Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Using semantic search to find publicly available gene-expression datasets.

Bioinformatics (Oxford, England)·2026

Same author

Translating short-form Python exercises to other programming languages using diverse prompting strategies.

GigaScience·2025

Same author

Opportunities and considerations for using artificial intelligence in bioinformatics education.

Bioinformatics advances·2025

Same author

Using semantic search to find publicly available gene-expression datasets.

bioRxiv : the preprint server for biology·2025

Same author

Comparison of Predictive Factors of Flu Vaccine Uptake Pre- and Post-COVID-19 Using the NIS-Teen Survey.

Vaccines·2024

Same author

Comparison of B-Cell Lupus and Lymphoma Using a Novel Immune Imbalance Transcriptomics Algorithm Reveals Potential Therapeutic Targets.

Genes·2024

Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026

Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026

Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026

Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026

Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026

Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 10, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

A cloud-based workflow to quantify transcript-expression levels in public cancer compendia.

P J Tatlow¹, Stephen R Piccolo^1,2

¹Department of Biology, Brigham Young University, Provo, Utah, USA.

Scientific Reports

|December 17, 2016

Summary

This summary is machine-generated.

Cloud computing enables researchers to analyze massive sequencing datasets without large data transfers. This study processed 12,307 RNA-Sequencing samples affordably, highlighting the need for preprocessing optimization.

More Related Videos

Development of Compendium for Esophageal Squamous Cell Carcinoma

Development of Compendium for Esophageal Squamous Cell Carcinoma

Published on: April 12, 2024

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

Related Experiment Videos

Last Updated: Mar 10, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Development of Compendium for Esophageal Squamous Cell Carcinoma

Development of Compendium for Esophageal Squamous Cell Carcinoma

Published on: April 12, 2024

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Public sequencing data repositories are growing into petabytes, making local data transfer infeasible for researchers.
The National Cancer Institute is exploring cloud-computing environments for molecular data analysis to overcome data transfer limitations.
Cloud computing allows scientists to move analytical tools to the data and scale computational resources dynamically.

Purpose of the Study:

To evaluate the performance and cost-effectiveness of cloud-based configurations for processing large-scale RNA-Sequencing data.
To quantify transcript-expression levels for a substantial number of cancer cell line and genome atlas samples using cloud infrastructure.

Main Methods:

Quantified transcript-expression levels for 12,307 RNA-Sequencing samples from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas.
Utilized two distinct cloud-based configurations, employing preemptible virtual machines for cost-efficient processing.
Collected performance metrics during sample processing to track step durations and computational resource utilization.

Main Results:

Processed 12,307 RNA-Sequencing samples at a low cost of $0.09 per sample using preemptible virtual machines.
Detailed performance metrics were gathered, enabling tracking of processing step durations and computational resource usage.
While reference alignment and expression quantification are computationally efficient, preprocessing steps require further optimization.

Conclusions:

Cloud-based analysis of large sequencing datasets is feasible and cost-effective, enabling researchers to "bring tools to the data."
The study demonstrates a scalable and economical approach to analyzing transcript-expression levels from public compendia.
Optimization of preprocessing workflows remains crucial for maximizing efficiency in cloud-based genomic data analysis.