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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Gene Evolution - Fast or Slow?

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.
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Updated: Jun 12, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

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Published on: August 3, 2018

Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System

Weijia Xu1, Stuart Ozer, Robin R Gutell

  • 1Texas Advanced Computing Center, The University of Texas, Austin, Texas, USA.

Scientific and Statistical Database Management : International Conference, SSDBM ... : Proceedings. International Conference on Scientific and Statistical Database Management
|May 27, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational method for RNA structure analysis, improving the identification of base pair interactions in large sequence alignments. The approach enhances accuracy and efficiency for diverse phylogenetic classifications.

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • Comparative sequence analysis identifies RNA structures but faces computational and phylogenetic limitations.
  • Traditional methods struggle with large-scale alignments and diverse sequence origins.

Purpose of the Study:

  • To develop a computationally efficient method for RNA structure analysis using coevolutionary rates.
  • To improve the identification of base pair interactions in large and phylogenetically diverse RNA sequence datasets.

Main Methods:

  • Utilized coevolutionary rates among nucleotide positions based on phylogenetic relationships.
  • Developed a novel data schema for managing sequence information in a relational database.
  • Implemented the method using Microsoft SQL Server 2005 for analyzing 16S ribosomal RNA sequences.

Main Results:

  • Achieved 90% sensitivity in identifying base pair interactions in bacterial 16S ribosomal RNA sequences.
  • Demonstrated a 40-fold increase in scale and 50% better sensitivity compared to previous studies.
  • Identified covariation signals for cross-strand base stacking and other subtle RNA structural constraints.

Conclusions:

  • The proposed coevolutionary rate approach offers a scalable and sensitive method for RNA structure prediction.
  • This method enhances the discovery of structural elements and constraints in large RNA sequence datasets.
  • The findings contribute to a deeper understanding of RNA structural dynamics and functional implications.