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Related Concept Videos

Coordination of Gene Expression Processes in Bacteria01:29

Coordination of Gene Expression Processes in Bacteria

The DNA replication, transcription, and translation processes are intricately coupled in bacteria, allowing efficient gene expression and rapid protein synthesis. While this physical and functional coordination is advantageous, it introduces challenges that bacteria overcome through specific regulatory mechanisms.Coupling of Replication, Transcription, and TranslationThe coupling of replication, transcription, and translation is a hallmark of bacterial gene expression. As the replisome unwinds...
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

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Published on: February 9, 2017

A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series.

Sara C Madeira1, Arlindo L Oliveira

  • 1Knowledge Discovery and Bioinformatics (KDBIO) group, INESC-ID, Lisbon, Portugal. smadeira@kdbio.inesc-id.pt

Algorithms for Molecular Biology : AMB
|June 6, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces e-CCC-Biclustering, an efficient algorithm for finding gene expression patterns over time. It helps discover gene regulatory mechanisms by identifying co-regulated genes in biological processes.

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Last Updated: Jun 22, 2026

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Understanding gene expression dynamics is crucial for deciphering complex biological processes.
  • Biclustering algorithms are vital for discovering local expression patterns and regulatory mechanisms.
  • Analyzing time-series gene expression data requires efficient methods to identify coherent temporal responses.

Purpose of the Study:

  • To propose e-CCC-Biclustering, an algorithm for identifying maximal contiguous column coherent biclusters with approximate expression patterns.
  • To develop an efficient biclustering method with polynomial time complexity for time-series gene expression data.
  • To extend biclustering capabilities to handle missing values, anticorrelated/scaled patterns, and varying error tolerances.

Main Methods:

  • Developed e-CCC-Biclustering algorithm utilizing matrix discretization and string processing techniques for efficiency.
  • Implemented extensions for handling missing data, discovering anticorrelated and scaled patterns.
  • Introduced a scoring criterion combining statistical significance and similarity for overlapping biclusters.

Main Results:

  • Demonstrated the effectiveness of e-CCC-Biclustering on real transcriptomic data from Saccharomyces cerevisiae.
  • Showcased the algorithm's relevance in discovering regulatory modules in response to heat stress.
  • Highlighted the advantage of approximate pattern matching over exact matching methods.

Conclusions:

  • Efficiently identifying co-regulated genes with similar expression patterns is key to understanding gene regulatory networks.
  • The proposed methodology aids in discovering relevant biological phenomena and strengthening evidence for specific regulatory mechanisms.
  • e-CCC-Biclustering provides a valuable tool for advancing research in gene regulatory network analysis.