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Cancers Originate from Somatic Mutations in a Single Cell

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

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Related Experiment Video

Updated: Dec 27, 2025

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

SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.

¹Bioinformatics Research and Early Development, Roche Sequencing Solutions, Belmont, CA, USA. Li_tai.fang@roche.com.

Methods in Molecular Biology (Clifton, N.J.)

|March 4, 2020

Summary

This summary is machine-generated.

Identifying cancer mutations with next-generation sequencing is challenging due to sequencing errors. SomaticSeq uses machine learning to integrate multiple algorithms, significantly improving the accuracy of detecting these crucial somatic mutations.

Keywords:

Bioinformatics Ensemble method Machine learning Sequencing Somatic mutations

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Last Updated: Dec 27, 2025

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

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Published on: October 18, 2013

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Comparative Lesions Analysis Through a Targeted Sequencing Approach

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Published on: November 5, 2019

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Next-generation sequencing (NGS) is standard for identifying somatic mutations in cancer genomes by comparing tumor and normal tissues.
High sequencing error rates in NGS data can obscure true somatic mutations, especially in whole-genome or exome sequencing.
Accurate somatic mutation detection is critical for understanding cancer development and for clinical applications.

Purpose of the Study:

To develop a computational method that improves the accuracy of somatic mutation detection from next-generation sequencing data.
To address the challenge of distinguishing true somatic mutations from sequencing errors in cancer genomes.
To leverage machine learning to enhance the reliability of somatic mutation call sets.

Main Methods:

SomaticSeq integrates outputs from multiple established somatic mutation detection algorithms.
A machine learning classifier is trained to differentiate between genuine somatic mutations and sequencing errors.
The approach is applied to analyze next-generation sequencing data from cancer genomes.

Main Results:

SomaticSeq significantly enhances the accuracy of somatic mutation detection compared to individual algorithms.
The machine learning component effectively reduces false positive calls arising from sequencing errors.
Improved precision in somatic mutation identification is achieved, leading to more reliable cancer genome analysis.

Conclusions:

SomaticSeq offers a robust computational solution for improving somatic mutation detection accuracy in cancer genomics.
The integration of multiple algorithms and machine learning provides a powerful tool for cancer research.
This approach facilitates more reliable identification of driver mutations and genomic alterations in tumors.