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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: Jun 11, 2025

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems.

Rhalena A Thomas^1,2, Michael R Fiorini³, Saeid Amiri⁴

¹Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, QC, H3A 2B4, Canada. rhalena.thomas@mcgill.ca.

BMC Bioinformatics

|October 1, 2024

Summary

This summary is machine-generated.

scRNAbox is a new pipeline for analyzing single-cell RNA sequencing (scRNAseq) data on high-performance computing (HPC) systems. It offers an accessible, end-to-end solution for complex scRNAseq analysis without requiring extensive coding knowledge.

Keywords:

High performance computing systems Pipeline ScRNAseq Single-cell RNA sequencing

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Last Updated: Jun 11, 2025

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

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Published on: November 30, 2014

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNAseq) generates large datasets requiring significant computational resources.
Existing scRNAseq analysis tools often lack scalability or demand advanced programming skills.
High-performance computing (HPC) is essential for modern scRNAseq data analysis.

Purpose of the Study:

To develop a user-friendly, scalable pipeline for scRNAseq data analysis on HPC systems.
To address the computational demands and accessibility challenges in scRNAseq research.
To provide an integrated solution for end-to-end scRNAseq data processing.

Main Methods:

Developed scRNAbox, an end-to-end scRNAseq analysis pipeline for HPC environments.
Utilized the SLURM workload manager for efficient job execution.
Incorporated modules for quality control, sample integration, clustering, annotation, and differential gene expression analysis.

Main Results:

scRNAbox efficiently processes raw scRNAseq data from standard and Hashtag samples.
The pipeline includes comprehensive tools for data quality control and sample integration.
Demonstrated scRNAbox's utility by analyzing two publicly available scRNAseq datasets.

Conclusions:

scRNAbox provides a comprehensive and accessible solution for scRNAseq data analysis on HPC.
The pipeline simplifies complex analyses, bridging the gap between data generation and biological insight.
Facilitates advanced scRNAseq analysis for researchers with varying levels of coding expertise.