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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Mar 22, 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 benchmark for RNA-seq quantification pipelines.

Mingxiang Teng^1,2,3, Michael I Love^1,2, Carrie A Davis⁴

¹Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.

|April 25, 2016

Summary

This summary is machine-generated.

Evaluating RNA-sequencing (RNA-seq) analysis pipelines is challenging due to a lack of sensitive metrics. Our study found that RSEM slightly outperformed other methods, though overall performance was generally poor.

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Last Updated: Mar 22, 2026

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Published on: November 7, 2025

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Area of Science:

Bioinformatics
Computational Biology
Statistical Genetics

Background:

RNA-sequencing (RNA-seq) is a powerful tool for gene expression analysis.
Numerous computational pipelines exist for RNA-seq data analysis, leading to debate over the optimal method.
Current evaluation of these pipelines is hindered by a lack of sensitive performance metrics.

Purpose of the Study:

To develop and present statistical summaries and plots for evaluating RNA-seq pipeline performance.
To assess the sensitivity and specificity of competing RNA-seq analysis methods.

Main Methods:

Developed a series of statistical summaries and plots for performance evaluation.
Created an R/Bioconductor package (rnaseqcomp) to implement these evaluation metrics.
Assessed seven competing RNA-seq analysis pipelines using two independent datasets.

Main Results:

Overall performance of the assessed RNA-seq pipelines was generally poor.
Two pipelines significantly underperformed compared to others.
The RSEM pipeline demonstrated slightly superior performance over the remaining methods.

Conclusions:

Sensitive assessment metrics are crucial for evaluating RNA-seq analysis pipelines.
The developed R/Bioconductor package provides a valuable tool for performance assessment.
Further development of robust RNA-seq analysis methods is warranted.