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The reconstruction of the two distinct copies of each chromosome, called haplotypes, is an essential process for the characterization of the genome of an individual. Here we address a successful approach for haplotype assembly, called the weighted Minimum Error Correction (wMEC) problem, which consists in computing the two haplotypes that partition the sequencing reads into two disjoint sub-sets with the least number of corrections to the Single Nucleotide Polymorphisms values. To solve this problem we propose GenHap, a computational method based on Genetic Algorithms, which are able to obtain optimal solutions thanks to a global search process. To evaluate the effectiveness of GenHap, we test it on a synthetic (yet realistic) dataset based on the PacBio RS II sequencing technology. We compare the performance of GenHap against HapCol, an efficient state-of-the-art algorithm for haplotype assembly. We show that GenHap always obtains high accuracy solutions (in terms of haplotype error rate), and is up to 20× faster than HapCol on this synthetic (yet realistic) dataset.

Summary The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker. An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.

Endothelial protein C receptor (EPCR) is a CD1-like transmembrane glycoprotein with important regulatory roles in protein C (PC) pathway, enhancing PC's anticoagulant, anti-inflammatory, and antiapoptotic activities. Similarly to homologous CD1d, EPCR binds a phospholipid [phosphatidylethanolamine (PTY)] in a groove corresponding to the antigen-presenting site, although it is not clear if lipid exchange can occur in EPCR as in CD1d. The presence of PTY seems essential for PC gamma-carboxyglutamic acid (Gla) domain binding. However, the lipid-free form of the EPCR has not been characterized. We have investigated the structural role of PTY on EPCR, by multiple molecular dynamics (MD) simulations of ligand bound and unbound forms of the protein. Structural changes, subsequent to ligand removal, led to identification of two stable and folded ligand-free conformations. Compared with the bound form, unbound structures showed a narrowing of the A' pocket and a high flexibility of the helices around it, in agreement with CD1d simulation. Thus, a lipid exchange with a mechanism similar to CD1d is proposed. In addition, unbound conformations presented a reduced interaction surface for Gla domain, confirming the role of PTY in establishing the proper EPCR conformation for the interaction with its partner protein. Single MD simulations were also obtained for 29 mutant models with predicted structural stability and impaired binding ability. Ligand affinity calculations, based on linear interaction energy method, showed that substitution-induced conformational changes affecting helices around the A' pocket were associated to a reduced binding affinity. Mutants responsible for this effect may represent useful reagents for experimental tests.

Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an experiment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better estimation of systems biology models.

In order to share interesting data located in various institutions, it may be necessary to ensure secure accesses and privacy preservation. For example, it is the case of biomedical images, since they are related to confidential information corresponding to real patients. This paper describes an approach to enable the use of PIMA(GE)(2) Lib, Parallel IMAGE processing GEnoa Library, in the the analysis of distributed medical images complying with privacy preservation policy imposed by the owner institutions. To achieve this goal, a set of specialized I/O functions was developed to enable secure remote data access and elaboration. exploiting the Grid infrastructure and without copy data from their original position. The work considers the use of the EGEE infrastructure and the GFAL API. The approach was experimented in the analysis of images obtained through the Tissue MicroArray technique; the tests gave positive results also in the execution time.

Surface characterization of peptides may provide useful information about functionality and potential interactions with other molecules. A description of a protein site through a surface that models the shape conferred by the exposed residues is an effective tool for the analysis and the modelling of proteins that may highlight similarities and relationships not detectable through comparisons at level of primary, secondary and tertiary structure. This study concerns the development of a tool that extracts the residues that concur to the shape modelling of the surface of a protein or a portion of it. This task is accomplished without taking into account the order of the amino acids in the primary structure, but only according to the selection of a portion of the protein indicated through geometric parameters or an explicit list of amino acids belonging to the site of interest. Both in the case of an entire protein and in the case of a portion of it, the methods provides the mesh that models the surface described by the exposed residues that constitute the external envelope. The developed tool which allows the extraction of the exposed residues, and thus of the potential function determinants, is applied to identify the amino acids that concur to the structural interaction in several protein complexes.

BLAST is probably the most used application in bioinformatics teams. BLAST complexity tends to be a concern when the query sequence sets and reference databases are large. Here we present BGBlast: an approach for handling the computational complexity of large BLAST executions by porting BLAST to the Grid platform, leveraging the power of the thousands of CPUs which compose the EGEE infrastructure. BGBlast provides innovative features for efficiently managing BLAST databases in the distributed Grid environment. The system (1) keeps the databases constantly up to date while still allowing the user to regress to earlier versions, (2) stores the older versions of databases on the Grid with a time and space efficient delta encoding and (3) manages the number of replicas for each database over the Grid with an adaptive algorithm, dynamically balancing between execution parallelism and storage costs.

The Tissue MicroArray (TMA) technique is assuming even more importance. Digital images acquisition becomes fundamental to provide an automatic system for Subsequent analysis. The accuracy of the results depends on the image resolution, which has to be very high in order to provide as many details as possible. Lossless formats are more suitable to bring information, but data file size become a critical factor researchers have to deal with. This affects not only storage methods but also computing times and performances. Pathologists and researchers who work with biological tissues, in particular with the TMA technique, need to consider a large number of case studies to formulate and validate their hypotheses. It is clear the importance of image sharing between different institutes worldwide to increase the amount of interesting data to work with. In this context, preserving the security of sensitive data is a fundamental issue. In most of the cases copying patient data in places different from the original database is forbidden by the owner institutes. Storage, computing and security are key problems of TMA methodology. In our system we tackle all these aspects using the EGEE (Enabling Grids for E-sciencE) Grid infrastructure. The Grid platform provides good storage, performance in image processing and safety of sensitive patient information: this architecture offers hundreds of Storage and Computing Elements and enables users to handle images without copying them to physical disks other than where they have been archived by the owner giving back to end-users only the processed anonymous images. The efficiency of the TMA analysis process is obtained implementing algorithms based on functions provided by the Parallel IMAge processing Genoa Library (PIMA(GE)(2) Lib). The acquisition of remotely distributed TMA images is made using specialized I/O functions based on the Grid File Access Library (GFAL) API. In our opinion this approach may represent important contribution to tele-pathology development.

In this paper we present SysBio-Gateway, a framework of standard solution for data integration in bioinformatics and Systems Biology. In the context of several research projects, we developed a set of databases and related web interfaces to cover many levels of the biological system complexity. Furthermore several analysis tools have been developed and integrated in the web resources in order to cope with problems of data integration and mining in a systems perspective. The SysBio-Gateway, freely accessible at the URL http://www.itb.cnr.it/sysbio-gateway, offers access to all the presented resources, which concerns different levels of organization of biological living systems, from genes to organs, passing through proteins, protein families, cellular processes, tissues, and pathologies.

The interferon-induced transmembrane protein 3 (IFITM3) inhibits lentiviral gene therapy vector entry into hematopoietic stem cells and can be overcome by Cyclosporine H (CsH), but underlying mechanisms remain unclear. Here, we show that mutating the evolutionarily conserved lysines of the IFITM3 intracellular loop abolishes its antiviral activity without affecting either its localization or its degradation by CsH through non-canonical lysosomal pathways. When confined to the plasma membrane, the lysine-competent IFITM3 lost restriction against VSV-G pseudotyped viral vectors but gained antiviral activity against vectors that fuse directly at the plasma membrane. Interestingly, altering the lysines did not alter IFITM3 homodimerization but impacted higher-order protein complex formation, suggesting loss of interaction with cellular co-factors. In agreement, IFITM3 expression was not sufficient to restrict viral vectors in myeloid K562 cells as opposed to promonocytic THP1 or primary HSC. We exclude the involvement of previously identified factors affecting IFITM3 biology and propose a novel model for IFITM3 restriction that depends on the presence of cellular co-factor(s) that may interact with IFITM3 through the intracellular loop lysine residues. Overall, our work provides significant insight into the mechanisms of action of IFITM3 and CsH that can be exploited for improved gene therapies and broadly acting antiviral strategies.

Cell cycle is one of the biological processes that has been investigated the most in the recent years, this due to its importance in cancer studies and drug discovery. The complexity of this biological process is revealed every time a mathematical simulation of the processes is carried out. We propose an automated approach that mathematically simulates the cell cycle process with the aim to describe the best estimation of the model. We have implemented a system that starting from a cell cycle model is capable of retrieving from a specific database, called Cell Cycle Database, the necessary mathematical information to perform simulation using a grid approach and identify the best model related to a specific dataset of experimental results from the real biological system. Our system allows the visualization of mathematical expressions, such as the kinetic rate law of a reaction, and the direct simulation of the models with the aim to give the user the possibility to interact with the simulation system. The parameter estimation process usually implies time-consuming computations due to algorithms of linear regression and stochastic methods. In particular, in the case of a stochastic approach based on evolutionary algorithms, the iterative selection process implies many different computations. Therefore, a large number of ODE system simulations are required: the grid infrastructure allows to distribute and obtain the best model that fits the experimental data. The computation of many ODE systems can be distributed on different grid nodes so that the execution time for the estimation of the best model is reduced. This system will be useful for the comparison of models with different initial conditions related to normal and de-regulated cell cycles.