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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Gene Evolution - Fast or Slow?02:05

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.
In contrast, regions which code...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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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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Updated: May 8, 2026

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

Phylogenomic distance method for analyzing transcriptome evolution based on RNA-seq data.

Xun Gu1, Yangyun Zou, Wei Huang

  • 1State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China.

Genome Biology and Evolution
|August 14, 2013
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method to measure genome expression distance using RNA-seq data, advancing evolutionary genomics. This approach accounts for variations, enabling robust phylogenomic analysis across species.

Keywords:
RNA-seqgenome expression distancetranscriptome evolution

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Last Updated: May 8, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Microarray technology advanced transcriptome evolution studies but had limitations.
  • Next-generation sequencing, specifically RNA-seq, offers improved resolution for genomic analysis.
  • Existing analytical frameworks for RNA-seq data lack specific designs for evolutionary genomics.

Purpose of the Study:

  • To develop a novel method for estimating genome expression distance from RNA-seq data.
  • To create a distance measure with explicit interpretations for gene expression evolution models.
  • To address limitations in current analytical frameworks for evolutionary genomics.

Main Methods:

  • Developed a new statistical method for estimating genome expression distance.
  • Incorporated considerations for data overdispersion, gene length variation, and sequencing depth.
  • Applied the method to mammalian RNA-seq data for validation.

Main Results:

  • The proposed distance measure provides explicit interpretations within gene expression evolution models.
  • The method effectively accounts for technical variations inherent in RNA-seq data.
  • Demonstrated the utility of the expression distance in phylogenomic analyses.

Conclusions:

  • The new genome expression distance method enhances RNA-seq data analysis for evolutionary genomics.
  • This approach offers a robust tool for comparative genomics and phylogenomic studies.
  • The method facilitates a deeper understanding of transcriptome evolution across species.