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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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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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.

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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Related Experiment Video

Updated: Apr 13, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

Alignment of Next-Generation Sequencing Reads.

Knut Reinert¹, Ben Langmead, David Weese

¹Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany; email: knut.reinert@fu-berlin.de , david.weese@fu-berlin.de.

Annual Review of Genomics and Human Genetics

|May 5, 2015

Summary

High-throughput sequencing generates billions of DNA fragments. This review covers fragment generation, applications, and algorithmic solutions for the critical read alignment problem in genomics.

Keywords:

high-throughput sequencing read mapping string indices

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Last Updated: Apr 13, 2026

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Published on: February 24, 2015

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-throughput sequencing technologies enable massive parallel measurement of DNA or RNA fragments.
Accurate mapping of these short fragments to a reference genome is a fundamental step in many genomic analyses.

Purpose of the Study:

To review techniques for DNA/RNA fragment generation in high-throughput sequencing.
To discuss applications of short-read sequencing in biomedical research.
To provide an overview of algorithmic approaches for the read alignment problem.

Main Methods:

Review of established and emerging DNA/RNA sequencing methodologies.
Analysis of computational algorithms designed for sequence read mapping.
Discussion of common challenges and potential solutions in read alignment.

Main Results:

Comprehensive overview of fragment generation techniques.
Categorization of key applications utilizing high-throughput sequencing data.
Detailed examination of algorithmic strategies for read alignment.

Conclusions:

Read alignment is a crucial computational challenge in genomics.
Understanding fragment generation and alignment algorithms is essential for effective biomedical research.
Addressing difficulties in read placement is key to accurate genomic interpretation.