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Related Concept Videos

Next-generation Sequencing03:00

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.

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Related Experiment Video

Updated: Jun 6, 2026

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
06:40

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Initial steps towards a production platform for DNA sequence analysis on the grid.

Angela C M Luyf1, Barbera D C van Schaik, Michel de Vries

  • 1Bioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, 1100 DE Amsterdam, The Netherlands.

BMC Bioinformatics
|December 16, 2010
PubMed
Summary

High-throughput DNA sequencing generates massive data. A new grid-based platform simplifies data analysis and collaboration for bioinformatics, reducing analysis time and improving accessibility for next-generation sequencing projects.

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Published on: January 27, 2016

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput DNA sequencers generate large datasets, overwhelming local servers.
  • Grid and workflow technologies offer solutions for efficient data handling and collaboration.
  • Existing grid interfaces can be challenging for novice users.

Purpose of the Study:

  • To develop a user-friendly grid-based platform for bioinformatics data analysis.
  • To address challenges in data storage, analysis, and collaboration for next-generation sequencing.

Main Methods:

  • Reused an existing platform developed for medical image analysis.
  • Developed workflows for sequence alignment tools (BLAST, BLAT) as a proof of concept.
  • Integrated data transfer, workflow execution, and job monitoring into a single graphical interface.

Main Results:

  • Significantly reduced analysis time for sequence alignment tasks.
  • Demonstrated a practical, powerful, and scalable solution for managing large sequencing datasets.
  • Made workflows and executables available to specific virtual organizations.

Conclusions:

  • In-house expertise and accessible tools enhance grid resource utilization for new users.
  • The developed platform effectively addresses capacity and collaboration issues in next-generation sequencing.
  • The methodology is currently used daily for DNA sequencing and other applications.