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Related Concept Videos

Mutations01:39

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Mutations01:35

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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
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Each human somatic cell contains 6 billion base pairs of DNA. Each base pair is 0.34 nm long, meaning each diploid cell contains a staggering 2 meters of DNA. This long DNA strand is packed inside a nucleus measuring only 10-20 microns in diameter with the help of specialized DNA-binding proteins called histones. Together they form a compact DNA-protein complex called chromatin. The chromatin is further compacted into higher-order structures. The highest level of compaction is achieved during...
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Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter? 
The chromatin
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Updated: Jan 27, 2026

Wild-type Blocking PCR Combined with Direct Sequencing as a Highly Sensitive Method for Detection of Low-Frequency Somatic Mutations
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RMut: R package for a Boolean sensitivity analysis against various types of mutations.

Hung-Cuong Trinh1, Yung-Keun Kwon2

  • 1Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

Plos One
|March 20, 2019
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Summary
This summary is machine-generated.

RMut is a new R package for analyzing Boolean network sensitivity to diverse mutations, including user-defined types. It efficiently identifies critical mutations and predicts drug targets in large-scale biological networks.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Boolean network models are widely used for in silico studies of biological network sensitivity to mutations.
  • Existing tools lack the capability to analyze sensitivity against a broad spectrum of mutations, including user-defined ones.

Purpose of the Study:

  • To develop RMut, an R package for comprehensive analysis of Boolean network sensitivity to various mutations.
  • To enable the examination of network sensitivity to user-defined mutations, mutation areas, and duration times.

Main Methods:

  • RMut employs a parallel algorithm using the OpenCL library for efficient analysis of large-scale networks.
  • The package supports well-known node-based and edgetic mutations, as well as novel user-defined mutations.
  • RMut allows specification of mutation area and duration for precise sensitivity analysis.

Main Results:

  • Real biological networks showed highest sensitivity to overexpression/state-flip (node-based) and edge-addition/-reverse (edgetic) mutations.
  • Edgetic mutations demonstrated superior predictive power for drug targets compared to node-based mutations.
  • Analysis of double edge-removal mutations revealed significant synergistic effects.

Conclusions:

  • RMut provides a flexible and efficient platform for analyzing network sensitivity to a wide array of mutations.
  • The findings highlight the importance of considering diverse mutation types for a comprehensive understanding of network behavior.
  • RMut facilitates advanced network sensitivity analysis, aiding in biological discovery and drug target identification.