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Related Experiment Video

Updated: Jan 11, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

From bench to bytes: a practical guide to RNA sequencing data analysis.

Prabin Dawadi¹, Bivek Pokharel¹, Anita Shrestha¹

¹Department of Biology, University of Mississippi, Oxford, MS, United States.

Frontiers in Genetics

|November 12, 2025

Summary

RNA sequencing (RNA-Seq) analysis is crucial for molecular biologists but requires computational skills. This guide helps beginners choose the right tools and statistical methods for confident and rigorous RNA-Seq data analysis.

Keywords:

Beginner’s guide DESeq2 RNA-Seq bioinformatics gene expression analysis

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Last Updated: Jan 11, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

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Published on: November 7, 2025

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Published on: September 16, 2019

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

RNA sequencing (RNA-Seq) is a standard high-throughput method for transcriptome quantification.
Molecular biology researchers increasingly need to analyze RNA-Seq data.
RNA-Seq analysis requires computational and statistical expertise, posing challenges for beginners.

Purpose of the Study:

To provide a decision-oriented guide for molecular biologists new to RNA-Seq analysis.
To help researchers select appropriate tools and statistical approaches based on their specific data, goals, and constraints.
To empower beginners with the knowledge for rigorous and confident RNA-Seq data analysis.

Main Methods:

Review of existing RNA-Seq analysis manuals and resources.
Identification of common challenges and knowledge gaps for beginners.
Development of a structured decision-making framework for tool and method selection.

Main Results:

Existing resources are often fragmented, specialized, or superficial.
A clear need exists for a guide that explains underlying principles.
The proposed guide facilitates informed choices in RNA-Seq analysis.

Conclusions:

Beginner-friendly guidance is essential for effective RNA-Seq data analysis.
Understanding principles alongside tool selection enhances analytical rigor.
This review aims to bridge the gap between experimental work and computational analysis in molecular biology.