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Related Concept Videos

Mutations01:35

Mutations

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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.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...
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Mismatch Repair01:20

Mismatch Repair

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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.
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Ensemble learning-based predictor for driver synonymous mutation with sequence representation.

Chuanmei Bi1, Yong Shi1, Junfeng Xia2

  • 1School of Biomedical Engineering, Anhui Medical University, Hefei, China.

Plos Computational Biology
|January 6, 2025
PubMed
Summary
This summary is machine-generated.

Identifying driver synonymous mutations in cancer is crucial. Our new tool, EPEL, uses ensemble learning and novel features like DNA shape to accurately predict mutation effects, outperforming existing methods and correlating with patient outcomes.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Synonymous mutations, previously overlooked, are increasingly recognized for their significant role in disease, especially cancer.
  • Accurate identification of driver synonymous mutations is essential for understanding cancer development, but current methods face data limitations.

Purpose of the Study:

  • To develop a novel computational method for predicting the functional impact of synonymous mutations in human cancers.
  • To investigate the utility of sequence-based features and deep learning representations for this task.

Main Methods:

  • Explored sequence-based features (DNA shape, physicochemical properties, nucleotide encoding) and deep learning features.
  • Developed EPEL (Effect Predictor for Synonymous Mutations), an ensemble learning model combining five tree-based predictors.
  • Optimized feature selection to maximize predictive accuracy.

Main Results:

  • EPEL demonstrated superior performance compared to state-of-the-art methods on an independent test set.
  • Incorporated DNA shape and deep learning features, representing a novel approach to assessing synonymous mutation impact.
  • Identified a significant correlation between EPEL effect scores and patient outcomes across various cancer types.
  • Found that deep learning DNA sequence representations did not significantly improve driver synonymous mutation identification in this context.

Conclusions:

  • EPEL provides a powerful and flexible tool for precisely targeting driver synonymous mutations in cancer research.
  • The developed web server (http://ahmu.EPEL.bio/) facilitates broader accessibility and application of EPEL for researchers.
  • Highlights the importance of integrating diverse features, including DNA shape, for accurate synonymous mutation effect prediction.