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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: Dec 31, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Cloud accelerated alignment and assembly of full-length single-cell RNA-seq data using Falco.

Andrian Yang^1,2, Abhinav Kishore¹, Benjamin Phipps¹

¹Victor Chang Cardiac Research Institute, 405 Liverpool St, Darlinghurst, 2010, New South Wales, Australia.

|January 1, 2020

Summary

Falco accelerates RNA-seq analysis, enabling scalable cloud-based read alignment and transcript assembly for full-length single-cell RNA-seq (scRNA-seq) data. This open-source framework significantly enhances computational efficiency for transcript isoform discovery.

Keywords:

Alignment Cloud computing Falco Single-cell RNA-seq Transcript assembly

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Last Updated: Dec 31, 2025

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNA-seq) analysis is crucial for transcript isoform discovery, involving read alignment and transcript assembly.
Current tools lack scalability for large-scale full-length bulk and single-cell RNA-seq (scRNA-seq) data analysis.
The previous Falco version focused solely on RNA-seq read counting, omitting alignment and assembly functionalities.

Purpose of the Study:

To enhance the Falco framework for scalable and accelerated RNA-seq data processing.
To introduce new modes for read alignment and transcript assembly within Falco.
To leverage cloud computing for efficient analysis of full-length RNA-seq data.

Main Methods:

Developed two new modes in Falco: alignment-only and transcript assembly.
Utilized parallel and distributed cloud computing environments.
Evaluated performance on public scRNA-seq datasets against standalone and cloud-enabled tools.

Main Results:

Falco achieved 2.5-16.4x speed-up for read alignment compared to optimized standalone computing.
Falco demonstrated a 10x average speed-up for read alignment versus the Rail-RNA tool.
Transcript assembly in Falco showed a 1.7-16.5x speed-up compared to optimized standalone computing.

Conclusions:

Falco is an updated, open-source framework for scalable cloud-based alignment and assembly of full-length scRNA-seq data.
The enhanced Falco significantly accelerates core RNA-seq analysis steps.
Source code is available for broader research community adoption.