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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
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Updated: Mar 20, 2026

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

Targeted virus detection in next-generation sequencing data using an automated e-probe based approach.

Marike Visser¹, Johan T Burger², Hans J Maree¹

¹Agricultural Research Council, Infruitec-Nietvoorbij: Institute for Deciduous Fruit, Vines and Wine, Stellenbosch, South Africa; Department of Genetics, Stellenbosch University, Stellenbosch, South Africa.

|May 23, 2016

Summary

This summary is machine-generated.

A new user-friendly software, Truffle, enables efficient plant virus detection using next-generation sequencing (NGS) and e-probes. This bioinformatics tool offers comparable results to traditional methods with reduced computational demands.

Keywords:

Bioinformatics Grapevine Grapevine leafroll disease High-throughput sequencing Virus diagnostics

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Area of Science:

Plant Pathology
Bioinformatics
Genomics

Background:

Next-generation sequencing (NGS) is increasingly used for plant virus detection, generating large datasets that require robust bioinformatic analysis pipelines.
Existing pipelines may present challenges due to computational infrastructure or skill limitations, hindering widespread adoption.

Purpose of the Study:

To develop user-friendly software for targeted plant virus detection using e-probes.
To enable custom e-probe design and screening of preloaded probes against raw NGS data.
To provide a more accessible and resource-efficient alternative to existing virus detection methods.

Main Methods:

Development of a novel bioinformatic pipeline named Truffle.
Implementation of e-probe technology for targeted virus detection.
Comparison of Truffle's performance against de novo assembly-based virus detection methods using grapevine data.

Main Results:

Truffle demonstrated comparable virus detection results to de novo assembly methods.
The Truffle pipeline required significantly less time and computational resources.
The software supports both custom e-probe design and the use of preloaded probe sets.

Conclusions:

Truffle offers an efficient, user-friendly, and resource-conscious solution for plant virus detection via NGS.
The software facilitates tailored virus detection for specific user needs and datasets.
Truffle is readily available for designing and screening e-probes, including preloaded sets for grapevine viruses.