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Open Access Highly Accessed Research article

Data structures and compression algorithms for high-throughput sequencing technologies

Kenny Daily12, Paul Rigor12, Scott Christley134, Xiaohui Xie124 and Pierre Baldi1245*

Author Affiliations

1 Department of Computer Science, University of California Irvine, Irvine, CA 92697 USA

2 Institute for Genomics and Bioinformatics, University of California Irvine, Irvine, CA 92697 USA

3 Department of Mathematics, University of California Irvine, Irvine, CA 92697 USA

4 Center for Complex Biological Systems, University of California Irvine, Irvine, CA 92697 USA

5 Department of Biological Chemistry, University of California Irvine, Irvine, CA 92697 USA

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BMC Bioinformatics 2010, 11:514  doi:10.1186/1471-2105-11-514

Published: 14 October 2010

Abstract

Background

High-throughput sequencing (HTS) technologies play important roles in the life sciences by allowing the rapid parallel sequencing of very large numbers of relatively short nucleotide sequences, in applications ranging from genome sequencing and resequencing to digital microarrays and ChIP-Seq experiments. As experiments scale up, HTS technologies create new bioinformatics challenges for the storage and sharing of HTS data.

Results

We develop data structures and compression algorithms for HTS data. A processing stage maps short sequences to a reference genome or a large table of sequences. Then the integers representing the short sequence absolute or relative addresses, their length, and the substitutions they may contain are compressed and stored using various entropy coding algorithms, including both old and new fixed codes (e.g Golomb, Elias Gamma, MOV) and variable codes (e.g. Huffman). The general methodology is illustrated and applied to several HTS data sets. Results show that the information contained in HTS files can be compressed by a factor of 10 or more, depending on the statistical properties of the data sets and various other choices and constraints. Our algorithms fair well against general purpose compression programs such as gzip, bzip2 and 7zip; timing results show that our algorithms are consistently faster than the best general purpose compression programs.

Conclusions

It is not likely that exactly one encoding strategy will be optimal for all types of HTS data. Different experimental conditions are going to generate various data distributions whereby one encoding strategy can be more effective than another. We have implemented some of our encoding algorithms into the software package GenCompress which is available upon request from the authors. With the advent of HTS technology and increasingly new experimental protocols for using the technology, sequence databases are expected to continue rising in size. The methodology we have proposed is general, and these advanced compression techniques should allow researchers to manage and share their HTS data in a more timely fashion.