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RNA-seq

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Related Experiment Video

Updated: May 21, 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

RSeQC: quality control of RNA-seq experiments.

Liguo Wang¹, Shengqin Wang, Wei Li

¹Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA. WL1@bcm.edu

Bioinformatics (Oxford, England)

|June 30, 2012

Summary

This summary is machine-generated.

RSeQC is a new package that provides a comprehensive and convenient tool for assessing RNA-sequencing (RNA-seq) data quality. It addresses the need for efficient quality control of large RNA-seq datasets.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA-sequencing (RNA-seq) is a powerful technique for transcriptome analysis.
Ensuring RNA-seq data quality through rigorous quality control (QC) is essential for reliable downstream analysis.
Current QC methods are often time-consuming and complex due to the large data volumes and diverse nature of RNA-seq data.

Purpose of the Study:

To develop a convenient and comprehensive tool for RNA-seq data quality control.
To provide efficient assessment of various RNA-seq experiment aspects.

Main Methods:

Development of the RSeQC package using Python and C.
Input formats include SAM, BAM, and BED files.
Utilizes R scripts for data visualization and efficient processing of large alignment files.

Main Results:

RSeQC evaluates multiple aspects of RNA-seq data, including sequence quality, GC bias, PCR bias, nucleotide composition, sequencing depth, strand specificity, coverage uniformity, and read distribution.
The package is efficient in handling large BAM/SAM files.

Conclusions:

RSeQC offers a valuable solution for comprehensive and efficient RNA-seq data quality assessment.
The tool aids researchers in ensuring the suitability of RNA-seq data for subsequent analyses.