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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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Related Experiment Video

Updated: Apr 3, 2026

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

Faster single-end alignment generation utilizing multi-thread for BWA.

Heeseung Jo¹, Gunhwan Koh²

¹Department of Information Technology, Chonbuk National University, Republic of Korea.

Bio-Medical Materials and Engineering

|September 26, 2015

Summary

This summary is machine-generated.

A new multi-threaded tool, BWA-MT, significantly speeds up next-generation sequencing (NGS) data analysis by optimizing sequence alignment. This enhanced Burrows-Wheeler Aligner (BWA) achieves up to 3.7x faster performance while maintaining identical results.

Keywords:

burrow-wheeler aligner (BWA) tool genome sequencing multi-thread next-generation sequencing (NGS)

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Published on: October 10, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) generates vast amounts of data, necessitating efficient analysis tools.
Sequence alignment to a reference genome is a computationally intensive bottleneck in NGS data processing.
Existing tools like Burrows-Wheeler Aligner (BWA) lack full multi-threading support, limiting performance on modern multi-core processors.

Purpose of the Study:

To develop a multi-threaded version of BWA (BWA-MT) to accelerate sequence alignment.
To leverage multi-core architectures for faster processing of NGS data.
To ensure BWA-MT produces identical alignment results to the original BWA.

Main Methods:

Developed BWA-MT by incorporating multi-thread computation capabilities into the existing BWA framework.
Evaluated BWA-MT performance using the hg19 human genome reference and varying read sequence counts (1M to 40M).
Tested on a 12-core system with 32 GB of memory to assess speed and result accuracy.

Main Results:

BWA-MT demonstrated a significant performance increase, achieving up to 3.7 times faster alignment times compared to BWA.
The tool generated identical Sequence Alignment Map (SAM) files as BWA, ensuring data consistency.
Performance gains were observed across different data loads, confirming efficiency.

Conclusions:

BWA-MT effectively accelerates NGS data analysis by utilizing multi-core processing.
The tool offers a substantial speed improvement without compromising the accuracy of sequence alignment.
BWA-MT presents a highly efficient and effective solution for computationally intensive alignment tasks in genomics.