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

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A systematic evaluation of single cell RNA-seq analysis pipelines.

Beate Vieth¹, Swati Parekh², Christoph Ziegenhain³

¹Anthropology and Human Genomics, Department of Biology II, Ludwig-Maximilians University, Munich, Germany.

Nature Communications

|October 13, 2019

Summary

Choosing the right single-cell RNA sequencing (scRNA-seq) pipeline is crucial. Our simulations show library preparation and normalization significantly impact differential expression analysis, comparable to quadrupling sample size.

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Last Updated: Jan 5, 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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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
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) methods are rapidly advancing, leading to diverse experimental and computational pipelines.
Lack of established best practices hinders optimal analysis of scRNA-seq data.

Purpose of the Study:

To systematically evaluate the impact of different computational pipeline components on scRNA-seq data analysis.
To identify key factors influencing differential gene expression detection in scRNA-seq studies.

Main Methods:

Simulated scRNA-seq data based on five library preparation protocols and nine differential expression scenarios.
Comprehensive evaluation of approximately 3000 pipelines, assessing mapping, imputation, normalization, and differential expression testing methods.
Analysis of interactions between different pipeline steps.

Main Results:

Normalization and library preparation protocols have the most significant impact on scRNA-seq analysis outcomes.
Library preparation influences the detection of symmetric expression differences.
Normalization methods are critical for asymmetric differential expression setups.

Conclusions:

Informed choices in scRNA-seq pipeline construction are essential for robust biological signal detection.
Optimizing pipeline components can dramatically improve analysis sensitivity, potentially matching the effect of increased sample size.