This supplement contains seven revised and extended papers selected from CIBB 2018 and CIBB 2019, the 15th and 16th editions of the international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB is a venue that embraces researchers with different backgrounds, ranging from mathematics to computer science, from materials science to medicine, and from engineering to biology, all interested in the investigation and application of computational intelligence methods to open problems in bioinformatics, biostatistics, systems biology, synthetic biology, and medical informatics.

BACKGROUND: High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. METHODS: Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. RESULTS: These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). CONCLUSION: With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

SUMMARY: Retroviruses and their vector derivatives integrate semi-randomly in the genome of host cells and are inherited by their progeny as stable genetic marks. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites in gene therapy or virally infected patients, essential for monitoring the evolution of genetically modified cells in vivo. However, since ∼30% of insertions land in low complexity or repetitive regions of the host cell genome, they cannot be correctly assigned and are currently discarded, limiting the accuracy and predictive power of clonal tracking studies. Here, we present γ-TRIS, a new graph-based genome-free alignment tool for identifying insertion sites even if embedded in low complexity regions. By using γ-TRIS to reanalyze clinical studies, we observed improvements in clonal quantification and tracking. AVAILABILITY AND IMPLEMENTATION: Source code at https://bitbucket.org/bereste/g-tris. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract This editorial introduces the articles selected for the special issue concerning the International Conferences on Parallel, Distributed, and Network‐Based Processing, which provided insights related to the efficient exploitation of parallel and distributed architectures, including power‐aware computing, application scheduling, and application development for GPUs.

Summary In the perspective of energy‐efficient storage solutions supporting scientific computing, we investigate the possibility of using low‐power Systems‐On‐Chip (SoCs) as storage bricks of a BeeGFS file system in support of a realistic genome sequencing pipeline from Bioinformatics. Metadata and data performances of such a file system made of low‐power devices are assessed via various benchmarks for an increasing number of clients accessing the data, and compared to those achieved with traditional power‐hungry architectures with a similar BeeGFS setup. Then, we show how the sequencing information streamed by portable sequencing devices, such as the Oxford Nanopore MinION, can be managed by the underlying BeeGFS file system built with low‐power SoCs.

Computational Biology is a fast-growing field that is enriched by different data-driven methodological approaches and by findings and applications in a broad range of biological areas. Fundamental to these approaches are the mathematical and computational models used to describe the different state s at microscopic (for example a biochemical reaction), mesoscopic (the signalling effects at tissue level), and macroscopic levels (physiological and pathological effects) of biological processes. In this paper we address the problem of combining two powerful classes of methodologies: Flux Balance Analysis (FBA) methods which are now producing a revolution in biotechnology and medicine, and Petri Nets (PNs) which allow system generalisation and are central to various mathematical treatments, for example Ordinary Differential Equation (ODE) specification of the biosystem under study. While the former is limited to modelling metabolic networks, i.e. does not account for intermittent dynamical signalling events, the latter is hampered by the need for a large amount of metabolic data. A first result presented in this paper is the identification of three types of cross-talks between PNs and FBA methods and their dependencies on available data. We exemplify our insights with the analysis of a pancreatic cancer model. We discuss how our reasoning framework provides a biologically and mathematically grounded decision making setting for the integration of regulatory, signalling, and metabolic networks and greatly increases model interpretability and reusability. We discuss how the parameters of PN and FBA models can be tuned and combined together so to highlight the computational effort needed to perform this task. We conclude with speculations and suggestions on this new promising research direction.

In the last two decades deep learning has attracted a lot of attention internationally, solving problems in different application domains and achieving results beyond expectations. For example it has been applied in bioinformatics, game playing, imaging processing, object detection, robotic and drug discovery. One of the main reasons for the incremented use of deep learning algorithms is the need to implement approaches for the analysis of the large amount of data produces in every field, bringing researchers to dedicate their work to deep learning development. One of the main topics discussed up today is the possibility to run the training of deep models in a parallel fashion, so to reduce the time otherwise needed to find the hyperparameters and to make the achievement of the result faster.

BACKGROUND: In order to fully characterize the genome of an individual, the reconstruction of the two distinct copies of each chromosome, called haplotypes, is essential. The computational problem of inferring the full haplotype of a cell starting from read sequencing data is known as haplotype assembly, and consists in assigning all heterozygous Single Nucleotide Polymorphisms (SNPs) to exactly one of the two chromosomes. Indeed, the knowledge of complete haplotypes is generally more informative than analyzing single SNPs and plays a fundamental role in many medical applications. RESULTS: To reconstruct the two haplotypes, we addressed the weighted Minimum Error Correction (wMEC) problem, which is a successful approach for haplotype assembly. This NP-hard problem consists in computing the two haplotypes that partition the sequencing reads into two disjoint sub-sets, with the least number of corrections to the SNP values. To this aim, we propose here GenHap, a novel computational method for haplotype assembly based on Genetic Algorithms, yielding optimal solutions by means of a global search process. In order to evaluate the effectiveness of our approach, we run GenHap on two synthetic (yet realistic) datasets, based on the Roche/454 and PacBio RS II sequencing technologies. We compared the performance of GenHap against HapCol, an efficient state-of-the-art algorithm for haplotype phasing. Our results show that GenHap always obtains high accuracy solutions (in terms of haplotype error rate), and is up to 4× faster than HapCol in the case of Roche/454 instances and up to 20× faster when compared on the PacBio RS II dataset. Finally, we assessed the performance of GenHap on two different real datasets. CONCLUSIONS: Future-generation sequencing technologies, producing longer reads with higher coverage, can highly benefit from GenHap, thanks to its capability of efficiently solving large instances of the haplotype assembly problem. Moreover, the optimization approach proposed in GenHap can be extended to the study of allele-specific genomic features, such as expression, methylation and chromatin conformation, by exploiting multi-objective optimization techniques. The source code and the full documentation are available at the following GitHub repository: https://github.com/andrea-tango/GenHap .

A method and a stand-alone Python code to estimate the 3D chromatin structure from chromosome conformation capture data are presented. The method is based on a multiresolution, modified-bead-chain chromatin model, evolved through quaternion operators in a Monte Carlo sampling. The solution space to be sampled is generated by a score function with a data-fit part and a constraint part where the available prior knowledge is implicitly coded. The final solution is a set of 3D configurations that are compatible with both the data and the prior knowledge. The iterative code, provided here as additional material, is equipped with a graphical user interface and stores its results in standard-format files for 3D visualization. We describe the mathematical-computational aspects of the method and explain the details of the code. Some experimental results are reported, with a demonstration of their fit to the data.

We present a parallelizzable, multilevel algorithm for the study of three-dimensional structure of biological macromolecules, applied to two fundamental topics: the 3D reconstruction of Chromatin and the elaboration of motion of proteins. For Chromatin, starting from contact data obtained through Chromosome Conformation Capture techniques, our method first subdivides the data matrix in biologically relevant blocks, and then treats them separately, at several levels, depending on the initial data resolution. The result is a family of configurations for the entire fiber, each one compatible with both experimental data and prior knowledge about specific genomes. For Proteins, the method is conceived as a solution for the problem of identifying motion and alternative conformations to the deposited structures. The algorithm, using quaternions, processes the main chain and the aminoacid side chian independently; it then exploits a Monte Carlo method for selection of biologically acceptable conformations, based on energy evaluation, and finally returns a family of conformations and of trajectories at single atom resolution.

In complex phenotypes (e.g., psychiatric diseases) single locus tests, commonly performed with genome-wide association studies, have proven to be limited in discovering strong gene associations. A growing body of evidence suggests that epistatic non-linear effects may be responsible for complex phenotypes arising from the interaction of different biological factors. A major issue in epistasis analysis is the computational burden due to the huge number of statistical tests to be performed when considering all the potential genotype combinations. In this work, we developed a computational efficient approach to compute empirical p-values concerning the presence of epistasis at a genome-wide scale in bipolar disorder, which is a typical example of complex phenotype with a relevant but unexplained genetic background. By running our approach we were able to identify 13 epistasis interactions between variants located in genes potentially involved in biological processes associated with the analyzed phenotype.

Low-power Systems on Chip (SoCs) derived from the embedded and mobile market can be profitably used to execute scientific workloads traditionally designed for power-hungry clusters, saving energy, gaining portability and reducing infrastructural costs and sizes. We investigate the possibility of using SoCs as storage bricks of a BeeGFS filesystem in the perspective of energy-efficient storage solutions supporting scientific computing. Then, we consider a use case from metagenomics analysis and show how the large amount of genome sequencing information streamed by portable sequencing devices could be managed by low-power SoCs making use of an underlying BeeGFS filesystem.

In molecular biology, the interaction mechanisms between microRNAs (miRNAs) with their messenger RNA targets are poorly understood. This is the reason why many miRNA-target prediction methods are available, but their results are often inconsistent. A lot of efforts focus on the quality of the sequence match between miRNA and target rather than on the role of the mRNA secondary structure in which the target is embedded. Nonetheless, it is known that the miRNA secondary structures contribute to target recognition, because there is an energetic cost to freeing base-pairing interactions within mRNA to make the target accessible for miRNA binding. This approach is implemented by PITA (Probability of Interaction by Target Accessibility), a very computational-intensive tool that is able to provide accurate results even when little is know about the conservation of the miRNA. In this paper we propose a new implementation of PITA, called lPITA, able to exploit a coarse-grained parallelism over low power architectures to reduce both execution times and the power consumption.

In the last decade, different computing paradigms and modelling frameworks for the description and simulation of biochemical systems based on stochastic modelling have been proposed. From a computational point of view, many simulations of the model are necessary to identify the behaviour of the system. The execution of thousands of simulations can require huge amount of time, therefore the parallelization of these algorithms is highly desirable. In particular, models that consider the size of volumes and objects involved in the reaction are very time-consuming, since many rules should be considered to take into account the position of the different molecules. In this work we present an implementation of a stochastic space-aware simulator which exploits the benefit and features of hybrid low-power computing architectures. This work shows that the simulator dynamic probabilistic approach to select possible chemical reactions can be applied and implemented in hybrid low-power low-cost architectures as well as current industry high-end servers.

BACKGROUND: Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process "big data" in a reasonable computational time. RESULTS: Here we present VISPA2 (Vector Integration Site Parallel Analysis, version 2), the latest optimized computational pipeline for integration site identification and analysis with the following features: (1) the sequence analysis for the integration site processing is fully compliant with paired-end reads and includes a sequence quality filter before and after the alignment on the target genome; (2) an heuristic algorithm to reduce false positive integration sites at nucleotide level to reduce the impact of Polymerase Chain Reaction or trimming/alignment artifacts; (3) a classification and annotation module for integration sites; (4) a user friendly web interface as researcher front-end to perform integration site analyses without computational skills; (5) the time speedup of all steps through parallelization (Hadoop free). CONCLUSIONS: We tested VISPA2 performances using simulated and real datasets of lentiviral vector integration sites, previously obtained from patients enrolled in a hematopoietic stem cell gene therapy clinical trial and compared the results with other preexisting tools for integration site analysis. On the computational side, VISPA2 showed a > 6-fold speedup and improved precision and recall metrics (1 and 0.97 respectively) compared to previously developed computational pipelines. These performances indicate that VISPA2 is a fast, reliable and user-friendly tool for integration site analysis, which allows gene therapy integration data to be handled in a cost and time effective fashion. Moreover, the web access of VISPA2 ( http://openserver.itb.cnr.it/vispa/ ) ensures accessibility and ease of usage to researches of a complex analytical tool. We released the source code of VISPA2 in a public repository ( https://bitbucket.org/andreacalabria/vispa2 ).

Although several computational methods have been developed for predicting interactions between miRNA and target genes, there are substantial differences in the achieved results. For this reason, machine learning approaches are widely used for integrating the predictions obtained from different tools. In this work we adopt a method, called M3GP, which relies on a genetic programming approach, to classify results from three tools: miRanda, TargetScan, and RNAhybrid. Such algorithm is highly parallelizable and its adoption provides great advantages while handling problems involving big datasets, since it is independent from the implementation and from the architecture on which it is executed. More precisely, we apply this technique for the classification of the achieved miRNA target predictions and we compare its results with those obtained with other classifiers.

MicroRNAs (or miRNA) are key regulators of gene expression, but the precise mechanisms underlying their interaction with their mRNA targets are still poorly understood. Since miRNA are involved in the onset of many different diseases, the study of their interaction with the genome is very important to study. Although the experimental identification of miRNA can be performed using sequencing techniques, the characterization of their target is quite complex. Therefore, computational methods are required to focus the analysis on small set of possibilities. Unfortunately, the limited knowledge about the processes that regulate miRNA target association impairs the development of optimal algorithm for prediction of the regulated genes. This is the reason why a variety of miRNA target prediction algorithms are available, but results of their application are often inconsistent. Therefore, many miRNA targets have been computationally predicted, but only a limited number of these were experimentally validated. Different approaches can be used to recognize miRNA targets. These efforts have focused primarily on the quality of the sequence match between microRNA and target rather than on the role of the mRNA secondary structure in which the target is embedded. Nonetheless, it is known that the miRNA secondary structures contribute to target recognition, because there is an energetic cost to freeing base-pairing interactions within mRNA in order to make the target accessible for microRNA binding. This second approach can provide good results even when little is know about the conservation of the miRNA, but it has the drawback of being computationally very expensive. In this paper we propose a parallelization of PITA, which is one of the most popular algorithm that exploits energetic considerations for computing the miRNA-target predictions.

Chromosome conformation capture techniques are producing a huge amount of data about the architecture of our genome. These data can provide us with a better understanding of the events that induce critical regulations of the cellular function from small changes in the three-dimensional genome architecture. Generating a unified view of spatial, temporal, genetic and epigenetic properties poses various challenges of data analysis, visualization, integration and mining, as well as of high performance computing and big data management. Here, we describe the critical issues of this new branch of bioinformatics, oriented at the comprehension of the three-dimensional genome architecture, which we call 'Nucleome Bioinformatics', looking beyond the currently available tools and methods, and highlight yet unaddressed challenges and the potential approaches that could be applied for tackling them. Our review provides a map for researchers interested in using computer science for studying 'Nucleome Bioinformatics', to achieve a better understanding of the biological processes that occur inside the nucleus.

BACKGROUND: Haplotype phasing is an important problem in the analysis of genomics information. Given a set of DNA fragments of an individual, it consists of determining which one of the possible alleles (alternative forms of a gene) each fragment comes from. Haplotype information is relevant to gene regulation, epigenetics, genome-wide association studies, evolutionary and population studies, and the study of mutations. Haplotyping is currently addressed as an optimisation problem aiming at solutions that minimise, for instance, error correction costs, where costs are a measure of the confidence in the accuracy of the information acquired from DNA sequencing. Solutions have typically an exponential computational complexity. WHATSHAP is a recent optimal approach which moves computational complexity from DNA fragment length to fragment overlap, i.e., coverage, and is hence of particular interest when considering sequencing technology's current trends that are producing longer fragments. RESULTS: Given the potential relevance of efficient haplotyping in several analysis pipelines, we have designed and engineered PWHATSHAP, a parallel, high-performance version of WHATSHAP. PWHATSHAP is embedded in a toolkit developed in Python and supports genomics datasets in standard file formats. Building on WHATSHAP, PWHATSHAP exhibits the same complexity exploring a number of possible solutions which is exponential in the coverage of the dataset. The parallel implementation on multi-core architectures allows for a relevant reduction of the execution time for haplotyping, while the provided results enjoy the same high accuracy as that provided by WHATSHAP, which increases with coverage. CONCLUSIONS: Due to its structure and management of the large datasets, the parallelisation of WHATSHAP posed demanding technical challenges, which have been addressed exploiting a high-level parallel programming framework. The result, PWHATSHAP, is a freely available toolkit that improves the efficiency of the analysis of genomics information.

BACKGROUND: Phosphorylation is one of the most important post-translational modifications (PTM) employed by cells to regulate several cellular processes. Studying the effects of phosphorylations on protein structures allows to investigate the modulation mechanisms of several proteins including chaperones, like the small HSPs, which display different multimeric structures according to the phosphorylation of a few serine residues. In this context, the proposed study is aimed at finding a method to correlate different PTM patterns (in particular phosphorylations at the monomers interface of multimeric complexes) with the dynamic behaviour of the complex, using physicochemical parameters derived from molecular dynamics simulations in the timescale of nanoseconds. RESULTS: We have developed a methodology relying on computing nine physicochemical parameters, derived from the analysis of short MD simulations, and combined with N identifiers that characterize the PTMs of the analysed protein. The nine general parameters were validated on three proteins, with known post-translational modified conformation and unmodified conformation. Then, we applied this approach to the case study of αB-Crystallin, a chaperone which multimeric state (up to 40 units) is supposed to be controlled by phosphorylation of Ser45 and Ser59. Phosphorylation of serines at the dimer interface induces the release of hexamers, the active state of αB-Crystallin. 30 ns of MD simulation were obtained for each possible combination of dimer phosphorylation state and average values of structural, dynamic, energetic and functional features were calculated on the equilibrated portion of the trajectories. Principal Component Analysis was applied to the parameters and the first five Principal Components, which summed up to 84 % of the total variance, were finally considered. CONCLUSIONS: The validation of this approach on multimeric proteins, which structures were known both modified and unmodified, allowed us to propose a new approach that can be used to predict the impact of PTM patterns in multi-modified proteins using data collected from short molecular dynamics simulations. Analysis on the αB-Crystallin case study clusters together all-P dimers with all-P hexamers and no-P dimer with no-P hexamer and results suggest a great influence of Ser59 phosphorylation on chain B.

High-throughput molecular biology techniques are widely used to identify physical interactions between genetic elements located throughout the human genome. Chromosome Conformation Capture (3C) and other related techniques allow to investigate the spatial organisation of chromosomes in the cell's natural state. Recent results have shown that there is a large correlation between co-localization and co-regulation of genes, but these important information are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we introduce NuChart-II, a tool for Hi-C data analysis that provides a gene-centric view of the chromosomal neighbourhood in a graph-based manner. NuChart-II is an efficient and highly optimized C++ re-implementation of a previous prototype package developed in R. Representing Hi-C data using a graph-based approach overcomes the common view relying on genomic coordinates and permits the use of graph analysis techniques to explore the spatial conformation of a gene neighbourhood.

The increasing awareness of the pivotal role of noise in biochemical systems has given rise to a strong need for suitable stochastic algorithms for the description and the simulation of biological phenomena. However, the high computational demand that characterizes stochastic simulation approaches coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviors makes the application of such kind of algorithms often unfeasible. So far, different parallelization approaches have been employed to reduce the computational time required for the analysis of biochemical systems modeled using stochastic algorithms. Most of the proposed solutions use an embarrassingly parallel approach to run in parallel several simulations using the cores of a workstation and/or the nodes of a cluster. In this work we present the Spatial TAU-leaping in Crowded Compartments (STAUCC) simulator, a software that relies on an efficient CUDA implementation of the Stau-DPP algorithm, a voxel-based method for the stochastic simulation of Reaction-Diffusion processes. We evaluate its application and performance for the modeling of diffusion processes simultaneously occurring within a space represented considering different levels of granularity.

The Grid infrastructures, such as the one provided by the European Grid Infrastructure, represent suitable solutions to achieve cost effective computational power. However, these solutions present to Grid users some challenges, i.e. to create a customized computing environment and its management.For this reason a Cloud-like approach is for certain applications more suitable than a pure Grid one.In this paper, we propose an approach based on WNoDeS- the Worker Nodes on Demand Service - to exploit both the computational and storage power of the Grid and the flexibility of the Cloud paradigm. This virtualization framework makes possible, trough a tight integration with Local Resource Management Systems, a dynamic allocation of virtual resources out of a common resource pool. WNoDes has been installed on some INFN Grid sites, allowing selected users to instantiate aset of customized virtual machines on top of the Grid resources. Running the applications on customized machines simplifies the computing model and reduces the failure rates. We show how it is feasible to smoothly perform a real life molecular surfaces analysis use case instantiating resources through WNoDeS.

SUMMARY A proper and efficient representation of possibly complex and large molecular surfaces is an important task in bioinformatics and biophysics. Molecular surfaces indeed are used for different aims, in particular, for computation, as visual support tools for biologists, in electrostatics problems involving implicit solvents (e.g. while solving the Poisson–Boltzmann equation) or for molecular dynamics simulations. This is the reason why, in literature, a multitude of algorithms that differ on the basis of the adopted representation and the approach/technology used were proposed. In this paper we present a CUDA‐based software component able to produce high‐resolution molecular surfaces based on the Van der Waals, solvent accessible, Richards–Connolly and blobby definitions. The component was designed to be used in heterogeneous visualization pipelines; therefore, the representation of the molecular surfaces suits both the direct visualization and the efficient storing for following processing steps. Experimental results show speedup figures between 39.3 and 88.4 considering molecules of different sizes and surface resolutions, resulting in meshes of up to 258.6 million triangles. Copyright © 2013 John Wiley & Sons, Ltd.

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 sessions/workshops held in the context of international conferences in the field of HPC, such as the special session ‘Grid, Parallel and Distributed Bioinformatics Applications’ of the ‘Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)’ 19. European Grid Infrastructure 20 has organized each year two forums as successors of the Enabling Grids for e-Science in Europe conferences 21 for the HPC community with dedicated sessions to the life sciences since 2009. Also, smaller events are launched very successfully in this field like the workshop series International Workshop on Science Gateways for Life Sciences (IWSG) 22 extended to IWSG for a wider community or the Black Forest Grid Workshop23. Accelerating applications using specific paradigms of computations, in particular for scientific computations, is a long-standing task and usually involves the use of custom-designed solutions. Traditionally, parallel computing has been employed for addressing bioinformatics problems that would otherwise be impossible to solve. A first memorable example has been the computational assembly of the human genome as post analysis of the whole genome shotgun sequencing proposed by Craig Venter, which challenged the long-standing Human Genome Project, arriving very close to an unpredictable victory 24. The point is that using parallel computing implies rethinking the whole application to exploit multiple processors, shared or distributed memory resources, and the network itself. The role of software architect is therefore of primary importance to maximize performance beside, and of course, the possibility of accessing a large computational facility 25. In this context, the cost of buying and maintaining an in-house cluster is very important, and this explains why the grid computing paradigm gained a great success in the mid-1990s 26. The term grid was used in analogy to the electric power grid to indicate the main goal to make the access to computing power as easy as the access to electricity. Ian Foster extended his definition of grid computing by a three-point checklist, which emphasizes that a grid manages distributed resources, uses standard, open, and generic-purpose protocols and interfaces, and carries out nontrivial quality of services 27. Grid middlewares are responsible for all the aspects related to the efficient management of the available computing power: the authentication of users, the submission and monitoring of jobs, and the data movement. Accordingly, the underlying hardware, the involved operating systems, batch systems, and file systems are hidden from the users. Grid was a very innovative paradigm of distributed and collaborative computing, but it often requires users to adapt their code for being able to run in this environment. Ten years later, cloud computing was presented as a more flexible solution 28. Cloud computing overcomes the idea of volunteer computing for resource sharing by proposing an ‘on-demand’ paradigm in which users pay for what they use. Cloud computing providers offer their services according to several fundamental models: infrastructure as a service (IaaS), platform as a service, and software as a service, where IaaS is the most basic model whereas the other provide higher level of abstraction 29. Furthermore, in the last decade, new paradigms have emerged in parallel computing, in particular concerning the exploitation of massively parallel architectures (GPUs) 30 such as CUDA 31 and OpenCL 32. Users can presently exploit up to 10 TFlops on a single workstation equipped with multiple CPUs and accelerators, with a cost of less than $5000 33. This allows also low-budget research groups to achieve very high computational performance for many compute-intensive applications in the field of computational biology 34. It is widely recognized that the biology research field is data driven because of the presence of high-throughput acquisition techniques and the increased level of details of simulated biological complex systems. The consequence is that there is the need to apply the most advanced HPC techniques and platforms to process the data and to turn them into real knowledge. This explains why all the available HPC solutions have been exploited to deal with the different kinds of analyses. Several kinds of analysis algorithms have been developed with very different behaviors: some of them are mainly CPU-intensive, whereas others require to access a large amount of data; some require a high number of communications between the parallel processes for each step of the computation, and some analysis require a stochastic approach where the accuracy of results is proportional to the number of runs. This is the reason why different parallelization paradigms, parallel, and distributed architectures have been considered in order to be able to achieve the highest possible performance figures. Salah and Kenli 35 proposed PAR-3D-BLAST, a parallel tool for protein structure comparison, where a two-level parallelism is exploited to improve the performance. Meier-Kolthoff et al. 36 describe a parallelized method to infer phylogenetic relationship using Genome Blast Distance Phylogeny on a reference set of microbial genome from the GEBA project. In particular, they developed an infrastructure in order to save computational time and data storage able to scale up the generation of phylogenetic tree. Even if the low-level details of the grid infrastructures are hidden via middlewares, often the application of bioinformatics methods on HPC facilities require specialized knowledge. Thus, researchers in this field currently need to deal especially with usability issues. On the one hand, these issues derive from the applied methods: the usability of many tools is limited and the implemented methods are very complex, reflecting the underlying complex theory. They thus require a lot of experience, also because the lack of user interfaces and of pre-configured settings deter novice users from them. It is clear that users have to become acquainted with the domain-related features, but the presence of a mere command line interface represents a big issue for a wide audience. On the other hand, the heterogeneity of the underlying distributed computing infrastructure augments the complexity, especially for users who do not have an information and communication technology background. A suitable solution to offer easy-to-use and intuitive access to applications are science gateways, which offer specific services tailored to the users' needs. Additionally, the users mostly do not only analyze and process data via single jobs but via workflows in computational biology. Thus, science gateways capable of managing workflows are essential for processing all the necessary steps. Kertesz et al. 37 introduced a case study on generating conformers via unconstrained molecular dynamics parallelizing single steps and creating a reusable workflow in the grid portal WS-PGRADE. The workflow can be performed significantly faster on European grid infrastructures than on single CPU machines. Grunzke et al. 38 addressed a further aspect of usability in workflow-enabled science gateways: metadata management via standards. They present Molecular Simulation Markup Language for the area of molecular simulations of small and large molecules. Molecular Simulation Markup Language allows for workflow-interoperability on data level and is supported in the grid workflows of the MoSGrid portal. Cloud computing is a model in which users access computational resources and storage facilities from a vendor over Internet, that is, the commercial Amazon Elastic Compute Cloud and Simple Storage Server 39. The user can exploit computers and storage for any task, such as serving websites or running computationally intensive parallel bioinformatics pipelines, being an administrator of its services and paying just for the time of effective usage. By instantiating many virtual resources, a parallel cluster can be deployed on demand, where common libraries such as the Message Passing Interface can be exploited. Also, batch-processing systems can be used to manage the different computations in a queue. Moreover, frameworks for distributed access to files such as Hadoop can be adapted to distributed programming paradigms such as MapReduce 40. The flexibility and the cost-effectiveness provided by cloud computing is extremely appealing for computational biology, in particular for small-medium biotechnology laboratories which need to perform bioinformatics analysis without coping with all the issues of having an in-house information and communication technology infrastructure 41. An intermediate solution is represented by Hybrid Clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones 42. Kiss et al. 43 investigated and analyzed how Windows Azure cloud can be applied for a virtual screening experiment relying on docking simulations, building a framework that exploits the generic worker concept. Kavasidis et al. 44 presented a bioinformatics knowledge discovery tool, BioWizard-C, for extracting and validating implicit associations between biological entities. The aim of the work is to demonstrate how porting a data-intensive application to the Cloud, affects positively its efficiency. Guerrero et al. 45 stated the importance of GPUs in Cloud Computing environments but also highlighted that the efficiency of such infrastructure, with respect to the use of local resources, should be evaluated on the size of the studied problem. In 2007, NVIDIA released the first version of the CUDA 31 programming tools, which enormously facilitated the exploitation of GPU hardware. Afterwards, other possibilities were presented (i.e. OpenCL 32 and OpenACC 46) aiming to provide users a simplified interface to the great computing capabilities offered by present graphics cards. In the field of bioinformatics, a huge number of applications has been ported to CUDA, and depending on the algorithm, the performance can be very competitive when compared with other platforms 47. In general terms, compute-intensive algorithms with simple mathematical operations are the ones that benefit the most from these computational architectures. NVIDIA cards can be installed in workstations quite easily, but they can be also exploited as part of distributed infrastructures such as grid and cloud platforms. Exploiting simple procedures, such as the Peripheral Component Interconnect Passthrough approach, NVIDIA cards can be mounted on virtual machines to provide the user more complete and customizable solutions, whose costs can be very competitive with respect to in-house implementations. Chessa and Pasquale 48 show in another problem of great relevance for biomedicine, how visual coding in the primary visual cortex can be modeled thanks to a well-designed parallel implementation, conveniently tuned to the GPU architecture. D'Agostino et al. 49 report dramatic speedup improvements obtained in local GPU machines for calculation of the molecular surface, problem of great relevance, which appears in many contexts of molecular sciences. Guerrero et al. 50 demonstrate how computational kernels for virtual screening applications can be ported on GPU architectures achieving both interesting energy efficiency and performance figures. We would like to thank the authors for contributing papers on their research on latest advances in Distributed, Parallel, and GPU-accelerated Approaches to Computational Biology for this special issue and all the reviewers for providing constructive reviews and in helping to shape this special issue. Finally, we would like to thank Prof. Geoffrey Fox for providing us an opportunity to bring this special issue to the research community.

In the last decade, different computing paradigms and modelling frameworks for the description and simulation of biochemical systems based on stochastic modelling have been proposed. From a computational point of view, many simulations of the model are necessary to identify the behaviour of the system. The execution of thousands of simulation can require huge amount of time, therefore the parallelization of these algorithms is highly desirable. In this work we discuss the different strategies that can be implemented for the parallelization of a space aware τ-DPP variant, that is proving a C-MPI implementation of the system and discussing its performances according to the simulation of a particle diffusion in a crowded environment.

BACKGROUND: The capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects. RESULTS: We propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a gene-centric database design, (2) on the evaluation of a set of static biomolecular annotations, defined as features, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called SNPranker 2.0 (http://www.itb.cnr.it/snpranker), improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores. CONCLUSIONS: Different databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.

Present advances in sequencing technology make possible to generate large amounts of data in short time. However one major issue is represented by the fact that fragments produced by these high-throughput methods are short and in very high number, resulting in a difficult analytical task. Two different approaches are currently applied to genome assembly, overlap graph and sequence hashing. In particular SSAKE, one of the first and more popular implementation of the sequence hashing algorithm, was designed to leverage the information from short sequence reads by stringently assembling them into contiguous sequences that can be used to characterize novel sequencing targets. In this paper we present an implementation of this tool for the CUDA architecture.

BACKGROUND: Inflammatory bowel diseases (IBD) refer to a group of inflammatory conditions concerning colon and small intestine, which cause socially uncomfortable symptoms and often are associated with an increased risk of colon cancer. IBD are complex disorders, which rely on genetic susceptibility, environmental factors, deregulation of the immune system, and host relationship with commensal microbiota. The complexity of these pathologies makes difficult to clearly understand the mechanisms of their onset. Therefore, the study of IBD must be faced exploiting an integrated and multilevel approach, ranging from genes, transcripts and proteins to pathways altered in affected tissues, and carefully considering their regulatory mechanisms, which may intervene in the pathology onset. It is also crucial to have a knowledge base about the symbiotic bacteria that are hosted in the human gut. To date, much data exist regarding IBD and human commensal bacteria, but this information is sparse in literature and no free resource provides a homogeneously and rationally integrated view of biomolecular data related to these pathologies. METHODS: Human genes altered in IBD have been collected from literature, paying particular interest for the immune system alterations prompted by the interaction with the gut microbiome. This process has been performed manually to assure the reliability of collected data. Heterogeneous metadata from different sources have been automatically formatted and integrated in order to enrich information about these altered genes. A user-friendly web interface has been created for easy access to structured data. Tools such as gene clustering coefficients, all-pairs shortest paths and pathway lengths calculation have been developed to provide data analysis support. Moreover, the implemented resource is compliant to the Galaxy framework, allowing the collected data to be exploited in the context of high throughput bioinformatics analysis. RESULTS: To fill the lack of a reference resource for 'omics' science analysis in the context of IBD, we developed the IBDsite (available at http://www.itb.cnr.it/ibd), a disease-oriented platform, which collects data related to biomolecular mechanisms involved in the IBD onset. The resource provides a section devoted to human genes identified as altered in IBD, which can be queried at different biomolecular levels and visualised in gene-centred report pages. Furthermore, the system presents information related to the gut microbiota involved in IBD affected patients. The IBDsite is compliant with all Galaxy installations (in particular, it can be accessed from our custom version of Galaxy, http://www.itb.cnr.it/galaxy), in order to facilitate high-throughput data integration and to enable evaluations of the genomic basis of these diseases, complementing the tools embedded in the IBDsite. CONCLUSIONS: Lots of sparse data exist concerning IBD studies, but no on-line resource homogeneously and rationally integrate and collect them. The IBDsite is an attempt to group available information regarding human genes and microbial aspects related to IBD, by means of a multilevel mining tool. Moreover, it constitutes a knowledge base to filter, annotate and understand new experimental data in order to formulate new scientific hypotheses, thanks to the possibility of integrating genomics aspects by employing the Galaxy framework. Discussed use-cases demonstrate that the developed system is useful to infer not trivial knowledge from the existing widespread data or from novel experiments.

Investigating ligand-regulated allosteric coupling between protein domains is fundamental to understand cell-life regulation. The Hsp70 family of chaperones represents an example of proteins in which ATP binding and hydrolysis at the Nucleotide Binding Domain (NBD) modulate substrate recognition at the Substrate Binding Domain (SBD). Herein, a comparative analysis of an allosteric (Hsp70-DnaK) and a non-allosteric structural homolog (Hsp110-Sse1) of the Hsp70 family is carried out through molecular dynamics simulations, starting from different conformations and ligand-states. Analysis of ligand-dependent modulation of internal fluctuations and local deformation patterns highlights the structural and dynamical changes occurring at residue level upon ATP-ADP exchange, which are connected to the conformational transition between closed and open structures. By identifying the dynamically responsive protein regions and specific cross-domain hydrogen-bonding patterns that differentiate Hsp70 from Hsp110 as a function of the nucleotide, we propose a molecular mechanism for the allosteric signal propagation of the ATP-encoded conformational signal.

Present advances in sequencing technology make possible to generate large amounts of data in short time. The problem is that fragments produced by these high-throughput methods are much shorter than in traditional Sanger sequencing, and this makes stringent the issue of exploiting an efficient sequence assembly algorithm. While two common approaches are actually applied to genome assembly, overlap graph and sequence hashing, the latter allows aggressive assembling of millions of short fragments with a reasonable memory and computational cost. In particular SSAKE, one of the first and more popular implementation of the sequence hashing algorithm, was designed to leverage the information from short sequence reads by stringently assembling them into contiguous sequences that can be used to characterize novel sequencing targets. In this paper we present a parallel version of this tool that enables a fast, lightweight and scalable solution for modern genome assembly.

miRNA target genes prediction represents a crucial step in miRNAs functional characterization. In this context, the challenging issue remains predictions accuracy and recognition of false positive results. In this article myMIR, a web based system for increasing reliability of miRNAs predicted targets lists, is presented. myMIR implements an integrated pipeline for computing ranked miRNA::target lists and provides annotations for narrowing them down. The system relies on knowledge base data, suitably integrated in order to extend the functional characterization of targeted genes to miRNAs, by highlighting the search on over-represented annotation terms. Validation results show a dramatic reduction in the quantity of predictions and an increase in the sensitivity, when compared to other methods. This improves the predictions accuracy and allows the formulation of novel hypotheses on miRNAs functional involvement.

Emerging technologies for structure matching based on surface descriptions have demonstrated their effectiveness in many research fields. In particular, they can be successfully applied to in silico studies of structural biology. Protein activities, in fact, are related to the external characteristics of these macromolecules and the ability to match surfaces can be important to infer information about their possible functions and interactions. In this work, we present a surface-matching algorithm, based on encoding the outer morphology of proteins in images of local description, which allows us to establish point-to-point correlations among macromolecular surfaces using image-processing functions. Discarding methods relying on biological analysis of atomic structures and expensive computational approaches based on energetic studies, this algorithm can successfully be used for macromolecular recognition by employing local surface features. Results demonstrate that the proposed algorithm can be employed both to identify surface similarities in context of macromolecular functional analysis and to screen possible protein interactions to predict pairing capability.

An emerging application field for structure matching is related to in silico studies of molecular biology: considering that protein function is mainly related to its external morphology, the possibility to match macromolecular surfaces is very important to infer information about the interaction of biological components. In this work we present a parallel algorithm based on images of local description, originally designed for object recognition in robotics, which has been adapted to identify surface complementarities for screening the interaction possibilities of biological macromolecules. The results obtained show the good discrimination power of the proposed approach, coupled with high performance figures.

BACKGROUND: Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block. RESULTS: The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%. CONCLUSIONS: The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique.

An emerging application field for structure matching is related to in silico studies of molecular biology. Considering that protein function is mainly related to its external morphology, the possibility to match macromolecular surfaces is very important to infer information about the possible interaction and function of biological components. Most of the present approaches aim to match complete objects and they exploit a complete 3D representations of them. These aspects do not suit well with the local formulation of the matching issue in the biological application domain. The problem is that the shapes of macromolecules that are experimentally known to dock cannot present high similarities except in small important functional regions. This is the reason why we chose to adapt a well known image based method, originally studied for matching similar structures, for screening possible interactions of couples of macromolecule. In this context the proposed work presents a parallel algorithm able to efficiently decompose high detailed 3D surfaces into a proper set of images to use for the macromolecular screening.

BACKGROUND: The design of mutants in protein functional regions, such as the ligand binding sites, is a powerful approach to recognize the determinants of specific protein activities in cellular pathways. For an exhaustive analysis of selected positions of protein structure large scale mutagenesis techniques are often employed, with laborious and time consuming experimental set-up. 'In silico' mutagenesis and screening simulation represents a valid alternative to laboratory methods to drive the 'in vivo' testing toward more focused objectives. RESULTS: We present here a high performance computational procedure for large-scale mutant modelling and subsequent evaluation of the effect on ligand binding affinity. The mutagenesis was performed with a 'saturation' approach, where all 20 natural amino acids were tested in positions involved in ligand binding sites. Each modelled mutant was subjected to molecular docking simulation and stability evaluation. The simulated protein-ligand complexes were screened for their impairment of binding ability based on change of calculated Ki compared to the wild-type.An example of application to the Endothelial Protein C Receptor residues involved in lipid binding is reported. CONCLUSION: The computational pipeline presented in this work is a useful tool for the design of structurally stable mutants with altered affinity for ligand binding, considerably reducing the number of mutants to be experimentally tested. The saturation mutagenesis procedure does not require previous knowledge of functional role of the residues involved and allows extensive exploration of all possible substitutions and their pairwise combinations. Mutants are screened by docking simulation and stability evaluation followed by a rationally driven selection of those presenting the required characteristics. The method can be employed in molecular recognition studies and as a preliminary approach to select models for experimental testing.

BACKGROUND: The NCBI dbEST currently contains more than eight million human Expressed Sequenced Tags (ESTs). This wide collection represents an important source of information for gene expression studies, provided it can be inspected according to biologically relevant criteria. EST data can be browsed using different dedicated web resources, which allow to investigate library specific gene expression levels and to make comparisons among libraries, highlighting significant differences in gene expression. Nonetheless, no tool is available to examine distributions of quantitative EST collections in Gene Ontology (GO) categories, nor to retrieve information concerning library-dependent EST involvement in metabolic pathways. In this work we present the Human EST Ontology Explorer (HEOE) http://www.itb.cnr.it/ptp/human_est_explorer, a web facility for comparison of expression levels among libraries from several healthy and diseased tissues. RESULTS: The HEOE provides library-dependent statistics on the distribution of sequences in the GO Direct Acyclic Graph (DAG) that can be browsed at each GO hierarchical level. The tool is based on large-scale BLAST annotation of EST sequences. Due to the huge number of input sequences, this BLAST analysis was performed with the aid of grid computing technology, which is particularly suitable to address data parallel task. Relying on the achieved annotation, library-specific distributions of ESTs in the GO Graph were inferred. A pathway-based search interface was also implemented, for a quick evaluation of the representation of libraries in metabolic pathways. EST processing steps were integrated in a semi-automatic procedure that relies on Perl scripts and stores results in a MySQL database. A PHP-based web interface offers the possibility to simultaneously visualize, retrieve and compare data from the different libraries. Statistically significant differences in GO categories among user selected libraries can also be computed. CONCLUSION: The HEOE provides an alternative and complementary way to inspect EST expression levels with respect to approaches currently offered by other resources. Furthermore, BLAST computation on the whole human EST dataset was a suitable test of grid scalability in the context of large-scale bioinformatics analysis. The HEOE currently comprises sequence analysis from 70 non-normalized libraries, representing a comprehensive overview on healthy and unhealthy tissues. As the analysis procedure can be easily applied to other libraries, the number of represented tissues is intended to increase.

Computer vision technologies of structure matching based on surface representation have demonstrated their effectiveness in many research fields. In particular they can be successfully applied to in silico studies of molecular biology. Protein activities, in fact, are driven by their external characteristics, therefore the ability to match surfaces allows one to quickly infer information about possible interactions and functions of biological components.In this work we present a surface matching algorithm which is able to screen possible macromolecular interactions in terms of surface complementarities. The main characteristics of the algorithm is the exploitation of an intermediate level of data representation for 3D surfaces based on images of local description. This approach enables the matching of small pieces of surfaces, which is a crucial aspect when working in the biological context.

The merging between molecular biology and computer science is becoming a key point in knowledge discovery, in particular when huge amount of data coming from different laboratories need to be integrated and analyzed. For example Tissue MicroArray (TMA) is a high throughput technology that allows researchers to analyze several tissue samples in a parallel way. TMA is a good validation method of researcher's hypothesis about pathological tissue morphology and biomarkers evaluation. In this context, the Grid technology is a good opportunity to share data and solve intensive storage and computing problems. However, the exploitation of the Grid is not a trivial pursuit for many users. Nowadays, an important issue is to provide a simplified use of Grid resources, enabling the use of tools that scientists usually employ. In this paper, we present TMAinspect, a Web based system for Tissue Microarray experiment analysis that provides to not expert users a graphical interface to search for TMA images and to efficiently process them in EGEE. For the image elaboration we use PIMA(GE)2 Lib, the Parallel IMAGE processing GEnoa Library.

The grid represents a great opportunity in the scientific community to solve data/compute intensive problems and to share data. Using the grid, different application domains could achieve great advantages; however, the exploitation of the grid is not a trivial pursuit for many users. The key issue is to enable a simplified use of grid resources and tools that scientists usually employ. In this paper, we investigate these topics in bioinformatics community to allow the efficient elaboration on the grid of images obtained through the tissue microarray technique. We present a grid framework for tissue microarray analysis, GF4TMA, that allows the selection of TMA images and their efficient analysis by the exploitation of the grid architecture. In particular, we exploited PIMA(GE)2Lib, the Parallel IMAGE processing GEnoa Library. A critical point was to enable parallel computations on the grid without compromising the efficiency and the user-friendliness of the native library. We tackled this issue by encapsulating the library in a grid service.

Surface characterization of molecules may provide useful information about their functionalities and potential interactions with other molecules. Even if many algorithms and tools for the protein surface modeling were proposed, presently a suitable and efficient system that enables wide range analysis of the protein external morphology does not exists. In this paper we present a parallel workflow for the reconstruction of molecular surfaces based on a set of grid services. The aim is to provide scientists to get easily and quickly an accurate surface representation of the molecules made available in the protein data bank. The system front end is represented by a Java application, that is responsible to manage the computation on the basis of the parameters specified by the user. Furthermore, the application permits to directly visualize the resulting molecular surface and to download it on the user machine. The modular approach we propose allows in perspective the extensibility of the system through the integration of further grid services, in order to perform even more complex surface analyses in the grid environment.

BACKGROUND: The ESTree database (db) is a collection of Prunus persica and Prunus dulcis EST sequences that in its current version encompasses 75,404 sequences from 3 almond and 19 peach libraries. Nine peach genotypes and four peach tissues are represented, from four fruit developmental stages. The aim of this work was to implement the already existing ESTree db by adding new sequences and analysis programs. Particular care was given to the implementation of the web interface, that allows querying each of the database features. RESULTS: A Perl modular pipeline is the backbone of sequence analysis in the ESTree db project. Outputs obtained during the pipeline steps are automatically arrayed into the fields of a MySQL database. Apart from standard clustering and annotation analyses, version VI of the ESTree db encompasses new tools for tandem repeat identification, annotation against genomic Rosaceae sequences, and positioning on the database of oligomer sequences that were used in a peach microarray study. Furthermore, known protein patterns and motifs were identified by comparison to PROSITE. Based on data retrieved from sequence annotation against the UniProtKB database, a script was prepared to track positions of homologous hits on the GO tree and build statistics on the ontologies distribution in GO functional categories. EST mapping data were also integrated in the database. The PHP-based web interface was upgraded and extended. The aim of the authors was to enable querying the database according to all the biological aspects that can be investigated from the analysis of data available in the ESTree db. This is achieved by allowing multiple searches on logical subsets of sequences that represent different biological situations or features. CONCLUSIONS: The version VI of ESTree db offers a broad overview on peach gene expression. Sequence analyses results contained in the database, extensively linked to external related resources, represent a large amount of information that can be queried via the tools offered in the web interface. Flexibility and modularity of the ESTree analysis pipeline and of the web interface allowed the authors to set up similar structures for different datasets, with limited manual intervention.

BACKGROUND: Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information. RESULTS: In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data. CONCLUSIONS: Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes.

UNLABELLED: The GoSh database is a collection of 58 990 Capra hircus and Ovis aries expressed sequence tags. A perl pipeline was prepared to process sequences, and data were collected in a MySQL database. A PHP-based web interface allows browsing and querying the database. Putative single nucleotide polymorphism (SNP) detection, as well as search to repeats were performed, and links to external related resources were provided. Sequences were annotated against three different databases and an algorithm was implemented to create statistics of the distribution of retrieved homologous ontologies in the Gene Ontology categories. The GoSh database is a repository of data and links related to goat and sheep expressed genes. AVAILABILITY: The GoSh database is available at http://www.itb.cnr.it/gosh/

BACKGROUND: This study concerns the development of a high performance workflow that, using grid technology, correlates different kinds of Bioinformatics data, starting from the base pairs of the nucleotide sequence to the exposed residues of the protein surface. The implementation of this workflow is based on the Italian Grid.it project infrastructure, that is a network of several computational resources and storage facilities distributed at different grid sites. METHODS: Workflows are very common in Bioinformatics because they allow to process large quantities of data by delegating the management of resources to the information streaming. Grid technology optimizes the computational load during the different workflow steps, dividing the more expensive tasks into a set of small jobs. RESULTS: Grid technology allows efficient database management, a crucial problem for obtaining good results in Bioinformatics applications. The proposed workflow is implemented to integrate huge amounts of data and the results themselves must be stored into a relational database, which results as the added value to the global knowledge. CONCLUSION: A web interface has been developed to make this technology accessible to grid users. Once the workflow has started, by means of the simplified interface, it is possible to follow all the different steps throughout the data processing. Eventually, when the workflow has been terminated, the different features of the protein, like the amino acids exposed on the protein surface, can be compared with the data present in the output database.