  High-throughput DNA sequencers are becoming indispensable in our
understanding of diseases at molecular level, in marker-assisted selection in
agriculture and in microbial genetics research. These sequencing instruments
produce enormous amount of data (often terabytes of raw data in a month) that
requires efficient analysis, management and interpretation. The commonly used
sequencing instrument today produces billions of short reads (upto 150 bases)
from each run. The first step in the data analysis step is alignment of these
short reads to the reference genome of choice. There are different open source
algorithms available for sequence alignment to the reference genome. These
tools normally have a high computational overhead, both in terms of number of
processors and memory. Here, we propose a hybrid-computing environment called
MUSIC (Mapping USIng hybrid Computing) for one of the most popular open source
sequence alignment algorithm, BWA, using accelerators that show significant
improvement in speed over the serial code.
