MUSIC

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