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Related Experiment Video

Updated: Nov 8, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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KnowSeq R-Bioc package: The automatic smart gene expression tool for retrieving relevant biological knowledge.

Daniel Castillo-Secilla1, Juan Manuel Gálvez1, Francisco Carrillo-Perez1

  • 1Department of Computer Architecture and Technology,University of Granada. C.I.T.I.C., Periodista Rafael Gómez Montero 2, 18014, Granada, Spain.

Computers in Biology and Medicine
|April 19, 2021
PubMed
Summary

KnowSeq software automates gene expression analysis for disease gene signature identification. This bioinformatics tool achieves high classification rates for diseases like breast and lung cancer.

Keywords:
BioconductorBioinformaticsClassificationEnrichmentGene expression

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Area of Science:

  • Bioinformatics and Computational Biology
  • Genomics and Transcriptomics
  • Biostatistics

Background:

  • Gene expression analysis is crucial for understanding disease mechanisms and identifying biomarkers.
  • Existing bioinformatics tools often lack integration, scalability, or advanced feature selection capabilities.
  • Automating complex transcriptomic data analysis pipelines is essential for efficient knowledge discovery.

Purpose of the Study:

  • To introduce KnowSeq, a scalable and modular R/Bioc package for automating gene expression analysis.
  • To provide a unified environment for identifying disease-specific gene signatures from diverse transcriptomic data.
  • To embed intelligent classification and feature selection methods for patient stratification and biological insight.

Main Methods:

  • Development of a unified R/Bioc software package integrating various bioinformatics tools.
  • Implementation of advanced algorithms for gene selection in multiclass problems.
  • Integration of classification and feature selection methods for patient data analysis.
  • Automated generation of detailed HTML reports for analysis transparency and modularity.

Main Results:

  • KnowSeq successfully processed raw transcriptomic data from diverse sources and technologies.
  • Models built using differentially expressed genes achieved high classification rates in breast and lung cancer studies.
  • Extracted biological knowledge, including Gene Ontologies and Pathways, aided expert decision-making.

Conclusions:

  • KnowSeq offers a powerful, scalable, and user-friendly solution for complex gene expression analysis.
  • The software facilitates the identification of gene signatures and aids in disease classification and biological knowledge discovery.
  • KnowSeq is readily available through Bioconductor, GitHub, and Docker for widespread adoption.