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Related Experiment Video

Updated: May 22, 2026

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Collection and Extraction of Saliva DNA for Next Generation Sequencing

Published on: August 27, 2014

Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform.

Anthony J Cox¹, Markus J Bauer, Tobias Jakobi

¹Computational Biology Group, Illumina Cambridge Ltd., Chesterford Research Park, Little Chesterford, Essex, UK. acox@illumina.com

Bioinformatics (Oxford, England)

|May 5, 2012

Summary

The Burrows-Wheeler transform (BWT) enables efficient compression of large DNA sequence data. Novel methods achieve lossless compression of 45x human genome data to under 0.5 bits per base, significantly reducing storage needs.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

The Burrows-Wheeler transform (BWT) is crucial for text compression and indexing.
High computational costs limit BWT application to large DNA sequencing datasets.
Previous work enabled BWT computation on human genome-scale data with moderate hardware.

Purpose of the Study:

Investigate BWT for compressing large DNA sequence datasets.
Develop efficient compression strategies for genomic data.
Enable compressed full-text indexing of large sequence collections.

Main Methods:

Utilized simulated reads to analyze compression levels against error rates, read lengths, and genome sampling.
Developed a novel 'implicit sorting' strategy for sequence reordering.
Applied BWT and second-stage compression algorithms.

Main Results:

Achieved lossless compression of 45x human genome data to under 0.5 bits per base (8.2 GB for 135.3 Gb).
Demonstrated compression improvements through sequence reordering and implicit sorting.
Obtained >4x size reduction compared to standard BWT compressors like bzip2.
Facilitated building compressed full-text indexes (e.g., FM-index) on large DNA collections.

Conclusions:

The developed BWT-based techniques offer significant data compression for large genomic datasets.
Implicit sorting enables compression benefits without sorting overhead.
The approach facilitates advanced indexing of massive DNA sequence collections.