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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 microarray-based...

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...

Maxam-Gilbert Sequencing

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.
Challenges of the Maxam-Gilbert Method
The...

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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

Base-calling for next-generation sequencing platforms.

Christian Ledergerber¹, Christophe Dessimoz

¹ETH Zurich, Switzerland.

Briefings in Bioinformatics

|January 20, 2011

Summary

This summary is machine-generated.

Next-generation DNA sequencing relies on base-calling to interpret light signals. This review covers recent advancements in base-calling methods for Illumina and Roche 454 platforms to improve sequence data quality.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Next-generation sequencing (NGS) technologies significantly reduce DNA sequencing costs.
Base-calling, the inference of DNA bases from light intensity signals, is crucial for NGS data generation.
Improving the quality of sequence data is a key area of interest in genomics.

Purpose of the Study:

To review recent developments in base-calling approaches for major sequencing platforms.
To highlight methodological improvements for accurate DNA sequencing.
To provide an overview of base-calling advancements for Illumina and Roche 454 technologies.

Main Methods:

Literature review of recent publications on base-calling algorithms.
Analysis of base-calling biases and their impact on sequence data quality.
Summarization of improvements for Illumina and Roche 454 sequencing platforms.

Main Results:

Characterization of biases inherent in current base-calling methods.
Identification of novel strategies to enhance base-calling accuracy.
Overview of the latest base-calling developments for widely used sequencing platforms.

Conclusions:

Advancements in base-calling are critical for maximizing the utility of NGS data.
Understanding and mitigating base-calling errors are essential for reliable genomic research.
This review consolidates current knowledge on base-calling improvements for Illumina and Roche 454.