High-throughput technologies (e.g. microarray, mass spectrometry, NGS) and clinical diagnostic tools (e.g. medical imaging) are producing an increasing amount of experimental and clinical data. In such a scenario, large-scale databases and bioinformatics tools are key tools for organizing and exploring biological and biomedical data with the aim to discover new knowledge in biology and medicine.

High-performance computing may play an important role in many phases of life sciences research, from raw data management and processing, to data analysis and integration, till data exploration and visualization. In particular, at the raw data layer, Grid infrastructures may offer the huge data storage needed to store experimental and biomedical data, while parallel computing can be used for basic pre-processing (e.g. parallel BLAST) and for more advanced analysis (e.g. parallel data mining). In such a scenario, novel parallel architectures (e.g. e.g. CELL processors, GPUs, FPGA, hybrid CPU/FPGA) coupled with emerging programming models may overcome the limits posed by conventional computers to the mining and exploration of large amounts of data.

At an higher layer, emerging biomedical applications need to use in a coordinated way both bioinformatics tools, biological data banks and patient’s clinical data, that require seamless integration, privacy preservation and controlled sharing. Service Oriented Architectures and semantic technologies, such as ontologies, may allow the building and deployment of the so-called collaboratories where remote scientists may conduct experimental research in a collaborative way.

The goal of HiBB is to bring together scientists in the fields of bioinformatics, biomedicine, medical informatics, high performance computing, as well as scientists working in biology and medicine, to discuss, among the others, the challenges and the requirements posed by novel data analysis pipelines for the management and analysis of omics data, that are more and more produced by high-throughput experimental platforms as well as diagnostic tools. Furthermore, the use of novel parallel architectures and dedicated hardware to implement bioinformatics and biomedical algorithms will be discussed.