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Updated: May 10, 2025
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
Published on: September 25, 2021
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Learning and teaching biological data science in the Bioconductor community.
Jenny Drnevich1, Frederick J Tan2, Fabricio Almeida-Silva3,4
1Roy J. Carver Biotechnology Center, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America.
Plos Computational Biology
|April 22, 2025
Summary
This guide overviews Bioconductor project resources for biological data science training. It offers best practices for omics data analysis, aiding learners and educators in this data-intensive field.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Biological research generates vast amounts of data.
- Effective training in biological data science is crucial.
- The Bioconductor project offers open-source tools for omics data analysis.
Purpose of the Study:
- To provide an overview of Bioconductor resources.
- To highlight best practices for omics data analysis using Bioconductor.
- To serve as a reference for biological data science education.
Main Methods:
- Review of Bioconductor project documentation.
- Identification of key resources for omics data analysis.
- Compilation of best practices for using Bioconductor.
Main Results:
- Bioconductor offers a comprehensive ecosystem of software and resources.
- Specific tools and workflows facilitate various omics data analyses.
- Best practices enhance reproducibility and efficiency in biological data science.
Conclusions:
- Bioconductor is a vital resource for biological data science training.
- Educators and learners can leverage Bioconductor for omics data analysis.
- Adoption of Bioconductor best practices improves research outcomes.
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