Latest advances in distributed, parallel, and graphic processing unit accelerated approaches to computational biology

Bioinformatics is a discipline that performs analyses, modeling, and simulations of complex biological systems by using a computer science approach, which typically means dealing with huge amounts of data. Although the most powerful supercomputers in the world are heavily involved in computational biology research, scalability, portability, integration, and usability of bioinformatics software still represent open issues. Currently, the possibility of parallelizing algorithms and analysis techniques exploiting various high-performance computing (HPC) techniques and platforms is receiving an even increasing interest. Examples include the porting of legacy applications to clusters, such as those for genome analysis, and the use of distributed technologies like grid and cloud computing for large, embarrassingly parallel computations. Also, performance acceleration using on-chip supercomputing, such as graphic processing units (GPUs) and massively parallel architectures for the processing of large data sets, is becoming largely exploited. This trend is motivated by the lightening improvement of novel molecular biology high-throughput technologies, such as next generation sequencing, which allow the analysis of inter personal variations in genomics and transcriptomics, but also to the development of mass spectrometry techniques for proteomics and metabolomics profiles. This clearly calls for novel solutions in the field of HPC. Also in the field of structural biology, in silico molecular dynamic simulations, ligand screening projects for neglected and complex diseases, and drug discovery campaigns are examples of the great advantages that HPC can provide to medicine and healthcare. Many are the projects aiming at developing technologies in the field of high-performance computational biology worldwide. Examples are the EU-funded FP7 projects Venus-C [1] and scientific gateway based user interface (SCI-BUS) [2], and the project Extreme Science and Engineering Discovery Environment (XSEDE) [3] funded by the US National Science Foundation. These projects aim to improve the quality of HPC services for diverse research areas and support a large number of applications and projects in computational biology, for example, Biodrugscore [4] in XSEDE and the Swiss Proteomics Gateway [5] in SCI-BUS. Furthermore, the FP7 projects BioHPC [6] and Elixir [7] are specifically dedicated to the life sciences. Whereas BioHPC offers a suite of applications in a science gateway, Elixir functions as data hub for life sciences organizations. As regards for the most important national initiatives, the Flagship project Interomics [8], Laboratory for Interdisciplinary Technologies in Bioinformatics (LITBIO) [9], and Italian Bioinformatics Network (Italbionet)[10] in Italy and IdeeB [11], Grid Support for Bioinformatics [12] and Renabi [13] in France are concerned with the development of bioinformatics HPC infrastructures. Also, in the UK, there are very important initiatives in this sense, such as MyGrid [14], which is very active in the development of tools for e-Science. In Germany, the D-Grid project MediGRID [15] was focused especially on grid services for biomedical research. Its work is continued via Technologie und Methodenplattform für die vernetzte medizinische Forschung e.V. [16], which is the umbrella organization for networked medical research in Germany. The D-Grid project MoSGrid [17] offers a complete solution for the molecular simulation community supporting HPC infrastructures via a web-based science gateway. It is being further developed via SCI-BUS. WeNMR [18] follows a similar approach like MoSGrid and it supplies a worldwide e-Infrastructure for NMR and structural biology. All these projects have prompted the diffusion of parallel and distributed solutions for bioinformatics, which also promoted the discussion of these topics in a number of conferences. In particular, this special issue of Concurrency and Computation: Practice and Experience is partially a follow-up to some special