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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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Updated: Mar 25, 2026

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

Single-cell Transcriptome Study as Big Data.

Pingjian Yu¹, Wei Lin¹

¹Genomics and Bioinformatics Lab, Baylor Institute for Immunology Research, Dallas, TX 75204, USA.

Genomics, Proteomics & Bioinformatics

|February 16, 2016

Summary

This summary is machine-generated.

Big-data technology offers solutions for storing, processing, and analyzing single-cell RNA sequencing (scRNA-seq) data. This article reviews strategies and proposes a workflow for handling complex scRNA-seq datasets.

Keywords:

Big data RNA-seq Signal normalization Single cell Transcriptional heterogeneity

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Last Updated: Mar 25, 2026

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

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

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

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

The proliferation of single-cell RNA sequencing (scRNA-seq) necessitates advanced data management solutions.
Inter-institutional scRNA-seq datasets present significant storage, processing, and analysis challenges.
Existing big-data frameworks require adaptation for the unique characteristics of single-cell transcriptomic data.

Purpose of the Study:

To explore the application of big-data technologies for scRNA-seq data analysis.
To discuss strategies for addressing signal stochasticity and heterogeneity in single-cell transcriptomes.
To propose an optimized workflow for large-scale scRNA-seq data integration and analysis.

Main Methods:

Comprehensive review of big-data applications in next-generation sequencing (NGS) studies.
Analysis of challenges specific to single-cell RNA sequencing data.
Development of a tailored workflow for scRNA-seq data processing and analysis.

Main Results:

Identification of key big-data strategies applicable to scRNA-seq.
Elucidation of methods to manage stochastic and heterogeneous single-cell signals.
Proposal of a workflow designed for the specific demands of single-cell studies.

Conclusions:

Big-data technologies are crucial for unlocking biological insights from large-scale scRNA-seq datasets.
A specialized workflow is essential for efficient and effective analysis of complex single-cell transcriptomic data.
The proposed framework facilitates comprehensive discovery from inter-institutional scRNA-seq data.