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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 8, 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

aRNApipe: a balanced, efficient and distributed pipeline for processing RNA-seq data in high-performance computing

Arnald Alonso^1,2, Brittany N Lasseigne¹, Kelly Williams¹

¹HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.

Bioinformatics (Oxford, England)

|January 22, 2017

Summary

This summary is machine-generated.

aRNApipe is a modular pipeline for RNA sequencing (RNA-seq) data analysis on high-performance clusters. It streamlines essential analyses, optimizes computational resources, and offers dynamic project management for efficient research.

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Last Updated: Mar 8, 2026

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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

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Published on: June 8, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNA-seq) generates large datasets requiring significant computational resources.
Existing RNA-seq analysis workflows can be complex and difficult to manage.
Optimizing computational resource utilization is crucial for efficient RNA-seq data processing.

Purpose of the Study:

To develop a project-oriented pipeline for RNA-seq data processing in high-performance computing (HPC) environments.
To create a modular and adaptable pipeline that can be easily migrated across different HPC systems.
To provide a centralized and user-friendly system for managing RNA-seq analyses and computational resources.

Main Methods:

Developed aRNApipe, a modular pipeline for RNA-seq data analysis.
Integrated essential primary RNA-seq analyses including quality control, alignment, and variant calling.
Implemented a project-oriented design with dynamic sample and module management.
Utilized a single configuration file for workflow parameter management and tracking.
Incorporated interactive web reports for sample tracking and genome assembly management.

Main Results:

aRNApipe successfully orchestrates RNA-seq workflows in HPC environments.
The pipeline supports essential analyses such as quality control, transcript alignment, count generation, fusion identification, alternative splicing, and variant calling.
aRNApipe offers modularity, allowing easy migration and customization for different HPC systems.
Centralized configuration and interactive reports enhance usability and tracking of analytical processes.

Conclusions:

aRNApipe provides an efficient and adaptable solution for RNA-seq data analysis in HPC environments.
The pipeline optimizes computational resource usage and streamlines complex workflows.
Its modular design and user-friendly features facilitate dynamic project management and data analysis.