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Updated: Sep 21, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Python for gene expression.

Leonid Bystrykh¹

¹ERIBA, University Medical Center Groningen, University of Groningen, Groningen, 9713 AV, The Netherlands.

|July 11, 2022

Summary

This summary is machine-generated.

R dominates gene expression analysis due to its extensive development. However, Python shows promise for becoming competitive, especially in single-cell differential gene expression, with areas for future improvement.

Keywords:

R differential gene expression limma python single cell expression

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Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Genome biology has seen significant advancements in analytical and computational methods.
Differential gene expression analysis is a computationally intensive area, largely developed within the R programming language ecosystem.

Purpose of the Study:

To explain the reasons behind R's dominance in gene expression data analysis.
To discuss Python's potential to become competitive in this field.
To identify current limitations and areas for improvement in Python for gene expression analysis.

Main Methods:

Review of R's capabilities and ecosystem for gene expression analysis.
Assessment of Python's current applicability in differential gene expression, including single-cell applications.
Identification of gaps in Python's functionality and potential development pathways.

Main Results:

R's dominance is attributed to its established packages and community support for gene expression analysis.
Python is already applicable to single-cell differential gene expression.
Specific areas in Python require further development to match R's comprehensive offerings.

Conclusions:

While R currently leads in gene expression analysis, Python presents a growing opportunity.
Further development of Python's bioinformatics libraries can enhance its competitiveness.
Python's potential is particularly noted in the emerging field of single-cell differential gene expression analysis.