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Updated: Dec 26, 2025

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

Interoperable RNA-Seq analysis in the cloud.

Alexander Lachmann¹, Daniel J B Clarke¹, Denis Torre¹

¹Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA; Library of Integrated Network-based Cellular Signatures, Data Coordination and Integration Center (BD2K-LINCS DCIC), USA; Knowledge Management Center for Illuminating the Druggable Genome (KMC-IDG), USA.

Biochimica Et Biophysica Acta. Gene Regulatory Mechanisms

|March 12, 2020

Summary

This summary is machine-generated.

This study compares RNA-Sequencing (RNA-Seq) quantification methods for cloud deployment. Pseudo-alignment tools like kallisto and Salmon offer cost-effective, accurate transcript quantification, while HISAT2 excels in speed among classical aligners.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA-Sequencing (RNA-Seq) is crucial for genome-wide transcript quantification.
Various aligners exist for mapping raw reads to gene and transcript counts.
Cost-efficient cloud-based RNA-Seq analysis is increasingly important.

Purpose of the Study:

To compare transcript quantification methods for cloud infrastructure.
To identify cost-effective and accurate RNA-Seq analysis services.
To enable near real-time integration of new RNA-Seq data with existing datasets.

Main Methods:

Developed cloud infrastructure with microservices for file transfer and alignment jobs.
Performed in-depth benchmarks of different transcript quantification methods.
Evaluated pseudo-alignment algorithms (kallisto, Salmon) and classical aligners (HISAT2).

Main Results:

Pseudo-alignment algorithms (kallisto, Salmon) provide high read quality estimation and efficient runtime.
HISAT2 demonstrates the fastest performance among classical aligners with good alignment quality.
Identified suitable methods for cost-effective, accurate, and cloud-based RNA-Seq analysis.

Conclusions:

kallisto and Salmon are recommended for cost-efficient and accurate cloud-based RNA-Seq quantification.
HISAT2 is a strong choice for rapid alignment in cloud environments.
The developed framework facilitates scalable and efficient RNA-Seq data analysis.