Optimization of data-intensive next generation sequencing in high performance computing

  title={Optimization of data-intensive next generation sequencing in high performance computing},
  author={Nagarajan Kathiresan and Rashid Al-Ali and Puthen V. Jithesh and Tariq AbuZaid and Ramzi Temanni and Andrey Ptitsyn},
  journal={2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)},
Advancement in Next Generation Sequencing (NGS) technology are associated with ever-increasing volume of genomic data every year. These genomic data are efficiently processed by empirical parallelism using High Performance Computing (HPC). The processed data can be used for genome-wide association studies, genetics, personalized medicine and many other areas. There are different kind of algorithms and implementations used in different phases of genome processing. In this paper, we used BWAKIT… CONTINUE READING

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Key Quantitative Results

  • We observed that, 40%, 65%, 71% and 76% improvement in performance while processing 2, 4, 8 and 16 samples concurrently using our own scheduling heuristics. As a result, our proposed NGS workflow automation will improve the performance upto 76% compared to application scalability based workflows.


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