NGScopy

NGScopy detects copy number variations (CNVs) from next-generation sequencing data across whole genome sequencing (WGS), whole exome sequencing (WES), and targeted panel sequencing (TPS), enabling quantitative CNV identification even from sparse sequencing coverage.

Key Features:

• Quantitative CNV detection: Provides quantitative identification of CNVs across large-scale and small-scale events.
• Intragenic CNV sensitivity: Detects intragenic CNVs that may be missed by other methods.
• Comparable resolution: Achieves CNV detection resolution comparable to human SNP Array 6.0 for relevant events.
• Sparse-coverage capability: Operates effectively on sparse sequencing coverage typical of targeted panels or low-depth datasets.
• Chromosome-level parallelization: Allows parallel processing by chromosomes to leverage multiple processors or cores.
• Multi-variant integration: Integrated with the UNCseq targeted panel to support detection of CNVs alongside single nucleotide variants (SNVs) and small insertions/deletions (indels) across a panel of over 200 cancer-associated genes.
• RNA-seq validation support: Supports integration with RNA-seq data to validate DNA-detected SNVs when RNA-seq coverage is sufficient.

Scientific Applications:

• Clinical cancer genomics: Applied to CNV profiling and variant detection in oncology studies.
• 07-0120 lung cancer cohort analysis: Used to analyze 100 snap-frozen lung cancer specimens from the 07-0120 tissue cohort lacking same-patient germline DNA with validation against Sanger sequencing and SNP Array.
• RNA–DNA cross-validation: Employed within the UNCqeR assay to cross-validate DNA-detected SNVs using RNA-seq when coverage permits.
• 11-1115 lung cancer cohort validation: Applied to an independent lung cancer tissue collection (11-1115 cohort) with available germline DNA and confirmation using assays performed in a CLIA-certified laboratory.

Methodology:

Employs a novel algorithm tailored for samples with sparse sequencing coverage, provides quantitative CNV calling, and supports chromosome-level parallelization.