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

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 microarray-based...

Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

RACE - Rapid Amplification of cDNA Ends

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since the...

Sanger Sequencing

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

You might also read

Related Articles

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

Sort by

Same author

Bridging the Gap: Recommendations for Sustainable Research Partnerships Between Community Health and Academic Health Centers.

Journal of health care for the poor and underserved·2026

Same author

More proximal lumbar source of type II endoleak is associated with worse outcomes after infrarenal endovascular aortic repair.

Journal of vascular surgery·2026

Same author

The integrated to developmental stress response (ISR-DASR) shift reveals a functional transition from cellular to stem cell organismal adaptation.

Scientific reports·2026

Same author

Autonomous Wheelchairs Deployment in Healthcare Facilities: Requirements and Challenges.

Delaware journal of public health·2026

Same author

From the Guest Editors: Artificial Intelligence and Big Data in the Health Sciences.

Delaware journal of public health·2026

Same author

Harnessing AI for Transformative Healthcare: Proceedings and Strategic Roadmap from AI4Health Industry Day 2026 in Delaware.

Delaware journal of public health·2026

Search research articles

Related Experiment Video

Updated: Jun 1, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

CloudAligner: A fast and full-featured MapReduce based tool for sequence mapping.

Tung Nguyen¹, Weisong Shi, Douglas Ruden

¹Computer Science Department, Wayne State University, US. nttung@wayne.edu.

BMC Research Notes

|June 8, 2011

Summary

This summary is machine-generated.

CloudAligner, a new Hadoop MapReduce application, enhances next-generation sequencing (NGS) data analysis by offering higher performance and accuracy for genetic research. This cloud-based tool is user-friendly and handles long DNA sequences efficiently.

More Related Videos

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

Related Experiment Videos

Last Updated: Jun 1, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

Area of Science:

Genomics and Bioinformatics
Computational Biology
Next-Generation Sequencing (NGS) Data Analysis

Background:

Next-generation sequencing (NGS) generates massive datasets, posing storage, compatibility, scalability, and performance challenges.
Cloud Computing and MapReduce offer a promising solution for efficient genome sequence mapping.
Existing MapReduce applications struggle with long reads and lack user-friendly interfaces for biologists.

Purpose of the Study:

To develop a cloud-based application for efficient NGS data analysis.
To address limitations of existing MapReduce tools in handling long reads and primary functions.
To provide a user-friendly, high-performance solution for genomic data processing.

Main Methods:

Developed a Hadoop MapReduce-based application named CloudAligner.
Designed CloudAligner to process long DNA sequences efficiently.
Implemented features for bisulfite and pair-end mapping, and optimized for parallel processing.

Main Results:

CloudAligner demonstrates 35-80% performance gain over cloud-based counterparts by omitting the reduce phase.
Achieved higher accuracy compared to local-based approaches like RMAP.
CloudAligner successfully processes long sequences and supports various input/output formats.

Conclusions:

CloudAligner is faster and more accurate than existing tools like CloudBurst and RMAP.
The web-based interface makes CloudAligner more accessible to biologists without programming skills.
CloudAligner effectively addresses the challenges of NGS data analysis in the cloud.