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Updated: Apr 15, 2026
11:02
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
Published on: October 18, 2013
20.1K
Structural variation discovery in the cancer genome using next generation sequencing: computational solutions and
Biao Liu1, Jeffrey M Conroy1, Carl D Morrison1
1Center for Personalized Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA.
Oncotarget
|April 8, 2015
Summary
Somatic structural variations (SVs) in cancer genomes are identified using Next-Generation Sequencing (NGS). This review guides the analysis of NGS data for accurate SV detection and interpretation in cancer research.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Somatic structural variations (SVs) are key drivers of carcinogenesis.
- Next-Generation Sequencing (NGS) is crucial for cancer genome analysis.
- Accurate identification of SVs requires advanced computational tools.
Purpose of the Study:
- To review current analytical tools for SV detection in NGS-based cancer studies.
- To provide a practical guide for analyzing and interpreting NGS data for SVs.
- To summarize SV types, NGS signatures, and computational methods.
Main Methods:
- Overview of computational programs for SV detection.
- Summary of common SV groups and NGS signatures.
- Discussion of principles, similarities, and differences of existing tools.
Main Results:
- Comprehensive review of SV detection tools for NGS cancer data.
- Detailed summary of SV characteristics and detection signatures.
- Analysis of computational methods and unresolved issues in the field.
Conclusions:
- Effective analysis of NGS data is essential for SV detection in cancer.
- This review offers a practical guide for researchers in the field.
- Understanding computational tools aids in interpreting cancer genome SVs.
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