Small insertions and deletions (indels) of bases in the DNA of an organism can map to functionally important sites in human genes, for example, and in turn, influence human traits and diseases. Dindel detects such indels, particularly small indels (> 50 nucleotides), from short-read data by using a Bayesian approach. Due to its high sensitivity to detect small indels, Dindel has been adopted by many bioinformatics projects, e.g., the 1,000 Genomes Project, despite its pedestrian performance. In this paper, we first analyze and characterize the current version of Dindel to identify performance bottlenecks. We then design, implement, and optimize a parallelized Dindel (pDindel) for a multicore CPU architecture by exploiting thread-level parallelism (TLP) and data-level parallelism (DLP). Our optimized pDindel can achieve up to a 37-fold speedup for the computational part of Dindel and a 9-fold speedup for the overall execution time over the current version of Dindel.
['Da Zhang', 'Hao Wang', 'Kaixi Hou', 'Jing Zhang', 'Wu-chun Feng']
pDindel: Accelerating indel detection on a multicore CPU architecture with SIMD