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RNA-seq

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Related Experiment Video

Updated: Nov 3, 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

pyrpipe: a Python package for RNA-Seq workflows.

Urminder Singh¹, Jing Li², Arun Seetharam³

¹Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50014, USA.

NAR Genomics and Bioinformatics

|June 4, 2021

Summary

This summary is machine-generated.

A new Python package, pyrpipe, offers a flexible framework for fast, reproducible RNA-Seq analysis. This tool simplifies complex bioinformatics workflows, enabling efficient data interpretation and sharing of results.

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Last Updated: Nov 3, 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

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

The increasing volume of RNA-Seq data necessitates efficient and reproducible analysis methods.
Existing tools often lack flexibility and ease of integration for complex pipelines.
A unified framework is required to manage and analyze large-scale transcriptomic datasets.

Purpose of the Study:

To develop a Python package, pyrpipe, for creating flexible, reproducible, and manageable RNA-Seq analysis pipelines.
To provide high-level APIs for popular RNA-Seq tools within a Python environment.
To facilitate the integration of custom code and third-party programs for enhanced pipeline customization.

Main Methods:

Development of pyrpipe as an object-oriented Python package.
Implementation of high-level APIs for accessing RNA-Seq analysis tools.
Support for pipeline customization, checkpointing, and integration with workflow management systems.
Generation of detailed analysis and benchmark reports.

Main Results:

pyrpipe enables the straightforward development of complex, reproducible RNA-Seq pipelines.
The package integrates popular RNA-Seq tools and allows for custom code integration.
Case studies demonstrate pyrpipe's functionality with diverse RNA-Seq datasets (GTEx, SARS-CoV-2, Zea mays).
pyrpipe supports execution across multiple computing environments with efficient resource management.

Conclusions:

pyrpipe provides a robust and flexible solution for RNA-Seq data analysis.
The package enhances reproducibility and simplifies debugging of bioinformatics workflows.
pyrpipe is freely available with comprehensive documentation, promoting its adoption in research.