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Related Concept Videos

Viruses with RNA Genomes01:29

Viruses with RNA Genomes

RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
Size and Structure of Viral Genomes01:26

Size and Structure of Viral Genomes

Viral genomes exhibit remarkable diversity in size, structure, and composition, influencing their replication strategies and interactions with host cells. These genomes consist of either DNA or RNA and may be linear or circular. Additionally, they can be single-stranded or double-stranded, with each configuration affecting how the virus propagates within a host. RNA viruses, for instance, generally have smaller genomes than DNA viruses, a factor that contributes to their high mutation rates and...
Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
Viral Mutations00:36

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 for adaptive...
RNA Structure01:19

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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
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RNA Structure

Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
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Related Experiment Video

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Confocal Imaging of Double-Stranded RNA and Pattern Recognition Receptors in Negative-Sense RNA Virus Infection
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Published on: January 26, 2019

Genotype phenotype mapping in RNA viruses - disjunctive normal form learning.

Chuang Wu1, Andrew S Walsh, Roni Rosenfeld

  • 1School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA. chuangw@cs.cmu.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 2, 2010
PubMed
Summary

This study introduces new algorithms to predict RNA virus changes by learning genotype-phenotype relationships. These machine learning methods accurately map genetic sequences to viral traits like drug resistance.

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Area of Science:

  • Virology
  • Computational Biology
  • Machine Learning

Background:

  • RNA virus phenotypes arise from complex molecular mechanisms involving key residue changes.
  • Understanding genotype-phenotype relationships is crucial for predicting viral evolution and developing interventions.

Purpose of the Study:

  • To develop and evaluate Disjunctive Normal Form (DNF) learning algorithms for predicting RNA virus phenotypes from genotype data.
  • To assess the accuracy and efficiency of these algorithms on simulated and real-world viral datasets.

Main Methods:

  • Development of DNF learning algorithms to model genotype-phenotype functions as Boolean combinations of covariates.
  • Testing algorithm consistency and efficiency on simulated sequences.
  • Validation on diverse RNA virus datasets (drug resistance, antigenicity, pathogenicity) and the UCIs promoter gene dataset.
  • Comparison with existing machine learning algorithms using leave-one-out cross-validation.

Main Results:

  • The DNF learning algorithms demonstrated consistency and efficiency on simulated data.
  • Superior prediction accuracy was achieved compared to other machine learning algorithms on HIV drug resistance and UCIs promoter gene datasets.
  • The algorithms effectively inferred genotype-phenotype mappings from moderate-sized labeled sequence data, typical of mutagenesis experiments.
  • Greedy learning of DNFs from large datasets was also established.

Conclusions:

  • The developed DNF learning algorithms provide a powerful and accurate method for inferring genotype-phenotype mappings in RNA viruses.
  • These algorithms offer a significant advancement in predicting viral traits, with potential applications in drug resistance, antigenicity, and pathogenicity studies.
  • The publicly available Java implementation will facilitate further research and application in the field.