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Related Experiment Video

Updated: Jan 1, 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

A protocol to evaluate RNA sequencing normalization methods.

Zachary B Abrams¹, Travis S Johnson^2,3, Kun Huang^3,4

¹Department Biomedical Informatics, Ohio State University, 250 Lincoln Tower, 1800 Cannon Dr. Columbus, Columbus, OH, 43210, USA. Zachary.Abrams@osumc.edu.

BMC Bioinformatics

|December 22, 2019

Summary

This summary is machine-generated.

RNA sequencing normalization is crucial for accurate transcriptomic data analysis. Our study found that Transcripts Per Million (TPM) best preserves biological signals, outperforming other methods tested.

Keywords:

Biological variability Normalization RNASeq Standardization

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Published on: September 16, 2019

Area of Science:

Genomics and Transcriptomics
Bioinformatics and Computational Biology

Background:

RNA sequencing (RNASeq) enables transcriptome analysis but introduces experimental errors.
Normalization methods are essential for reducing non-biological variability in RNASeq data.
Standardized comparison of RNASeq normalization technique efficacy is lacking.

Purpose of the Study:

To develop and apply tests for evaluating RNASeq normalization methods.
To assess the performance of various normalization techniques on a large-scale dataset.
To establish a protocol for measuring non-biological variability post-normalization.

Main Methods:

Proposed a testing protocol to quantify non-biological variability in RNASeq data.
Applied the protocol to evaluate multiple RNASeq normalization methods.
Utilized a large-scale, standardized dataset for systematic evaluation.

Main Results:

Identified Transcripts Per Million (TPM) as the top-performing normalization method.
TPM demonstrated superior preservation of biological signal compared to other tested methods.
The study provides a framework for assessing normalization method validity.

Conclusions:

Normalization is vital for accurate interpretation of genomic and transcriptomic experiments.
Further optimization of RNASeq normalization methods is necessary.
The proposed schema facilitates systematic evaluation and improvement of RNASeq normalization techniques.