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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Related Experiment Video

Updated: Aug 17, 2025

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

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A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data.

Paul Bastide1, Charlotte Soneson2,3, David B Stern4

  • 1IMAG, Université de Montpellier, CNRS, Montpellier, France.

Molecular Biology and Evolution
|December 12, 2022
PubMed
Summary
This summary is machine-generated.

A new simulation framework generates realistic interspecies RNA-Seq datasets by incorporating phylogenetic relationships. This tool aids in comparing differential gene expression analysis methods for evolutionary studies.

Keywords:
RNA-Seqcomparative transcriptomicscrayfishdifferential gene expressionorthologous genesphylogenetic comparative methods

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Area of Science:

  • Evolutionary biology
  • Bioinformatics
  • Genomics

Background:

  • Interspecies RNA-Seq data offers insights into gene expression evolution.
  • Single-species differential expression analysis is well-established.
  • A gap exists in simulation tools for interspecies gene expression data.

Purpose of the Study:

  • To develop a novel simulation framework for interspecies RNA-Seq data.
  • To generate realistic count datasets considering phylogenetic relationships.
  • To benchmark differential expression analysis methods in an interspecies context.

Main Methods:

  • Developed a simulation framework integrating RNA-Seq and phylogenetic comparative methods.
  • Generated realistic count datasets accounting for interspecies phylogenetic relationships.
  • Conducted a simulation study mirroring a freshwater crayfish dataset.

Main Results:

  • The simulation framework successfully generates realistic interspecies RNA-Seq data.
  • A benchmark revealed strengths and weaknesses of classical and phylogenetic differential expression analysis methods.
  • Reanalysis of the crayfish dataset provided new insights.

Conclusions:

  • The new simulation tool addresses a critical need in interspecies gene expression research.
  • The benchmark facilitates informed selection of analysis methods for comparative transcriptomics.
  • The framework and benchmark are available in the R package compcodeR.