  A wide variety of large-scale data has been produced in bioinformatics. In
response, the need for efficient handling of biomedical big data has been
partly met by parallel computing. However, the time demand of many
bioinformatics programs still remains high for large-scale practical uses due
to factors that hinder acceleration by parallelization. Recently, new
generations of storage devices have emerged, such as NAND flash-based
solid-state drives (SSDs), and with the renewed interest in near-data
processing, they are increasingly becoming acceleration methods that can
accompany parallel processing. In certain cases, a simple drop-in replacement
of hard disk drives (HDDs) by SSDs results in dramatic speedup. Despite the
various advantages and continuous cost reduction of SSDs, there has been little
review of SSD-based profiling and performance exploration of important but
time-consuming bioinformatics programs. For an informative review, we perform
in-depth profiling and analysis of 23 key bioinformatics programs using
multiple types of devices. Based on the insight we obtain from this research,
we further discuss issues related to design and optimize bioinformatics
algorithms and pipelines to fully exploit SSDs. The programs we profile cover
traditional and emerging areas of importance, such as alignment, assembly,
mapping, expression analysis, variant calling, and metagenomics. We explain how
acceleration by parallelization can be combined with SSDs for improved
performance and also how using SSDs can expedite important bioinformatics
pipelines, such as variant calling by the Genome Analysis Toolkit (GATK) and
transcriptome analysis using RNA sequencing (RNA-seq). We hope that this review
can provide useful directions and tips to accompany future bioinformatics
algorithm design procedures that properly consider new generations of powerful
storage devices.
