Related Experiment Video
Updated: Mar 19, 2026
09:45
Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
12.2K
FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing
Ronglai Shen1, Venkatraman E Seshan2
1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA shenr@mskcc.org.
Nucleic Acids Research
|June 9, 2016
Summary
FACETS enables allele-specific copy number analysis (ASCN) from next-generation sequencing (NGS) data. This open-source tool accurately calls integer copy numbers, adjusting for tumor purity, ploidy, and heterogeneity in cancer sequencing.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Next-generation sequencing (NGS) provides valuable mutation data, but its utility can be extended to comprehensive genomic annotation.
- Accurate copy number alterations (CNAs) are crucial for cancer subtyping and treatment decisions, especially with targeted gene panels.
- Existing methods often struggle with tumor purity, ploidy, and clonal heterogeneity, limiting reliable interpretation of cancer gene copy number data.
Purpose of the Study:
- To develop and validate FACETS, an open-source bioinformatics tool for allele-specific copy number (ASCN) analysis using NGS data.
- To provide accurate, tumor purity-, ploidy-, and clonal heterogeneity-adjusted integer copy number calls for clinical cancer sequencing.
- To demonstrate the broad applicability of FACETS across different sequencing platforms, including whole genome, whole exome, and targeted gene panels.
Main Methods:
- Developed FACETS, a fully integrated, stand-alone pipeline for ASCN analysis.
- Incorporated post-processing of sequencing BAM files and joint segmentation of read counts.
- Implemented algorithms for integer copy number calls corrected for tumor purity, ploidy, and clonal heterogeneity.
Main Results:
- FACETS successfully performs ASCN analysis, enabling detection of deletions, copy-neutral loss-of-heterozygosity (LOH), and allele-specific gains/amplifications.
- The tool provides accurate integer copy number calls adjusted for critical confounding factors like tumor purity and ploidy.
- Demonstrated FACETS's application on The Cancer Genome Atlas (TCGA) lung adenocarcinoma whole-exome data and a clinical targeted gene panel.
Conclusions:
- FACETS is a versatile and robust open-source tool for allele-specific copy number analysis from various NGS data.
- The software facilitates reliable interpretation of cancer gene copy number alterations in both research and clinical settings.
- FACETS enhances the diagnostic and therapeutic utility of NGS by providing precise genomic annotation beyond mutation detection.
