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Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

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...
Cancers Originate from Somatic Mutations in a Single Cell02:21

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...
Mismatch Repair01:20

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

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Related Experiment Video

Updated: May 13, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

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

Published on: October 18, 2013

An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data.

Yuichi Shiraishi1, Yusuke Sato, Kenichi Chiba

  • 1Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. yshira@hgc.jp

Nucleic Acids Research
|March 9, 2013
PubMed
Summary

Empirical Bayesian mutation Calling (EBCall) improves somatic mutation detection, especially for low-frequency mutations in cancer genomes. This novel method enhances accuracy in complex tumor samples with varying sequencing depths.

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Comparative Lesions Analysis Through a Targeted Sequencing Approach
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Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

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Last Updated: May 13, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

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Published on: October 18, 2013

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • High-throughput sequencing has advanced cancer genome analysis, identifying numerous somatic mutations.
  • Current mutation calling methods struggle with low sequencing depths and tumor purity.
  • Accurate somatic mutation detection is crucial for understanding cancer heterogeneity.

Purpose of the Study:

  • To introduce a novel computational method for accurate somatic mutation detection.
  • To address the limitations of existing methods in low-coverage and low-purity scenarios.
  • To enable the identification of subclonal mutations for detailed tumor profiling.

Main Methods:

  • Developed Empirical Bayesian mutation Calling (EBCall) using an empirical Bayesian framework.
  • Estimated model parameters using sequencing data from multiple non-paired normal samples.
  • Validated the method on whole-exome sequencing data with varying depths (87.5-206.3x).

Main Results:

  • EBCall outperforms existing methods in detecting mutations with moderate allele frequencies.
  • The method accurately identifies low-allele frequency mutations (≤ 10%) within minor tumor subpopulations.
  • Demonstrated improved detection capabilities in challenging low-sequencing depth and tumor content scenarios.

Conclusions:

  • Empirical Bayesian mutation Calling (EBCall) offers a robust approach for somatic mutation detection.
  • The method facilitates the deciphering of fine substructures within tumor specimens.
  • EBCall enhances the accuracy of cancer genome analysis, particularly for subclonal mutations.