Yun Heo1, Anand Ramachandran1, Wen-Mei Hwu1, Jian Ma2, Deming Chen1. 1. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. 2. Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Abstract
UNLABELLED: The most important features of error correction tools for sequencing data are accuracy, memory efficiency and fast runtime. The previous version of BLESS was highly memory-efficient and accurate, but it was too slow to handle reads from large genomes. We have developed a new version of BLESS to improve runtime and accuracy while maintaining a small memory usage. The new version, called BLESS 2, has an error correction algorithm that is more accurate than BLESS, and the algorithm has been parallelized using hybrid MPI and OpenMP programming. BLESS 2 was compared with five top-performing tools, and it was found to be the fastest when it was executed on two computing nodes using MPI, with each node containing twelve cores. Also, BLESS 2 showed at least 11% higher gain while retaining the memory efficiency of the previous version for large genomes. AVAILABILITY AND IMPLEMENTATION: Freely available at https://sourceforge.net/projects/bless-ec CONTACT: dchen@illinois.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
UNLABELLED: The most important features of error correction tools for sequencing data are accuracy, memory efficiency and fast runtime. The previous version of BLESS was highly memory-efficient and accurate, but it was too slow to handle reads from large genomes. We have developed a new version of BLESS to improve runtime and accuracy while maintaining a small memory usage. The new version, called BLESS 2, has an error correction algorithm that is more accurate than BLESS, and the algorithm has been parallelized using hybrid MPI and OpenMP programming. BLESS 2 was compared with five top-performing tools, and it was found to be the fastest when it was executed on two computing nodes using MPI, with each node containing twelve cores. Also, BLESS 2 showed at least 11% higher gain while retaining the memory efficiency of the previous version for large genomes. AVAILABILITY AND IMPLEMENTATION: Freely available at https://sourceforge.net/projects/bless-ec CONTACT: dchen@illinois.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.