MUSIC

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