LFQC

MOTIVATION: Next Generation Sequencing (NGS) technologies have revolutionized
genomic research by reducing the cost of whole genome sequencing. One of the
biggest challenges posed by modern sequencing technology is economic storage of
NGS data. Storing raw data is infeasible because of its enormous size and high
redundancy. In this article, we address the problem of storage and transmission
of large FASTQ files using innovative compression techniques. RESULTS: We
introduce a new lossless non-reference based FASTQ compression algorithm named
Lossless FASTQ Compressor. We have compared our algorithm with other state of the
art big data compression algorithms namely gzip, bzip2, fastqz (Bonfield and
Mahoney, 2013), fqzcomp (Bonfield and Mahoney, 2013), Quip (Jones et al., 2012),
DSRC2 (Roguski and Deorowicz, 2014). This comparison reveals that our algorithm
achieves better compression ratios on LS454 and SOLiD datasets. AVAILABILITY AND
IMPLEMENTATION: The implementations are freely available for non-commercial
purposes. They can be downloaded from
http://engr.uconn.edu/rajasek/lfqc-v1.1.zip. CONTACT: rajasek@engr.uconn.edu.

LFQC: a lossless compression algorithm for FASTQ files.

LFQC is a lossless, non-reference based FASTQ compression algorithm that addresses the problem of storage and transmission of large FASTQ files. The algorithm achieves better compression ratios than other state-of-the-art big data compression algorithms such as gzip, bzip2, fastqz, fqzcomp, Quip, and DSRC2 on LS454 and SOLiD datasets. 

LFQC

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