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What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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A biologically inspired measure for coexpression analysis.

Sanghamitra Bandyopadhyay1, Malay Bhattacharyya

  • 1Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, India. sanghami@isical.ac.in

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 14, 2011
PubMed
Summary

A new measure, BioSim, enhances gene coexpression analysis by capturing nonlinear dependencies missed by traditional methods. This tool improves the identification of gene relationships and expression patterns across various biological contexts.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene coexpression analysis is crucial for understanding gene function and regulatory networks.
  • Existing similarity measures, like the correlation coefficient, primarily capture linear dependencies and struggle with nonlinear relationships in gene expression data.
  • There is a need for robust measures that accurately quantify pairwise gene coexpression, accounting for both similarity and deviation.

Purpose of the Study:

  • To introduce a novel measure, BioSim, for quantifying pairwise gene coexpression.
  • To address the limitations of existing measures in capturing nonlinear dependencies in gene expression data.
  • To provide a comprehensive framework for coexpression analysis, including differential expression and dynamics.

Main Methods:

  • Developed BioSim, a novel measure based on the aggregation of stepwise relative angular deviation of gene expression vectors.
  • Evaluated BioSim's performance against existing measures using synthetic and biological datasets.
  • Integrated BioSim with module-finding algorithms and gene ontology for statistical validation.

Main Results:

  • BioSim effectively captures both linear and nonlinear dependencies in gene expression, outperforming traditional measures.
  • Statistical analysis confirmed BioSim's significance in identifying coexpressed gene modules.
  • Extensions of BioSim successfully identified expression variability across phenotypes, differential expression patterns, and coexpression dynamics.

Conclusions:

  • BioSim offers a more accurate and comprehensive approach to modeling pairwise gene coexpression.
  • The BioSim framework provides advanced capabilities for analyzing gene expression patterns, dynamics, and relationships.
  • This novel measure and its extensions hold significant potential for advancing biological data analysis and discovery.