and artificial intelligence algorithms have progressed in recent years. However, only a few tools have taken advantage of machine-learning algorithms for CNV detection, and none propose using artificial intelligence to automatically detect probable CNV-positive samples. The most developed approach is to use a reference or normal dataset to compare with the samples of interest, and it is well known that selecting appropriate normal samples represents a challenging task that dramatically influences the precision of results in all CNV-detecting tools. With careful consideration of these issues, we propose here ifCN

SUBMITTER: Cabello-Aguilar S 

PROVIDER: S-EPMC9547229 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature