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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.

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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...

Genomics

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Related Experiment Video

Updated: Jun 10, 2026

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 practical, bioinformatic workflow system for large data sets generated by next-generation sequencing.

Cinzia Cantacessi¹, Aaron R Jex, Ross S Hall

¹Department of Veterinary Science, The University of Melbourne, 250 Princes Highway, Werribee, Victoria 3030, Australia.

Nucleic Acids Research

|August 5, 2010

Summary

This summary is machine-generated.

This study presents a user-friendly bioinformatics workflow for analyzing transcriptomics data from next-generation sequencing (NGS). The system aids researchers in identifying potential drug targets in pathogens and understanding gene expression in various organisms.

Related Experiment Videos

Last Updated: Jun 10, 2026

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

Area of Science:

Biomedical Science
Bioinformatics
Genomics

Background:

Transcriptomics is crucial for understanding cellular function, disease, and pathogen biology.
Next-generation sequencing (NGS) has expanded transcriptomics but requires specialized bioinformatics tools.
Existing bioinformatics tools can be challenging for researchers without extensive expertise.

Purpose of the Study:

To develop and evaluate a semi-automated bioinformatics workflow system for analyzing large-scale NGS transcriptomics data.
To provide a practical tool for researchers with limited bioinformatics expertise.
To demonstrate the utility of the workflow for identifying drug targets and analyzing transcriptomic differences.

Main Methods:

Construction of a semi-automated bioinformatics workflow system.
Utilized custom-written Perl, Python, and Unix shell scripts.
Applied the workflow to analyze transcriptomic data from the parasitic worm Oesophagostomum dentatum.

Main Results:

The workflow successfully analyzed and annotated large-scale NGS data.
Demonstrated utility in exploring transcriptomic differences between sexes and life stages of O. dentatum.
Enabled prediction and prioritization of essential molecules as potential drug targets (e.g., GTPases, kinases).

Conclusions:

The developed workflow system is a practical tool for transcriptomics data analysis and annotation.
It empowers researchers with limited bioinformatics expertise to analyze NGS data.
The system is adaptable for various applications and organisms, including pathogens of socio-economic importance.