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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Updated: Jan 7, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Benchmark and Evaluation for Somatic Structural Variants Detection with Long-read Sequencing Data.

Ziting Feng¹, Xuyan Liu¹, Yahui Liu¹

¹Laboratory of Omics Technology and Bioinformatics, Frontiers Science Center for Disease-related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.

Genomics, Proteomics & Bioinformatics

|December 31, 2025

Summary

This summary is machine-generated.

Detecting tumor-specific structural variations (somatic SVs) using long-read sequencing (LRS) is challenging. This study benchmarks 51 LRS strategies, finding no single method is optimal and highlighting areas for improvement in somatic SV detection tools.

Keywords:

Algorithm performance Benchmark strategy evaluation Long-read sequencing Sequencing depth Somatic structural variants detection

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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

Genomics
Cancer Genomics
Bioinformatics

Background:

Somatic structural variations (SVs) are key features of tumors but are difficult to detect comprehensively.
Long-read sequencing (LRS) offers potential for improved somatic SV detection due to its ability to span large genomic regions.
Current LRS-based somatic SV detection algorithms lack systematic performance characterization.

Purpose of the Study:

To rigorously evaluate the performance of diverse LRS-based somatic SV detection strategies.
To identify technical bottlenecks and limitations in current somatic SV detection methods.
To provide recommendations for optimizing LRS-based somatic SV detection and tool development.

Main Methods:

Evaluated 51 somatic SV detection strategies using LRS data.
Integrated 3 reference genomes, 2 aligners, 5 SV callers, and 5 processing methods.
Utilized simulated datasets and empirical data from HCC1395/HCC1395BL cell lines (ONT and PacBio platforms).

Main Results:

No single LRS-based strategy consistently outperformed others across all evaluated scenarios.
Workflows using germline SV callers showed high false-positive rates, unaffected by sequencing depth or tumor purity.
Challenges remain in detecting insertions, tandem repeat regions, and ultra-long SVs.

Conclusions:

Current LRS-based somatic SV detection tools require further refinement for consistent performance.
Germline SV caller-based workflows are not ideal for somatic SV detection due to high false positives.
This benchmark provides insights for selecting tools and advancing future LRS-based somatic SV detection methodologies.