1 research outputs found
Lossy Compression of Quality Values via Rate Distortion Theory
Motivation: Next Generation Sequencing technologies revolutionized many
fields in biology by enabling the fast and cheap sequencing of large amounts of
genomic data. The ever increasing sequencing capacities enabled by current
sequencing machines hold a lot of promise as for the future applications of
these technologies, but also create increasing computational challenges related
to the analysis and storage of these data. A typical sequencing data file may
occupy tens or even hundreds of gigabytes of disk space, prohibitively large
for many users. Raw sequencing data consists of both the DNA sequences (reads)
and per-base quality values that indicate the level of confidence in the
readout of these sequences. Quality values account for about half of the
required disk space in the commonly used FASTQ format and therefore their
compression can significantly reduce storage requirements and speed up analysis
and transmission of these data.
Results: In this paper we present a framework for the lossy compression of
the quality value sequences of genomic read files. Numerical experiments with
reference based alignment using these quality values suggest that we can
achieve significant compression with little compromise in performance for
several downstream applications of interest, as is consistent with our
theoretical analysis. Our framework also allows compression in a regime - below
one bit per quality value - for which there are no existing compressors.Comment: 7 Pages, 8 Figures, Submitted to Bioinformatic