Abstract:
Advances in sequencing technologies have revolutionized the eld of genomics by providing
cost e ective and high throughput solutions. In this paper, we develop a parallel
sequence assembler implemented on general purpose graphic processor units (GPUs). Our
work was largely motivated by a growing need in the genomic community for sequence
assemblers and increasing use of GPUs for general purpose computing applications. We investigated
the implementation challenges, and possible solutions for a data parallel approach
for sequence assembly. We implemented an Eulerian-based sequence assembler (GPU-Euler)
on the nVidia GPUs using the CUDA programming interface. GPU-Euler was benchmarked
on three bacterial genomes using input reads representing the new generation of sequencing
approaches. Our empirical evaluation showed that GPU-Euler produced lower run times,
and comparable performance in terms of contig length statistics to other serial assemblers.
We were able to demonstrate the promise of using GPUs for genome assembly, a computationally
intensive task.
An error correction step was also incorporated into GPU-Euler to be able to process
reads containing some errors. Error correction output was benchmarked on simulated read
on three bacterial genomes with different read length.
