The analysis of sequencing data is demanding because of the enormous data volume and the need for fast turnaround time, accuracy, reproducibility, and data security. This requires a large variety of expertise: Algorithm design, strong implementation skills for analyzing big data on standard hardware and accelerators, statistical knowledge, and specific domain knowledge for each medical problem. Hence, the development of tools is often fragmented, mainly driven by academic groups and SMEs with different levels of expertise in the required domains. We aim to address this problem by enabling academic groups and SMEs to significantly accelerate their time to market for innovative technical solutions in medical diagnostics by providing the open source software development kit (SDK) that enables researchers and software engineers to build efficient, hardware-accelerated, and sustainable tools for the analysis of medical NGS data. In this project we address the tight integration of Intel-based (co)processors (i.e. Xeon, Xeon Phi) to provide fast, well-tested, algorithmic components for medical next generation sequence (NGS) analysis by extending the existing C++ library SeqAn.

The project started in December 2015 and ended in 2020.