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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....

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Related Experiment Video

Updated: Oct 23, 2025

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Published on: November 30, 2014

A graphical, interactive and GPU-enabled workflow to process long-read sequencing data.

Shishir Reddy¹, Ling-Hong Hung², Olga Sala-Torra³

¹University of California, 92697, Irvine, CA, USA.

|August 24, 2021

Summary

This summary is machine-generated.

This study introduces user-friendly, GPU-accelerated bioinformatics software for Nanopore sequencing, significantly reducing costs and analysis time for cancer diagnostics. The tools enable faster, more accessible long-read sequencing analysis for clinical applications.

Keywords:

Cancer diagnostics Cloud computing FAIR GPU Leukemia Long-read sequencing Nanopore Workflows

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Published on: April 11, 2016

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Last Updated: Oct 23, 2025

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Long-read sequencing offers potential for portable, rapid cancer diagnostics.
Current bioinformatics tools lack graphical interfaces and are difficult to configure, especially with GPU acceleration on the cloud.
This limits the widespread adoption of advanced sequencing technologies in clinical settings.

Purpose of the Study:

To develop a graphical, cloud-enabled workflow for efficient analysis of Nanopore sequencing data.
To overcome challenges associated with GPU configuration and software accessibility for long-read sequencing analysis.
To demonstrate the utility of the developed tools in accelerating cancer diagnostics.

Main Methods:

Developed a containerized, graphical workflow for Nanopore data analysis utilizing GPUs.
Provided a pre-configured Amazon Machine Image (AMI) for simplified cloud-based GPU computing.
Applied the workflow to analyze Nanopore sequencing data from blood cancer cell lines using the Flongle adapter.

Main Results:

Achieved a 29x speedup and 93x cost reduction in the basecalling step.
Demonstrated the potential to significantly reduce turnaround time for cancer diagnostics.
Ensured reproducibility and ease of installation through containerization.

Conclusions:

The developed interactive and efficient software tools enhance accessibility of GPU and cloud computing for Nanopore data analysis.
Facilitates the adoption of cost-effective, fast, portable, and real-time long-read sequencing in biomedical and clinical research.
Empowers scientists to leverage advanced sequencing technologies for improved diagnostic capabilities.