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Mutations

Mutations

Mutations

Mutations

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.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...

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...

Viral Mutations

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

C4 Pathway and CAM

C4 Pathway and CAM

Most plants use the C3 pathway for carbon fixation. However, some plants, such as sugar cane, corn, and cacti that grow in hot conditions, use alternative pathways to fix carbon and conserve energy loss due to photorespiration. Photorespiration is the process that occurs when the oxygen concentration is high. Under such conditions, the rubisco enzyme in the Calvin cycle binds O2 instead of CO2, which halts photosynthesis and consumes energy.
C4 Pathway
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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

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Identifying mutated driver pathways in cancer by integrating multi-omics data.

Jingli Wu¹, Qirong Cai², Jinyan Wang¹

¹Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin 541004, China; College of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, China.

Computational Biology and Chemistry

|April 9, 2019

Summary

This summary is machine-generated.

Identifying cancer driver pathways is crucial for personalized medicine. This study introduces a new computational model and algorithm (PGA-MWS) that effectively identifies these pathways using multi-omics data.

Keywords:

Cancer Driver pathway Model Multi-omics data Parthenogenetic algorithm

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Area of Science:

Computational Biology
Genomics
Cancer Research

Background:

Driver pathways are critical in cancer formation and progression.
Identifying these pathways is essential for developing precision medicine strategies.

Purpose of the Study:

To propose an improved maximum weight submatrix problem model for identifying cancer driver pathways.
To develop a novel parthenogenetic algorithm (PGA-MWS) to solve this model.

Main Methods:

Integration of multi-omics data: somatic mutations, copy number variations, and gene expression.
Development of a model considering gene coverage, mutual exclusivity, and correlation.
Implementation of a parthenogenetic algorithm with a short chromosome code and greedy recombination operator.

Main Results:

The proposed PGA-MWS algorithm effectively identifies gene sets with high coverage and moderate mutual exclusivity.
PGA-MWS demonstrates superior performance and scalability compared to GA, MOGA, and iMCMC algorithms.
Identified gene sets are significantly enriched in known cancer signaling pathways, implicating known oncogenes and tumor suppressors.

Conclusions:

The developed computational approach provides a valuable tool for detecting cancer pathways.
This method aids in understanding cancer mechanisms and offers potential for precision medicine applications.