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Related Experiment Video

Updated: Feb 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

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data.

Joseph N Paulson^1,2,3, Cho-Yi Chen^1,2, Camila M Lopes-Ramos^1,2

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

BMC Bioinformatics

|October 5, 2017

Summary

This summary is machine-generated.

Analyzing large RNA-Sequencing datasets requires robust methods. We developed the Yet Another RNA Normalization (YARN) pipeline to address challenges in processing complex, multi-center gene expression data.

Keywords:

Filtering GTEx Normalization Preprocessing Quality control RNA-Seq

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Last Updated: Feb 21, 2026

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

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Published on: June 24, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Ultrahigh-throughput RNA-Sequencing (RNA-Seq) is standard for genome-wide transcriptional profiling.
Current analytical pipelines are optimized for smaller studies (tens of samples).
Large-scale projects generate complex datasets with hundreds or thousands of samples, introducing batch and tissue effects.

Purpose of the Study:

To address analytical challenges in large, heterogeneous RNA-Seq datasets.
To develop and demonstrate a new software pipeline for preprocessing, normalization, and filtering.
To facilitate downstream analysis of complex gene expression data.

Main Methods:

Developed Yet Another RNA Normalization (YARN) software pipeline.
Incorporated quality control, preprocessing, gene filtering, and normalization steps.
Utilized data from the Genotype-Tissue Expression (GTEx) project for demonstration.

Main Results:

Analysis of large RNA-Seq datasets necessitates careful quality control.
Accounting for sparsity due to heterogeneity is crucial in multi-group studies.
The YARN pipeline effectively facilitates downstream analysis of large, heterogeneous RNA-Seq data.

Conclusions:

The YARN pipeline provides essential tools for large-scale RNA-Seq data analysis.
The developed methods address challenges posed by batch and tissue effects.
An R package for YARN is publicly available for researchers.