Virtual Environment for Next Generation Sequencing Analysis

Next Generation Sequencing technology, on the one hand, allows a more accurate analysis, and, on the other hand, increases the amount of data to process. A new protocol for sequencing the messenger RNA in a cell, known as RNA- Seq, generates millions of short sequence fragments in a single run. These fragments, or reads, can be used to measure levels of gene expression and to identify novel splice variants of genes. The proposed solution is a distributed architecture consisting of a Grid Environment and a Virtual Grid Environment, in order to reduce processing time by making the system scalable and flexibl

A Cloud Infrastructure for Optimization of a Massive Parallel Sequencing Workflow

Massive Parallel Sequencing is a term used to describe several revolutionary approaches to DNA sequencing, the so-called Next Generation Sequencing technologies. These technologies generate millions of short sequence fragments in a single run and can be used to measure levels of gene expression and to identify novel splice variants of genes allowing more accurate analysis. The proposed solution provides novelty on two fields, firstly an optimization of the read mapping algorithm has been designed, in order to parallelize processes, secondly an implementation of an architecture that consists of a Grid platform, composed of physical nodes, a Virtual platform, composed of virtual nodes set up on demand, and a scheduler that allows to integrate the two platform

Optimizing Splicing Junction Detection in Next Generation Sequencing Data on a Virtual-GRID Infrastructure

The new protocol for sequencing the messenger RNA in a cell, named RNA-seq produce millions of short sequence fragments. Next Generation Sequencing technology allows more accurate analysis but increase needs in term of computational resources. This paper describes the optimization of a RNA-seq analysis pipeline devoted to splicing variants detection, aimed at reducing computation time and providing a multi-user/multisample environment. This work brings two main contributions. First, we optimized a well-known algorithm called TopHat by parallelizing some sequential mapping steps. Second, we designed and implemented a hybrid virtual GRID infrastructure allowing to efficiently execute multiple instances of TopHat running on different samples or on behalf of different users, thus optimizing the overall execution time and enabling a flexible multi-user environmen

Top-down profiling methodology and new Partition and Placement tool to improve Spiking Neural Network simulations on a massively many-core neuromorphic platform

none4nomixedURGESE, GIANVITO; BARCHI, FRANCESCO; MACII, Enrico; ACQUAVIVA, ANDREAURGESE, GIANVITO; BARCHI, FRANCESCO; MACII, Enrico; ACQUAVIVA, ANDRE

Optimizing Splicing Junction Detection in Next Generation Sequencing Data on a Virtual-GRID Infrastructure

The new protocol for sequencing the messenger RNA in a cell, named RNA-seq produce millions of short sequence fragments. Next Generation Sequencing technology allows more accurate analysis but increase needs in term of computational resources. This paper describes the optimization of a RNA-seq analysis pipeline devoted to splicing variants detection, aimed at reducing computation time and providing a multi-user/multisample environment. This work brings two main contributions. First, we optimized a well-known algorithm called TopHat by parallelizing some sequential mapping steps. Second, we designed and implemented a hybrid virtual GRID infrastructure allowing to efficiently execute multiple instances of TopHat running on different samples or on behalf of different users, thus optimizing the overall execution time and enabling a flexible multi-user environment

DIMCloud: a distributed framework for district energy simulation and management

To optimize energy consumption, it is needed to monitor real-time data and simulate all energy flows. In a city district context, energy consumption data usually come from many sources and encoded in different formats. However, few models have been proposed to trace the energy behavior of city districts and handle related data. In this article, we introduce DIMCloud, a model for heterogeneous data management and integration at district level, in a pervasive computing context. Our model, by means of an ontology, is able to register the relationships between different data sources of the district and to disclose the sources locations using a publish-subscribe design pattern. Furthermore, data sources are published as Web Services, abstracting the underlying hardware from the user’s point-of-view

Sinapic Acid Release at the Cell Level by Incorporation into Nanoparticles: Experimental Evidence Using Biomembrane Models

Sinapic acid (SA), belonging to the phenylpropanoid family, and its derivatives are secondary metabolites found in the plant kingdom. In recent years, they have drawn attention because of their various biological activities, including neuroprotective effects. In this study, SA was incorporated into two different nanoparticle systems, solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC). The influence of different concentrations of SA on the nanoparticle systems was evaluated. It was studied the efficacy of the nanoparticle systems to release the active ingredient at cell level through the use of models of biological membranes represented by multilamellar vesicles (MLV) of dimyristoylphosphatidylcholine (DMPC) and conducting kinetic studies by placing in contact SLN and NLC, both unloaded and loaded with two different amounts of SA, with the same biological membrane model. Differential scanning calorimetry (DSC) was used for these studies. The results indicated a different distribution of SA within the two nanoparticle systems and that NLC are able to incorporate and release SA inside the structure of the biological membrane model

A new distributed framework for integration of district energy data from heterogeneous devices

The introduction of ”smart” low-cost sensing (and actuating) devices enabled the recent diffusion of technological products within the ”Internet of Things” paradigm. In a city district context, such devices are crucial for visualization and simulation of energy consumption trends, to increase the energy distribution network efficiency and promote user awareness. Nevertheless, to unlock the potential of this technology, many challenges have to be faced at district level due to the current lack of interoperability between heterogeneous data sources. In this work, we introduce an original infrastructure model, which efficiently manage and integrate district energy data

A Flexible Distributed Infrastructure for Real-Time Co-Simulations in Smart Grids

Due to the increasing penetration of distributed generation, storage, electric vehicles and new ICT technologies, distribution networks are evolving towards the Smart Grid paradigm. For this reason, new control strategies, algorithms and technologies need to be tested and validated before their actual field implementation. In this paper we present a novel modular distributed infrastructure, based on real-time simulation, for multi-purpose Smart Grid studies. The different components of the infrastructure are described and the system is applied to a case study based on a real urban district located in northern Italy. The presented infrastructure is shown to be flexible and useful for different and multi-disciplinary Smart Grid studies