Light-weight reference-based compression of FASTQ data.

ABSTRACT:

Background

The exponential growth of next generation sequencing (NGS) data has posed big challenges to data storage, management and archive. Data compression is one of the effective solutions, where reference-based compression strategies can typically achieve superior compression ratios compared to the ones not relying on any reference.

Results

This paper presents a lossless light-weight reference-based compression algorithm namely LW-FQZip to compress FASTQ data. The three components of any given input, i.e., metadata, short reads and quality score strings, are first parsed into three data streams in which the redundancy information are identified and eliminated independently. Particularly, well-designed incremental and run-length-limited encoding schemes are utilized to co

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC4459677 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature