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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: Feb 23, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Bio-Docklets: virtualization containers for single-step execution of NGS pipelines.

Baekdoo Kim¹, Thahmina Ali¹, Carlos Lijeron¹

¹Center for Translational and Basic Research and Belfer Research Building, Hunter College of The City University of New York, 413 E 69th St, New York, NY 10021.

|September 1, 2017

Summary

Bio-Docklets simplify complex next-generation sequencing (NGS) data analysis by packaging bioinformatics pipelines into easy-to-use Docker containers. This enables non-experts to run sophisticated analyses, like RNA sequencing, on any platform.

Keywords:

CHIPseq NGS RNAseq bioinformatics docker

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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:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) data analysis requires specialized bioinformatics skills and complex software deployment.
Virtualization containers offer a solution for simplifying software and pipeline deployment across diverse computational environments.

Purpose of the Study:

To abstract complex, multistep bioinformatics pipelines for NGS data analysis.
To provide a user-friendly interface for running NGS data analysis pipelines, accessible to non-bioinformatics experts.

Main Methods:

Developed Docker containers, termed Bio-Docklets, to encapsulate preconfigured bioinformatics pipelines.
Utilized a meta-script, BioBlend, and Galaxy API to automate Bio-Docklet execution and pipeline control.
Integrated with Visual Omics Explorer for interactive, web-based data visualization of pipeline outputs.

Main Results:

Successfully deployed two Bio-Docklets for RNA sequencing and chromatin immunoprecipitation sequencing (ChIP-seq) analysis.
Achieved a simplified user experience where pipelines run as single tools with defined input/output endpoints.
Enabled programmatic deployment for concurrent analysis of multiple datasets by developers.

Conclusions:

Bio-Docklets significantly lower the barrier to entry for NGS data analysis, empowering researchers without extensive bioinformatics expertise.
The approach ensures seamless pipeline execution across various computing platforms, including workstations, clusters, and cloud services.
Facilitates reproducible and scalable genomic data analysis for both end-users and developers.