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Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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SEG - A Software Program for Finding Somatic Copy Number Alterations in Whole Genome Sequencing Data of Cancer
Mucheng Zhang1, Deli Liu1, Jie Tang1
1Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA30602-7229, USA.
Computational and Structural Biotechnology Journal
|September 28, 2018
Summary
A new software tool, SEG, efficiently identifies small somatic copy number alterations (CNAs) in cancer genomes using whole genome sequencing (WGS) data. SEG improves upon existing methods by accurately detecting smaller events, aiding cancer research.
Area of Science:
- Genomics and Bioinformatics
- Cancer Research
- Computational Biology
Background:
- Whole genome sequencing (WGS) is crucial for identifying somatic copy number alterations (CNAs) in cancer.
- Existing software tools face challenges in detecting smaller CNAs and mitigating sequencing/mapping biases.
Purpose of the Study:
- To develop a novel software program, SEG (segment), for accurate CNA detection in cancer genomes.
- To enhance the identification of small CNAs often missed by other methods.
Main Methods:
- SEG utilizes mapped read or fragment density for CNA discovery.
- It normalizes data using log2-ratios of tumor to normal densities to reduce artifacts.
- Dynamic programming is employed to identify change-points and segment chromosomes for CNA identification.
Main Results:
- SEG successfully identifies segments with CNAs.
- Analyses using simulated and real WGS data demonstrate SEG's superior ability to detect smaller CNAs compared to existing tools.
Conclusions:
- SEG is an effective software tool for precise CNA identification in cancer genomics.
- Its enhanced sensitivity for small CNAs offers significant advantages for cancer research and diagnostics.
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