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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...

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Gene expression data analysis using a novel approach to biclustering combining discrete and continuous data.

Yann Christinat1, Bernd Wachmann, Lei Zhang

  • 1Laboratory for Computational Biology and Bioinformatics, School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Station 14, CH-1015 Lausanne, Switzerland. yann.christinat@epfl.ch

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 8, 2008
PubMed
Summary

This study introduces a novel biclustering algorithm for gene expression data that avoids local maxima by combining discrete and continuous data searches. The method effectively identifies statistically significant and biologically relevant biclusters, as demonstrated on yeast and cancer datasets.

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Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data analysis often relies on classical clustering methods.
  • Biclustering offers a more advanced approach by grouping genes and conditions simultaneously.
  • Existing biclustering algorithms can be limited by heuristic initialization and convergence to local optima.

Purpose of the Study:

  • To develop a novel biclustering algorithm that overcomes limitations of existing methods.
  • To improve the detection of statistically significant and biologically relevant biclusters.
  • To enhance pattern detection in gene expression data through a hybrid discrete-continuous search strategy.

Main Methods:

  • A novel biclustering algorithm combining discrete and continuous data analysis.
  • Utilizing discrete biclustering results to initialize a local search on continuous data, avoiding heuristic initialization issues.
  • Designing biclusters with ordered rows and columns for enhanced pattern recognition, similar to Ordering Preserving Submatrix (OPSM).

Main Results:

  • The algorithm successfully identified statistically significant and biologically relevant biclusters in yeast, human cancer, and random datasets.
  • On the yeast genome, 89% of the largest non-overlapping biclusters were enriched with Gene Ontology annotations.
  • Comparative analysis showed superior efficiency over OPSM and ISA when incorporating gene and condition orders.

Conclusions:

  • The proposed algorithm effectively captures biologically relevant biclusters by leveraging ordered data.
  • This novel approach offers improved performance and avoids common pitfalls of heuristic-based biclustering methods.
  • The findings highlight the potential of this algorithm for advancing gene expression data analysis in various biological contexts.