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Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Jun 24, 2025

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

A Multi-Omics Extraction Method for the In-Depth Analysis of Synchronized Cultures of the Green Alga Chlamydomonas reinhardtii

Published on: August 8, 2019

The tidyomics ecosystem: Enhancing omic data analyses.

William J Hutchison^1,2, Timothy J Keyes^3,4,

¹Walter and Eliza Hall Institute of Medical Research, Division of Bioinformatics, Parkville, VIC 3052, Australia.

Biorxiv : the Preprint Server for Biology

|June 3, 2024

Summary

This summary is machine-generated.

The tidyomics software ecosystem simplifies omic data analysis by integrating Bioconductor with tidy R programming. This facilitates easier data manipulation, analysis, and cross-disciplinary collaboration for biological research.

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Last Updated: Jun 24, 2025

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Published on: May 9, 2017

Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

The increasing volume of omic data poses challenges in data handling and analysis.
Bioconductor is a community-driven platform for biological data analysis.
Tidy R programming provides a standardized approach to data organization and manipulation.

Purpose of the Study:

To introduce the tidyomics software ecosystem, which bridges Bioconductor and the tidy R paradigm.
To streamline omic data analysis workflows.
To enhance learning and foster cross-disciplinary collaboration in omics research.

Main Methods:

Development of the tidyomics software ecosystem.
Integration of Bioconductor packages within a tidy R framework.
Application of tidyomics to a large-scale dataset from the Human Cell Atlas.

Main Results:

Demonstration of tidyomics' effectiveness in analyzing a large omic dataset.
Successful integration of six data frameworks and ten analysis tools.
Streamlined omic data analysis and visualization.

Conclusions:

The tidyomics ecosystem offers a powerful and user-friendly solution for omic data analysis.
It simplifies complex analyses and promotes reproducible research.
Encourages broader adoption of advanced analytical methods in biological sciences.