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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.

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.
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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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Multi-species Conserved Sequences

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RNA-seq

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

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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

Lecture notes: 2010 and beyond, the decade of high-performance computing for the next-generation sequence analysis.

Mary Qu Yang¹, Jack Y Yang

¹Department of Health and Human Services, Oak Ridge, DOE, United States National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20852, USA. yangma@mail.NIH.gov

International Journal of Computational Biology and Drug Design

|January 22, 2010

Summary

Developing intelligent algorithms for next-generation sequencing (NGS) analysis is crucial for handling complex genomic data. This study focuses on high-performance genetic algorithms utilizing multi-core technology for efficient bioinformatics tasks.

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Area of Science:

Bioinformatics and Computational Biology
Genomics and Genetics
Algorithm Development

Background:

Next-generation sequencing (NGS) generates vast, complex datasets (metagenome, epigenome, disease genome, immunogenome, transcriptome).
Analyzing NGS data is critical for understanding biological systems and diseases but presents significant computational challenges due to data scale and noise.
Existing bioinformatics algorithms struggle with the efficiency and accuracy required for comprehensive NGS data analysis.

Purpose of the Study:

To address the challenges in next-generation sequence analysis.
To develop high-performance algorithms for handling diverse NGS data types.
To leverage multi-core technology for enhanced computational efficiency in bioinformatics.

Main Methods:

Development of high-performance genetic algorithms.
Implementation of multi-core processing for parallel computation.
Adaptation of algorithms for emerging high-throughput sequencing platforms.

Main Results:

Demonstrated the potential of genetic algorithms for efficient NGS data analysis.
Showcased the benefits of multi-core technology in accelerating bioinformatics computations.
Established a framework for adaptable and scalable NGS analysis tools.

Conclusions:

High-performance genetic algorithms are effective for next-generation sequence analysis.
Multi-core technology significantly improves the speed and efficiency of handling large genomic datasets.
The developed approach provides a robust foundation for future advancements in computational genomics.