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The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of structured data, coupled with contextual information about their creation, expressed in domain-specific standards and machine readable formats. This paper analyses the potential support to FAIRness of the openEHR e-health standard, by theoretically assessing the compliance with each of the 15 FAIR principles of a hypothetical Clinical Data Repository (CDR) developed according to the openEHR specifications. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant CDRs.

The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of data, coupled with contextual information about their creation, expressed in domain-specific standards and machine-readable formats. This paper analyses the potential support to FAIRness of the openEHR specifications and reference implementation, by theoretically assessing their compliance with each of the 15 FAIR principles. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant Clinical Data Repositories (CDRs).

Telemedicine is the practice of providing health services remotely by means of ICT. It is widely considered as a viable solution for overcoming distance barriers in healthcare delivery. Its applications range from monitoring patients with chronic diseases, to visiting patients at a distance, to obtaining a second opinion or performing remote surgical operations. Real-time telemedicine, in particular, includes all the technologies and methodologies needed when it is essential to support live audio/video interactions between patients and doctors or between doctors. In the past, its adoption has been limited because it required dedicated network infrastructures and expensive hardware stations. Here we present the CRS4 real-time telemedicine system, the prototype we developed to address these issues, and its 10 years’ evolution. By means of low-cost hardware and open-source software, it supports remote real-time ultrasound examinations, performed by a clinician close to the patient and guided by the instructions of a specialist at a distance. The system is suitable for all the diagnostic procedures which require very specialised skills to be performed correctly, also called operator-dependent, and it can be also used for educational purposes. Its clinical effectiveness has been demonstrated in two use cases, paediatric cardiology and point-of-care ultrasonography.

Current high-throughput sequencing technologies allow us to acquire entire genomes in a very short time and at a relatively sustainable cost, thus resulting in an increasing diffusion of genetic test capabilities, in specialized clinical laboratories and research centers. In contrast, it is still limited the impact of genomic information on clinical decisions, as an effective interpretation is a challenging task. From the technological point of view, genomic data are big in size, have a complex granular nature and strongly depend on the computational steps of the generation and processing workflows. This article introduces our work to create the openEHR Genomic Project and the set of genomic information models we developed to catch such complex structure and to preserve data provenance efficiently in a machine-readable format. The models support clinical actionability of data, by improving their quality, fostering interoperability and laying the basis for re-usability.

This paper describes an e-learning platform developed in the context of the European Project CyTest (2014-1-IT01-KA202-002607), dedicated to Cytological Training at European Standard through Telepathology. The main, and novel, feature of our system is the deep integration between virtual microscopy and the training system: images are not simply there to be seen but they are active parts of testing, supporting quantitative measurement of image comprehension, for instance by evaluating the identification of relevant cellular structures by the position of markers put by the student on the image. The solution we developed offers a complete tool for easy creation and interactive access to questions related to images and fully integrates the components of virtual microscopy and teaching, based on state-of-the-art instruments for digital pathology images management, as OMERO, and for training course distribution, as Moodle. The system can be easily extended to support histopathological diagnosis. The software is distributed as Open Source and available on GitHub.

Use of software containers and services in science is a rising trend that is not satisfied by the HPC computing resources often available in research contexts. We propose a method to grow Kubernetes clusters onto transient nodes allocated through the Grid Engine batch workload manager. The method is being used to run a mix of data-intensive service applications and bursty HPC-style workflows on an OpenStack-based Kubernetes deployment, while keeping a homogeneous job management, logging, monitoring, and storage infrastructure. Moreover, it is relatively straightforward to convert the implementation to be compatible with other workload managers.

Background: Telementoring is a real time telemedicine modality that allows a specialist to remotely assist a less expert operator. Videoconferencing is the main means for implementing telementoring applications and has been used in a wide range of clinical disciplines.; Long-distance communication can be affected by misunderstanding and misinterpretation, in particular when dealing with remote guidance. Videoconferencing can be enhanced using Augmented Reality (AR), which enriches video feeds with virtual objects like 3D models, pointers and annotations.; Objective: To evaluate through an exploratory study the impact of AR in remotely assisted ultrasound exams.; Material and Methods: In previous studies we developed a real time telementoring system (CRS4-TELEMED) for allowing a specialist to remotely guide the execution of an echocardiographic exam by an operator. The system manages three streams: the video from an IP cam, showing the exam scene; the sonographic video from the ultrasound machine; the bidirectional audio for allowing communication between the specialist and the operator. The system has been extended with a set of virtual telepointers, superimposed on the sonographic video and the keyboard of the ultrasound machine, in order to allow the remote specialist, which uses an Android tablet, to indicate regions of interests. The local operator wears Android smartglasses (Epson Moverio bt-200) that enable optical see through visualization of pointers.ARToolkit was used for implementing marker based AR.Results: Preliminary tests were conducted at the Pediatric Cardiology department of AOU Brotzu in Cagliari. Doctors and nurses with different skills and experience in ultrasound exams were involved. Participants judged the system positively, considering AR telepointers useful for improving remote assistance. The following issues emerged: optical alignment is suboptimal, some training is required to understand how smartglasses and AR work, smartglasses results uncomfortable, especially if the user wears glasses.Conclusions: AR based telepointers can be used for improving telementoring, allowing a remote specialist to indicate region of interests. Issues like comfort and alignment precision can be overcome with future evolution of smartglasses.Future works include: new pointers for indicating region on the patients and how to use the ultrasound probe; a user friendly in app calibration procedure for increasing AR precision on smartglasses; a field testing for evaluating the system.

Modern sequencing machines produce order of a terabyte of data per day, which need subsequently to go through a complex processing pipeline. The conventional workflow begins with a few independent, shared-memory tools, which communicate by means of intermediate files. Given its lack of robustness and scalability, this approach is ill-suited to exploiting the full potential of sequencing in the context of healthcare, where large-scale, population-wide applications are the norm. In this work we propose the adoption of stream computing to simplify the genomic resequencing pipeline, boosting its perfor­mance and improving its fault-tolerance. We decompose the first steps of the genomic processing in two distinct and specialized modules (preprocessing and alignment) and we loosely compose them via communication through Kafka streams, in order to allow for easy composability and integration in the already-existing YARN-based pipelines. The proposed solution is then experimentally validated on real data and shown to scale almost linearly.

The known challenge of underutilization of data and biological material from biorepositories as potential resources formedical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability—entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)—are likely to address only parts of the problem. In this article,we argue that biologicalmaterial and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks.Wepropose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently., peer-reviewed

Conference poster presented at NETTAB 2016.

The adoption of Big Data technologies can potentially boost the scalability of data-driven biology and health workflows by orders of magnitude. Consider, for instance, that technologies in the Hadoop ecosystem have been successfully used in data-driven industry to scale their processes to levels much larger than any biological- or health-driven work attempted thus far. In this work we demonstrate the scalability of a sequence alignment pipeline based on technologies from the Hadoop ecosystem – namely, Apache Flink and Hadoop MapReduce, both running on the distributed Apache YARN platform. Unlike previous work, our pipeline starts processing directly from the raw BCL data produced by Illumina sequencers. A Flink-based distributed algorithm reconstructs reads from the Illumina BCL data, and then demultiplexes them – analogously to the bcl2fastq2 program provided by Illumina. Subsequently, the BWA-MEM-based distributed aligner from the Seal project is used to perform read mapping on the YARN platform. While the standard programs by Illumina and BWA-MEM are limited to shared-memory parallelism (multi-threading), our solution is completely distributed and can scale across a large number of computing nodes. Results show excellent pipeline scalability, linear in the number of nodes. In addition, this approach automatically benefits from the robustness to hardware failure and transient cluster problems provided by the YARN pipeline, as well as the scalability of the Hadoop Distributed File System. Moreover, this YARN-based approach complements the up-and-coming version 4 of the GATK toolkit, which is based on Spark and therefore can run on YARN. Together, they can be used to form a scalable complete YARN-based variant calling pipeline for Illumina data, which will be further improved with the arrival of distributed in-memory filesystem technology such as Apache Arrow, thus removing the need to write intermediate data to disk.

Data-intensive research depends on tools that manage multi-dimensional, heterogeneous data sets. We have built OME Remote Objects (OMERO), a software platform that enables access to and use of a wide range of biological data. OMERO uses a server-based middleware application to provide a unified interface for images, matrices, and tables. OMERO’s design and flexibility have enabled its use for light microscopy, high content screening, electron microscopy, and even non-image genotype data. OMERO is open source software and available at http://openmicroscopy.org.

A recent clinical trial for adrenoleukodystrophy (ALD) showed the efficacy and safety of lentiviral vector (LV) gene transfer in hematopoietic stem progenitor cells. However, several common insertion sites (CIS) were found in patients' cells, suggesting that LV integrations conferred a selective advantage. We performed high-throughput LV integration site analysis on human hematopoietic stem progenitor cells engrafted in immunodeficient mice and found the same CISs reported in patients with ALD. Strikingly, most CISs in our experimental model and in patients with ALD cluster in megabase-wide chromosomal regions of high LV integration density. Conversely, cancer-triggering integrations at CISs found in tumor cells from γretroviral vector–based clinical trials and oncogene-tagging screenings in mice always target a single gene and are contained in narrow genomic intervals. These findings imply that LV CISs are produced by an integration bias toward specific genomic regions rather than by oncogenic selection.

A systematic study of issues related to suspending, migrating and resuming virtual clusters for data-driven HPC applications is presented. The interest is focused on nontrivial virtual clusters, that is where the running computation is expected to be coordinated and strongly coupled. It is shown that this requires that all cluster level operations, such as start and save, should be performed as synchronously as possible on all nodes, introducing the need of barriers at the virtual cluster computing meta-level. Once a synchronization mechanism is provided, and appropriate transport strategies have been setup, it is possible to suspend, migrate and resume whole virtual clusters composed of ''heavy'' (4 GB RAM, 6 GB disk images) virtual machines in times of the order of few minutes without disrupting parallel computation-albeit of the MapReduce type-running inside them. The approach is intrinsically parallel, and should scale without problems to larger size virtual clusters.

The increasing di usion of data acquisition systems paves the way to traceability based process management and de nition. In the clinical context, IHE formalizes the reference guidelines, periodically enhanced to re ect processes evolution. In this work we describe how we have modeled the phlebotomy process following the IHE references and best-practices to obtain a fully traceable work ow. The work has resulted in two new transactions for the IHE LBL pro le, describing samples containers production and samples collection. The complete work ow has been im- plemented and successfully tested in real clinical environ- ments. The traceability data acquired have then been stud- ied using Process Mining techniques to compare the pro- duction model with idealized work ow and guide further developments.

Objective: Though vagus nerve stimulation (VNS) is an important option in pharmacoresistant epilepsy, its mechanism of action remains unclear. The observation that VNS desynchronised the EEG activity in animals suggested that this mechanism could be involved in VNS antiepileptic effects in humans. Indeed VNS decreases spiking bursts, whereas its effects on the EEG background remain uncertain. The objective of the present study is to investigate how VNS affects local and inter regional syncronization in different frequencies in pharmacoresistent partial epilepsy. Methods: Digital recordings acquired in 11 epileptic subjects 1 year and 1 week before VNS surgery were compared with that obtained 1 month and 1 year after VNS activation. Power spectrum and synchronization were then analyzed and compared with an epileptic group of 10 patients treated with AEDs only and with 9 non-epileptic patients. Results: VNS decreases the synchronization of theta frequencies (P!0.01), whereas it increases gamma power spectrum and synchronization (!0.001 and 0.01, respectively). Conclusions: The reduction of theta frequencies and the increase in power spectrum and synchronization of gamma bands can be related to VNS anticonvulsant mechanism. In addition, gamma modulation could also play a seizure-independent role in improving attentional performances. Significance: These results suggest that some antiepileptic mechanisms affected by VNS can be modulated by or be the reflection of EEG changes.

In this paper, we present a textural features analysis applied to a medical image segmentation problem where other methods fail, i.e. the localization of thrombotic tissue in the aorta. This problem is extremely relevant because many clinical applications are being developed for the computer assisted, image driven planning of vascular intervention, but standard segmentation techniques based on edges or gray level thresholding are not able to differentiate thrombus from surrounding tissues like vena, pancreas having similar HU average and noisy patterns. Our work consisted in a deep analysis of the texture segmentation approaches used for CT scans, and on experimental tests performed to find out textural features that better discriminate between thrombus and other tissues. Finding that Run Length codes are those performing better both in literature and experiments, we tried to understand the reason of their success suggesting a revision of this approach with feature selection and the use of specifically thresholded Run Lengths that improve the discriminative power of measures reducing the computational cost.

The recent diffusion of web technologies in medical environments is deeply changing the work of radiologists and surgeons, allowing them to store and retrieve patient information, images but also reports and elaborated data. Among elaborated data, 3D reconstructions are becoming more popular because they allow, for example, a precise evaluation of the geometry of organs thus making the measurements necessary for surgical planning easier and more accurate. In this paper, we describe the architecture of a service for 3D model quantitative evaluation, specialized to the analysis of Abdominal Aortic Aneurysms. It is based on a centralized server that stores model images and measurement information and exchanges data with clients using web standards like XML, DICOM3 and VRML97. This allows the secure exchange of heterogeneous data (images, 3D models and reports) that is necessary for complex applications. Furthermore, this arrangement ensures that each step of the complex medical and IT-related procedure is performed by the most trained and expert personnel available, even if he/she is not at the end user location. Easy 3D model measurement and analysis is ensured by using VRML97 and External Authoring Interface [EAI Working Group, “Web site and mailing list” http://www.vrml.org/WorkingGroups/vrml-eai ]; standardization in patient data and report management is ensured using DICOM3 and XML.

In this paper a novel framework for the segmentation, 3D reconstruction and web distribution of vessel structures specifically tailored to the assessment of abdominal aortic aneurysms for endovascular surgery planning is presented. Deformable models are used for segmentation, while VRML97 and ECMA scripting are used to obtain models that are not only viewable from any VRML97 enabled browser, but that also allow users to perform, directly from standard web browsers, guided measurements of geometrical parameters, relevant to surgical planning.

HL7 version 2 is the most popular messaging standard for clinical systems interoperability. Most of the tools for messaging management are Java or .NET based, while Python programming language lacks of comparable solutions. This paper describes HL7apy, an open-source HL7 v2 compliant messaging library, written in Python. The library offers means to create, parse, navigate and validate messages. As an example application, we present a full implementation of the IHE Patient Demographics Query ITI-21 transaction. The resulting module has been integrated in GNU Health, a popular open-source Hospital Information System.

A training system for simulating temporal bone surgery is presented. The system is based on patient-specific volumetric object models derived from 3D CT and MR imaging data. Real-time feedback is provided to the trainees via real-time volume rendering and haptic feedback. The performance constraints dictated by the human perceptual system are met by exploiting parallelism via a decoupled simulation approach on a multi-processor PC platform. In this paper, system components are detailed and the current state of the integrated system is presented.

With vast increases in biological data generation, mechanisms for data storage and analysis have become limiting. A data structure, semantically typed data hypercubes (SDCubes), that combines hierarchical data format version 5 (HDF5) and extensible markup language (XML) file formats, now permits the flexible storage, annotation and retrieval of large and heterogenous datasets.

In this work we describe pyEHR, a new toolkit for building scalable clinical/phenotypic data management systems for biomedical research applications. The toolkit uses openEHR formalisms to guarantee the decoupling of clinical data descriptions from implementation details, and NoSQL technologies, or next-generation SQL ones, to provide scalable storage back-ends.

In this work we present a strategy to integrate Hadoop-based applications into the Galaxy platform along with an extensible implementation of this adapter and related utilities. The strategy is based on the idea of introducing a new Galaxy datatype that provides a layer of indirection, thus relaxing the requirement to place data on a Galaxy-accessible file system and instead allowing the referenced data to be placed on any addressable space, including the Hadoop Distributed File System or Amazon S3. The adapter supports using Hadoop-based applications as part of Galaxy workflows. We demonstrate a practical application where this Hadoop-Galaxy adapter was used at CRS4 to accelerate the bioinformatics analysis of viral vector integration sites through the introduction of Hadoop-based computation components, while keeping the workflow under control of biologists with little specific technical training.

The analysis of the genomic distribution of viral vector genomic integration sites is a key step in hematopoietic stem cell-based gene therapy applications, allowing to assess both the safety and the efficacy of the treatment and to study the basic aspects of hematopoiesis and stem cell biology. Identifying vector integration sites requires ad-hoc bioinformatics tools with stringent requirements in terms of computational efficiency, flexibility, and usability. We developed VISPA (Vector Integration Site Parallel Analysis), a pipeline for automated integration site identification and annotation based on a distributed environment with a simple Galaxy web interface. VISPA was successfully used for the bioinformatics analysis of the follow-up of two lentiviral vector-based hematopoietic stem-cell gene therapy clinical trials. Our pipeline provides a reliable and efficient tool to assess the safety and efficacy of integrating vectors in clinical settings. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0067-5) contains supplementary material, which is available to authorized users.

The number of domains affected by the big data phenomenon is constantly increasing, both in science and industry, with high-throughput DNA sequencers being among the most massive data producers. Building analysis frameworks that can keep up with such a high production rate, however, is only part of the problem: current challenges include dealing with articulated data repositories where objects are connected by multiple relationships, managing complex processing pipelines where each step depends on a large number of configuration parameters and ensuring reproducibility, error control and usability by non-technical staff. Here we describe an automated infrastructure built to address the above issues in the context of the analysis of the data produced by the CRS4 next-generation sequencing facility. The system integrates open source tools, either written by us or publicly available, into a framework that can handle the whole data transformation process, from raw sequencer output to primary analysis results.

We tested a novel approach for the quantitative remote analysis of abdominal aortic aneurysm by reconstructing their 3D geometry and topological structure (i.e. centerline path) and put them on the web as VRML97 models including specialized code enabling the user to perform guided inspection and measurements useful for surgical planning.

Virtualization has become a common abstraction layer in modern data centers, enabling resource owners to manage complex infrastructure independently of their applications. Conjointly, virtualization is becoming a driving technology for a manifold of industry grade IT services. The cloud concept includes the notion of a separation between resource owners and users, adding services such as hosted application frameworks and queueing. Utilizing the same infrastructure, clouds carry significant potential for use in high-performance scientific computing. The ability of clouds to provide for requests and releases of vast computing resources dynamically and close to the marginal cost of providing the services is unprecedented in the history of scientific and commercial computing.

Genotype measurement is a key step in genome-wide association studies -- those studies that aim to uncover the underlying genetic causes of physical traits, including disease. The leading technology for measuring genotypes is the SNP microarray, where hundreds of thousands of genetic variants are interrogated simultaneously. For some of the most commonly used high-throughput genotyping technologies, the conversion from raw measured data to genotype calls (i.e., identifying the specific genomic variants) requires the concurrent analysis of many samples, with the quality of the results crucially depending on the size of the batch. However, current software for microarray analysis is characterized by poor scalability with respect to input batch sizes. In large-scale studies, this limits the ability to harness the large number of samples available to improve the accuracy of genotype calling. Here, we present a scalable MapReduce application that offers both greater scalability and flexibility than the current state-of-the-art. The software can process datasets as large as 7000 samples in a day, it is more than one order of magnitude faster than previous solutions, and it is currently used in production.

Virtualization has become a common abstraction layer in modern data centers, enabling resource owners to manage complex infrastructure independently of their applications. Conjointly virtualization is becoming a driving technology for a manifold of industry grade IT services. The cloud concept includes the notion of a separation between resource owners and users, adding services such as hosted application frameworks and queuing. Utilizing the same infrastructure, clouds carry significant potential for use in high-performance scientific computing. The ability of clouds to provide for requests and releases of vast computing resource dynamically and close to the marginal cost of providing the services is unprecedented in the history of scientific and commercial computing. Distributed computing concepts that leverage federated resource access are popular within the grid community, but have not seen previously desired deployed levels so far. Also, many of the scientific datacenters have not adopted virtualization or cloud concepts yet. This workshop aims to bring together industrial providers with the scientific community in order to foster discussion, collaboration and mutual exchange of knowledge and experience. This year's workshop featured 9 papers on diverse topics in HPC virtualization. Papers of note include Kim et al. proposing group-based cloud memory deduplication along with Nanos et al. presenting results from a high-performance cluster interconnect prototype for VMs with a user-level RDMA protocol over standard 10Gbps Ethernet. The chairs would like to thank the Euro-Par organizers and the members of the program committee along with the speakers and attendees, whose interaction contributed to a stimulating environment. VHPC is planning to continue the successful co-location with Euro-Par in 2012.

We introduce a new numerical method, called "SURFER," for the simulation of two- and three-dimensional flows with several fluid phases and free interfaces between them. We consider incompressible fluids obeying the Navier-Stokes equation with Newtonian viscosity in the bulk of each phase. Capillary forces are taken into account even when interfaces merge or break up. Fluid interfaces are advanced in time using an exactly volume conserving variant of the volume of fluid algorithm, thus allowing for full symmetry between fluid phases. The Navier-Stokes equation is solved using staggered finite differences on a MAC grid and a split-explicit time differencing scheme, while incompressibility is enforced using an iterative multigrid Poisson solver. Capillary effects are represented as a stress tensor computed from gradients of the volume fraction function. This formulation is completely independent of the topology of interfaces and relatively easy to implement in 3D. It also allows exact momentum conservation in the discretized algorithm. Numerical spurious effects or "parasite currents" are noticed and compared to similar effects in Boltzmann lattice gas methods for immiscible fluids. Simulations of droplets pairs colliding in 2D and in 3D are shown. Interface reconnection is performed easily, despite the large value of capillary forces during reconnection.

Oncogenesis induced by insertional mutagenesis with gene therapy vectors occurs mainly by activation of proto-oncogenes found at or nearby the insertion site. This activation often occurs by an enhancer-mediated mechanism or by a process of splicing capture which generates chimeric transcripts comprising portions of vector and cellular mRNAs. Although the activation of oncogenes may be reduced by the use of self-inactivating design and moderate cellular promoters, how to reduce genotoxic splicing capture events and aberrant transcript formation triggered by vector integration is still unclear. We developed a modified Linear Amplification-Mediated (LAM) PCR technique, named cDNA LAM PCR (cLAM-PCR), aimed at retrieving, from the whole transcriptome of LV-transduced cells aberrantly spliced mRNAs that contain lentiviral vector (LV) sequences fused with cellular transcripts in a high-throughput fashion. The sequences of cLAM-PCR products were obtained by 454 pyrosequencing and analyzed by dedicated high-throughput computational pipeline running in a computer cluster that use a dynamic analysis process composed by different steps based on a map-reduce parallelization model. Thus, chimeric LV-genome sequences are recognized, the nucleotide position of the fused sequence is identified (the splice site), and the remaining portion mapped on the appropriate genome assembly by BLAST. We identified several established and previously unknown splice sites within the LV backbone that participate in the aberrant splicing process with variable efficiency. Results obtained with different LV designs show that integrated LVs can perturb the processing of cellular transcripts by interacting with the cellular splicing machinery and fusing with its own splice sites to cellular splice sites both upstream and downstream the integration site. So far, 70 different fusion transcripts could be identified in total, 84% of which were fused to known splice sites of gene exons, 6% were fused to uncharacterized cryptic splice sites located in introns and the remaining 10% were fused to genomic sequences not corresponding to any annotated gene. This analysis allows identifying also several different slice sites within the LV backbone that participated to the aberrabt splicing process. Quantitative PCR on different LV portions within the LV backbone allow measuring the relative contribution to the aberrant splicing process of each splice site identified. Interestingly, the amount of transcription occurring in regions outside the expression cassette reaches the 3% of the entire transgene expression. The cLAM-PCR technique, coupled to high-throughput sequencing and the computational power of our specialized data analysis pipeline allows gaining insights into the biology of vector-mediated splicing alteration. Since this process could induce neoplastic transformation by the generation of aberrant oncogenic protein, its in-depth characterization is instrumental in the development of next-generation LV with a higher safety profile.

Modern DNA sequencing machines have opened the flood gates of whole genome data; and the current processing techniques are being washed away. Medium-sized sequencing laboratories can produce 4-5 TB of data per week that need to be post-processed. Unfortunately, they are often still using ad hoc scripts and shared storage volumes to handle the data, resulting in low scalability and reliability problems. We present a MapReduce workflow that harnesses Hadoop to post-process the data produced by deep sequencing machines. The workflow takes the output of the sequencing machines, performs short read mapping with a novel parallel version of the popular BWA aligner, and removes duplicate reads---two thirds of the entire processing workflow. Our experiments show that it provides a scalable solution with a significantly improved throughput over its predecessor. It also greatly reduces the amount of operator attention necessary to run the analyses thanks to the robust platform that Hadoop provides. The workflow is going into production use at the CRS4 Sequencing and Genotyping Platform.

Results of a numerical study of the dynamics of a collection of disks colliding inelastically in a periodic two-dimensional enclosure are presented. The properties of this system, which is perhaps the simplest model for rapidly flowing granular materials, are markedly different from those known for atomic or moleclar gases, in which collisions are of elastic nature. The most prominent feature characterizing granular systems, even in the idealized situation in which no external forcing exists and the initial condition is statistically homogeneous, is their inherent instability to inhomogeneous fluctuations. Granular “gases” are thus generically nonuniform, a fact that suggests extreme caution in pursuing direct analogies with molecular gases. We find that once an inhomogeneous state sets in, the velocity distribution functions differ from the classical Maxwell-Boltzmann distribution. Other characteristics of the system are different from their counterparts in molecular systems as well. For a given value of the coefficient of restitution,e, a granular system forms clusters of typical separationL0≈l/(1-e2)1/2, wherel is the mean free path in the corresponding homogeneous system. Most of the fluctuating kinetic energy then resides in the relatively dilute regions that surround the clusters. Systems whose linear dimensions are less thenL0 do not give rise to clusters; still they are inhomogeneous, the scale of the corresponding inhomogeneity being the longest wavelength allowed by the system's size. The present article is devoted to a detailed numerical study of the above-mentioned clustering phenomenon in two dimensions and in the absence of external forcing. A theoretical framework explaining this phenomenon is outlined. Some general implications as well as practical ramifications are discussed.

Summary: BioBlend.objects is a new component of the BioBlend package, adding an object-oriented interface for the Galaxy REST-based application programming interface. It improves support for metacomputing on Galaxy entities by providing higher-level functionality and allowing users to more easily create programs to explore, query and create Galaxy datasets and workflows. Availability and implementation: BioBlend.objects is available online at https://github.com/afgane/bioblend . The new object-oriented API is implemented by the galaxy/objects subpackage. Contact: simone.leo@crs4.it

Summary: End-to-end next-generation sequencing microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult owing to a lack of interoperability, reproducibility and transparency. To overcome these limitations we present Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for next-generation sequencing microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics. Availability and implementation: Orione is available online at http://orione.crs4.it . Contact: gianmauro.cuccuru@crs4.it Supplementary information: Supplementary data are available at Bioinformatics online.

High Performance Computational Clusters are, in general, rather rigid objects that present to their user a limited number of degrees of freedom related, usually, only to the specification of the resources requested and to the selection of specific applications and libraries. While in standard production environments this is reasonable and actually desirable, it can become an hindrance when one needs a dynamic and flexible computational environment, for instance for experiments and evaluation, where very different computational approaches, e.g., map-reduce, standard parallel jobs and virtual HPC clusters need to coexist on the same physical facility. In this paper we will present our efforts to address some of these challenges while maintaining a unified cluster management environment.

MapReduce has become increasingly popular as a simple and efficient paradigm for large-scale data processing. One of the main reasons for its popularity is the availability of a production-level open source implementation, Hadoop, written in Java. There is considerable interest, however, in tools that enable Python programmers to access the framework, due to the language's high popularity. Here we present a Python package that provides an API for both the MapReduce and the distributed file system sections of Hadoop, and show its advantages with respect to the other available solutions for Hadoop Python programming, Jython and Hadoop Streaming.

The rapid growth in the throughput to cost ratio of experimental data production technologies is generating vast amounts of scientific data, often organized into "large" objects (genomes, bio-images) exhibiting complex internal structures. Frequently, datasets must be shared between multiple research groups interested not only in the final results, but also in how they are produced. The practical difficulties of moving terabytes or more of data across the network, as well as the need to maintain a clear separation between software stack and storage infrastructure, are thus raising interest in the use of virtual clusters for HPC and data intensive applications. In this paper we employ a MapReduce implementation of an image analysis pipeline used by deep sequencing platforms to analyse different virtual cluster scenarios and their impact on system performance.

For a large class of scientific data analysis applications it is becoming important, due to the sheer size of datasets, to have the option to perform the analysis directly where the data are stored, rather than on remote computational clusters. A possible strategy is the use of virtual clusters, thus guaranteeing a high degree of isolation from the underlying physical computational structure, and a very compact initial description. Deploying, saving and restoring HPC dedicated virtual clusters introduces, however, a different class of requirements on the virtual machines managing infrastructure, in particular for what concerns storage I/O requirements, whose scalability boundaries are easily reached. Here we discuss an alternative approach based on a storage model that leverages the WORM (write once, read many) character of the data used by VM management to increase, in a scalable way, the aggregate data bandwidth available to virtual cluster level operations and provide preliminary results indicating that it is a viable solution.

Bioinformatics applications currently require both processing of huge amounts of data and heavy computation. Fulfilling these requirements calls for simple ways to implement parallel computing. MapReduce is a general-purpose parallelization model that seems particularly well-suited to this task and for which an open source implementation (Hadoop) is available. Here we report on its application to three relevant algorithms: BLAST, GSEA and GRAMMAR. The first is characterized by relatively low-weight computation on large data sets, while the second requires heavy processing of relatively small data sets. The third one can be considered as containing a mixture of these two computational flavors. Our results are encouraging and indicate that the framework could have a wide range of bioinformatics applications while maintaining good computational efficiency, scalability and ease of maintenance.

To demonstrate a simple low-cost system for tele-echocardiology, focused on paediatric cardiology applications. The system was realized using open-source software and COTS technologies. It is based on the transmission of two simultaneous video streams, obtained by direct digitization of the output of an ultrasound machine and by a netcam showing the examination that is taking place. These streams are then embedded into a web page so they are accessible, together with basic video controls, via a standard web browser. The system can also record video streams on a server for further use. The system was tested on a small group of neonatal cases with suspected cardiopathies for a preliminary assessment of its features and diagnostic capabilities. Both the clinical and technological results were encouraging and are leading the way for further experimentation. The presented system can transfer clinical images and videos in an efficient way and in real time. It can be used in the same hospital to support internal consultancy requests, in remote areas using Internet connections and for didactic purposes using low cost COTS appliances and simple interfaces for end users. The solution proposed can be extended to control different medical appliances in those remote hospitals.

A Monte Carlo scheme for the search of extensive conserved quantities in lattice gas automata models is described. It is based on an approximation to the microscopic dynamics and it amounts to estimating the dimension of the eigenspace with eigenvalue 1 of a linear operator related to the lattice gas automata model evolution operator linearized around equilibrium distributions. The applicability of this technique is limited to models with collision rules satisfying semi-detailed balance.

Iterative image reconstruction algorithms for positron emission tomography (PET) require a sophisticated system matrix (model) of the scanner. Our aim is to set up such a model offline for the YAP-(S)PET II small animal imaging tomograph in order to use it subsequently with standard ML-EM (maximum-likelihood expectation maximization) and OSEM (ordered subset expectation maximization) for fully three-dimensional image reconstruction. In general, the system model can be obtained analytically, via measurements or via Monte Carlo simulations. In this paper, we present the multi-ray method, which can be considered as a hybrid method to set up the system model offline. It incorporates accurate analytical (geometric) considerations as well as crystal depth and crystal scatter effects. At the same time, it has the potential to model seamlessly other physical aspects such as the positron range. The proposed method is based on multiple rays which are traced from/to the detector crystals through the image volume. Such a ray-tracing approach itself is not new; however, we derive a novel mathematical formulation of the approach and investigate the positioning of the integration (ray-end) points. First, we study single system matrix entries and show that the positioning and weighting of the ray-end points according to Gaussian integration give better results compared to equally spaced integration points (trapezoidal integration), especially if only a small number of integration points (rays) are used. Additionally, we show that, for a given variance of the single matrix entries, the number of rays (events) required to calculate the whole matrix is a factor of 20 larger when using a pure Monte-Carlo-based method. Finally, we analyse the quality of the model by reconstructing phantom data from the YAP-(S)PET II scanner.

Our work focuses on the development of interactive multi-user holographic displays that allow freely moving naked eye participants to share a three dimensional scene with fully continuous, observer independent, parallax. Our approach is based on a scalable design that exploits a specially arranged array of projectors and a holographic screen. The feasibility of such an approach has already been demonstrated with a working hardware and software 7.4M pixel prototype driven at 10-15Hz by two DVI streams. In this short contribution, we illustrate our progress, presenting a 50M pixel display prototype driven by a dedicated cluster hosting multiple consumer level graphic cards.

We present an interactive multi-user holographic environment that allows freely moving naked eye participants to share a large 3D scene with fully continuous, observer independent, parallax.

The CRS4 experimental bone-burr simulator implements visual and haptic effects through the incorporation of a physics-based contact model and patient-specific data. Psychophysical tests demonstrate that, despite its simplified model and its inherent technological constraints, the simulator can articulate material differences, and that its users can learn to associate virtual bone with real bone material. Tests addressed both surface probing and interior drilling task. We also explore a haptic contrast sensitivity function based on the model's two main parameters: an elastic constant and an erosion factor. Both parameters manifest power-law-like sensitivity with respective exponents of around two and three. Further tests may reveal how well simulator users perceive fine differences in bone material, like those encountered while drilling through real volume boundaries.