Call For Participation
High-throughput DNA sequencing instruments are capable of generating terabytes of sequencing data in a single experiment at a cost that is affordable on a routine basis. Analyzing such data is fundamental to many applications including genome resequencing, de novo genome sequencing, transcriptome sampling, metagenomics, and population diversity studies. The rate and volume of data generation is exposing the limitations of serial bioinformatics software. Effective exploitation of high performance computing technologies including multicores, accelerators, cluster and cloud computing platforms can bridge this critical gap.
The goal of this workshop is to bring together a community of bioinformatics researchers interested in development of parallel algorithms and high performance computing software for high-throughput DNA sequence analysis and its myriad applications. In particular, this workshop focuses on community-driven development of parallel software libraries to enable the bioinformatics community to more easily exploit high performance computing technologies. Development of such libraries is feasible because bioinformatics applications often rely on a common core of index and data structures – for e.g., look up tables, suffix trees/arrays, de Bruijn graphs etc. Such libraries have proved enormously useful in other application domains (e.g. BLAS libraries for scientific computing), and similar efforts are currently underway in other application domains (e.g. parallel graph libraries).
This workshop is supported in part by an NSF/NIH Big Data award to develop parallel software libraries for high throughput sequencing.
School of Computational Science and Engineering
Georgia Institute of Technology
Atlanta, GA 30332, USA
- National Science Foundation
- National Institutes of Health
- Intel Parallel Computing Center on Big Data in Biosciences and Public Health
|2nd||Workshop on Parallel Software Libraries for Sequence Analysis, 2016|
|1st||Workshop on Parallel Software Libraries for Sequence Analysis, 2015|