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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....

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.
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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...

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Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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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.

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Related Experiment Video

Updated: Mar 19, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Recommendations on e-infrastructures for next-generation sequencing.

Ola Spjuth¹, Erik Bongcam-Rudloff², Johan Dahlberg³

¹Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, P.O. Box 591, SE-75124, Sweden. ola.spjuth@farmbio.uu.se.

|June 9, 2016

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) data growth necessitates robust e-infrastructures. Recommendations focus on flexible, scalable computational resources and professional management for future data analysis demands.

Keywords:

Cloud computing E-infrastructure High-performance computing Next-generation sequencing

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) generates vast amounts of data, increasing demands on computational infrastructures.
The EU COST Action SeqAhead initiative gathered collective experiences to address these growing needs.

Purpose of the Study:

To provide recommendations for e-infrastructure development and maintenance supporting NGS data analysis.
To address computational, storage, network, software, and automation requirements for NGS data.

Main Methods:

Consensus-based recommendations derived from collective experiences of EU COST Action SeqAhead participants.
Analysis of demands across data preprocessing, upstream/downstream processing, delivery, storage, and archiving.

Main Results:

Identified critical needs for computational power, storage, robust networks, and flexible software stacks.
Highlighted the importance of analysis automation, education, and emerging trends in NGS data handling.

Conclusions:

E-infrastructures for NGS require significant, ongoing effort and investment for setup and maintenance.
Prioritizing processing capacity and e-infrastructure flexibility is crucial for future data analysis demands.
Professional service units and collaboration between researchers and IT professionals are recommended for e-infrastructure development and maintenance.