High Performance Computing Infrastructure Research Articles

Large AI Spaces and Russia’s Strategy in the Context of the “Sanctions War”

The article is devoted to the study of the role of technological development in the field of artificial intelligence (AI) in the international political processes, the formation of large policy spaces in the field of AI, as well as the development of Russia in this area. The author provides an analysis of national AI strategies, examines the priority spheres and directions of development of these technologies for different countries, the availability of resources for the goals set, and the specifics of national AI policies of the leading states. The article compares the data on the volume of funding for AI developments, patent activities, and the existing infrastructure for high-performance computing in the countries, that occupy leading positions in the field of AI. The paper provides a brief overview of the key scientific and technological areas that are crucial for the creation of next-generation AI technologies: quantum computing and neuromorphic technologies within the framework of existing major national projects on the study of the brain. The author devotes a special place to the study of international political aspects of technological development in the field of AI in the work. From the perspective of the impact of AI policy on integration processes, the conclusion is made about the formation of two spaces: the first unites the OECD countries with the unconditional leadership of the USA and the EU in the field of research, development, infrastructure, resources, and international standards. The second, restrained space, relies on the technological and financial power of China, where are starting to get involved countries, including Russia, that are facing with narrowing range of opportunities for cooperation with Western countries. The author concludes that the confrontation between the two AI spaces is influenced by a more general trend of decoupling of the economies of the USA and China, which in turn contributes to technological isolation trends at the global level. A special place in the article is devoted to the analysis of the development of AI technologies in Russia; it considers official documents, leading players, and Russia’s positions at the international level. The possible directions of Russia’s cooperation with the world leading players are considered, which allows to develop AI technologies and at the same time maintain technological sovereignty in this area.

Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.

Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.

Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications

Distributed supercomputing is becoming common in different companies and academia. Most of the parallel computing researchers focused on harnessing the power of commodity processors and even internet computers to aggregate their computation powers to solve computationally complex problems. Using flexible commodity cluster computers for supercomputing workloads over a dedicated supercomputer and expensive high-performance computing (HPC) infrastructure is cost-effective. Its scalable nature can make it better employed to the available organizational resources, which can benefit researchers who aim to conduct numerous repetitive calculations on small to large volumes of data to obtain valid results in a reasonable time. In this paper, we design and implement an HPC-based supercomputing facility from commodity computers at an organizational level to provide two separate implementations for cluster-based supercomputing using Hadoop and Spark-based HPC clusters, primarily for data-intensive jobs and Torque-based clusters for Multiple Instruction Multiple Data (MIMD) workloads. The performance of these clusters is measured through extensive experimentation. With the implementation of the message passing interface, the performance of the Spark and Torque clusters is increased by 16.6% for repetitive applications and by 73.68% for computation-intensive applications with a speedup of 1.79 and 2.47 respectively on the HPDA cluster. We conclude that the specific application or job could be chosen to run based on the computation parameters on the implemented clusters.

Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project.

A de novo start‐lost variant in ANKRD28 in a Holstein calf with dwarfism

Osteochondrodysplasias comprise a broad spectrum of hereditary developmental disorders of bone and cartilage, but can also have significant effects on tendons, ligaments, and muscles (Warman et al., 2011). According to Online Mendelian Inheritance in Animals, there are six known dwarfism phenotypes in cattle, which are inherited both recessively and dominantly and are due to structural or signaling pathways disruptions (OMIA 000299-9913; Boegheim et al., 2017). A 3.5-month-old, female, purebred Holstein calf was reported because of its short stature, protruding scapulohumeral joints, increased concavity of the frontal bone, and bossing of the intercornual prominence (Figure S1). A whole-genome sequencing (WGS) approach was performed as described before (Jacinto et al., 2022) using genomic DNA extracted from blood of the affected calf, her dam, and from semen of her sire. Reads were mapped to the ARS-UCD1.2 assembly (Rosen et al., 2020) resulting in an average read depth of 20.7× in the calf, 21.8× in the dam, and 22.1× in the sire, and then processed as reported earlier (Jacinto et al., 2021). We hypothesized that the affected calf had a novel form of dwarfism and investigated the genetic origin. The trio-based WGS approach identified no homozygous protein-changing variants present exclusively in the genome of the affected calf. Consequently, a recessive inheritance seems unlikely. Given that the reported calf was the only case and dwarfism had never before been reported in the Canadian Holstein population, we hypothesized that a dominant mode of inheritance due to a de novo mutation event was more likely. We found three heterozygous private protein-changing variants present in the calf and absent in both parental genomes and in 5365 controls (Table S1). Review of the sequencing data for each of these heterozygous variants revealed that all were indeed de novo variants affecting different genes. Only one of these variants concerns an interesting putative candidate gene (ANKRD28) for the observed phenotype. This heterozygous variant at chr1:152807533C>CA represents a 1-bp insertion in exon 2 of the ANKRD28 gene that directly affect the translation initiation (start) codon (XM_024989836.1: c.2dupT; Figure S1). The predicted consequence of this start-lost variant is that amino acid Met1 is converted to Leu after activation of an upstream translation initiation site at cDNA position −20, resulting in N-terminal protein extension by insertion of six amino acids between Met1 and Gly2 (XP_024845604.1: p.Met1_Gly2insIleValGlyGlyLysAlaLeu). We propose the heterozygous c.2dupT variant as a candidate causative variant for the observed congenital disorder and ANKRD28 as a novel candidate gene for dwarfism phenotypes. The protein-altering nature of this de novo mutation strongly suggests the causality of the variant. The Genome Aggregation Database showed that ANKRD28 falls into the class of loss-of-function haploinsufficient genes (Karczewski et al., 2020). Therefore, two different situations could explain the perceived phenotype: either haploinsufficiency or co-expression of an N-terminally extended protein, leading to non- or abnormal expression of the mutant allele. Nonetheless, since this is a single case study and we have no functional confirmation, this result must be considered preliminary and should be interpreted with caution. It must also be noted that the analysis of short-read genomic data is not ideal for identifying larger structural variants. Further individual cases of dwarfism in cattle or other animal species could be investigated for ANKRD28 variants by DNA sequencing. We thank the Interfaculty Bioinformatics Unit of the University of Bern for providing high-performance computational infrastructure. Irene M. Häfliger was funded by the Swiss National Science Foundation, grant number 172911. Wayne Dickieson of Birkentree Holsteins is gratefully acknowledged. Open access funding provided by University of Bern. The authors declare no conflict of interest. The WGS data are available under the study accession no. PRJEB18113 at the European Nucleotide Archive (www.ebi.ac.uk/ena; calf SAMEA8565094, dam SAMEA8565093, sire SAMEA8565095). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Operationalising fairness in medical AI adoption: detection of early Alzheimer’s disease with 2D CNN

ObjectivesTo operationalise fairness in the adoption of medical artificial intelligence (AI) algorithms in terms of access to computational resources, the proposed approach is based on a two-dimensional (2D) convolutional neural...

Linear Weak Scalability of Density Functional Theory Calculations without Imposing Electron Localization.

Linear scaling density functional theory (DFT) approaches to the electronic structure of materials are often based on the tendency of electrons to localize in large atomic and molecular systems. However, in many cases of actual interest, such as semiconductor nanocrystals, system sizes can reach a substantial extension before significant electron localization sets in, causing a considerable deviation from linear scaling. Herein, we address this class of systems by developing a massively parallel DFT approach which does not rely on electron localization and is formally quadratic scaling yet enables highly efficient linear wall-time complexity in the weak scalability regime. The method extends from the stochastic DFT approach described in Fabian et al. (WIRES: Comp. Mol. Sci.2019, e1412) but is entirely deterministic. It uses standard quantum chemical atom-centered Gaussian basis sets to represent the electronic wave functions combined with Cartesian real-space grids for some operators and enables a fast solver for the Poisson equation. Our main conclusion is that when a processor-abundant high-performance computing (HPC) infrastructure is available, this type of approach has the potential to allow the study of large systems in regimes where quantum confinement or electron delocalization prevents linear scaling.

A methodological approach for assessing the value of energy storage in the power system operation by mid-term simulation

This paper presents a novel methodological approach to assess the operational value of energy storage by mid-term simulation considering the co-optimization of energy and reserves provision in the day-ahead electricity market (DAM) framework. A 0/1 mixed integer linear formulation of the traditional Unit Commitment (UC) problem from the Transmission System Operator (TSO) perspective is utilized, allowing for the realistic and accurate modeling of both conventional and storage units’ operating phases and conditions. Multiple thermal units’ availability scenarios are generated and combined with different climatic conditions (load demand, RES and hydro generation) to formulate a large number of simulation scenarios. Parallel High-Performance Computing (HPC) infrastructure has been employed for the efficient execution of the computationally intensive simulation runs. The proposed optimization model has been tested in the future Greek mainland power system (year 2025) in accordance with the provisions of the approved Greek National Energy and Climate Plan and the most recent Greek TSO Adequacy Study. Simulation results illustrate that the addition of up to 780 MW of new closed-loop energy storage units can lead to higher DAM clearing prices (up to +2.1%), lower conventional thermal units cycling (up to -39.7%) and lower RES curtailment volumes (up to -93.5%) for the base case scenarios. Additionally, the increasing closed-loop energy storage capacity up to 780 MW affects negatively its system and market value, which decrease from 27.4 k€/MW to 16.0 k€/MW (system value) and from 16.5 k€/MW to 4.4 k€/MW (market value), respectively. Sensitivity analysis on the forecasted RES penetration level demonstrated that the operation of up to 3,000 MW of additional wind and PV plants can increase energy storage system value by 86–136% and market value by 184–320% with respect to the base case, thus indicating that the higher the RES penetration is, the higher the value of energy storage.

Workflows for Construction of Spatio-Temporal Probabilistic Maps for Volcanic Hazard Assessment

Probabilistic hazard assessments for studying overland pyroclastic flows or atmospheric ash clouds under short timelines of an evolving crisis, require using the best science available unhampered by complicated and slow manual workflows. Although deterministic mathematical models are available, in most cases, parameters and initial conditions for the equations are usually only known within a prescribed range of uncertainty. For the construction of probabilistic hazard assessments, accurate outputs and propagation of the inherent input uncertainty to quantities of interest are needed to estimate necessary probabilities based on numerous runs of the underlying deterministic model. Characterizing the uncertainty in system states due to parametric and input uncertainty, simultaneously, requires using ensemble based methods to explore the full parameter and input spaces. Complex tasks, such as running thousands of instances of a deterministic model with parameter and input uncertainty require a High Performance Computing infrastructure and skilled personnel that may not be readily available to the policy makers responsible for making informed risk mitigation decisions. For efficiency, programming tasks required for executing ensemble simulations need to run in parallel, leading to twin computational challenges of managing large amounts of data and performing CPU intensive processing. The resulting flow of work requires complex sequences of tasks, interactions, and exchanges of data, hence the automatic management of these workflows are essential. Here we discuss a computer infrastructure, methodology and tools which enable scientists and other members of the volcanology research community to develop workflows for construction of probabilistic hazard maps using remotely accessed computing through a web portal.

Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain

In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multiple additional information, or features, which are increasing the quantity of data to process. As a result, the quantity of features to deal with represents a drawback to process large series or massive histological images rapidly in a robust manner. Existing feature selection methods can reduce the amount of required information but the selected subsets lack reproducibility. We propose a novel methodology operating on high performance computing (HPC) infrastructures and aiming at finding small and stable sets of features for fast and robust segmentation of high-resolution histological images. This selection has two steps: (1) selection at features families scale (an intermediate pool of features, between spaces and individual features) and (2) feature selection performed on pre-selected features families. We show that the selected sets of features are stables for two different neuron staining. In order to test different configurations, one of these dataset is a mono-subject dataset and the other is a multi-subjects dataset to test different configurations. Furthermore, the feature selection results in a significant reduction of computation time and memory cost. This methodology will allow exhaustive histological studies at a high-resolution scale on HPC infrastructures for both preclinical and clinical research.

Quantum Computers for High-Performance Computing

Quantum computing systems are developing rapidly as powerful solvers for a variety of real-world calculations. Traditionally, many of these same applications are solved using conventional high-performance computing (HPC) systems, which have progressed sharply through decades of hardware and software improvements. Here, we present a perspective on the motivations and challenges of pairing quantum computing systems with modern HPC infrastructure. We outline considerations and requirements for the use cases, macroarchitecture, microarchitecture, and programming models needed to integrate near-term quantum computers with HPC system, and we conclude with the expectation that such efforts are well within reach of current technology.

On the performance investigation of a fast optical switches based optical high performance computing infrastructure

Optical networks based on fast optical switches (FOSes) could potentially solve the latency, bandwidth, cost and power consumption challenges in current electrical switches (ESes) based high performance computing (HPC) networks. In this work, we present a novel HPC network which employs distributed FOS interconnecting by removing ES (Firefly). In Firefly, Dragonfly topology is adopted for the inter-group connection of blades, while the intra-group connection of blades is implemented by FOS with fast optical flow control. The Firefly exploits the wavelength, space, and time switching domain with nanoseconds reconfiguration time of the FOS to achieve efficient statistical multiplexing operation. We numerically investigate the Firefly performance with real HPC traffic traces collected by running multiple computing applications in MareNostrum 3 HPC infrastructure with Leaf-Spine architecture. Compared with Leaf Spine architecture, results show that Firefly performs 62.4%, 54%, 68.6%, and 71.8% less latency for the applications Conjugate Gradient (CG), Multi-Grid (MG), Multiple Instruction Lattice Computation (MILC), and Miniature Molecular Dynamics (MINI_MD), respectively. Moreover, Firefly can save 56.4% cost and 65.7% power consumption, respectively, with respect to Leaf-Spine when both support around 10,000 blades.

Dense Matrix Multiplication Algorithms and Performance Evaluation of HPCC in 81 Nodes IBM Power 8 Architecture

Optimizing HPC systems based on performance factors and bottlenecks is essential for designing an HPC infrastructure with the best characteristics and at a reasonable cost. Such insight can only be achieved through a detailed analysis of existing HPC systems and the execution of their workloads. The “Quinde I” is the only and most powerful supercomputer in Ecuador and is currently listed third on the South America. It was built with the IBM Power 8 servers. In this work, we measured its performance using different parameters from High-Performance Computing (HPC) to compare it with theoretical values and values obtained from tests on similar models. To measure its performance, we compiled and ran different benchmarks with the specific optimization flags for Power 8 to get the maximum performance with the current configuration in the hardware installed by the vendor. The inputs of the benchmarks were varied to analyze their impact on the system performance. In addition, we compile and compare the performance of two algorithms for dense matrix multiplication SRUMMA and DGEMM.

Impact of HPC and Automated CFD Simulation Processes on Virtual Product Development—A Case Study

High-performance computing (HPC) enables both academia and industry to accelerate simulation-driven product development processes by providing a massively parallel computing infrastructure. In particular, the automation of high-fidelity computational fluid dynamics (CFD) analyses aided by HPC systems can be beneficial since computing time decreases while the number of significant design iterations increases. However, no studies have quantified these effects from a product development point of view yet. This article evaluates the impact of HPC and automation on product development by studying a formula student racing team as a representative example of a small or medium-sized company. Over several seasons, we accompanied the team, and provided HPC infrastructure and methods to automate their CFD simulation processes. By comparing the team’s key performance indicators (KPIs) before and after the HPC implementation, we were able to quantify a significant increase in development efficiency in both qualitative and quantitative aspects. The major aerodynamic KPI increased up to 115%. Simultaneously, the number of expedient design iterations within one season increased by 600% while utilizing HPC. These results prove the substantial benefits of HPC and automation of numerical-intensive simulation processes for product development.

Region-specific guidelines to encourage SMEs to use high performance computing

PurposeThis paper aims to explore high performance computing (HPC) in the context of the South East region of Ireland, which hosts a publicly available HPC infrastructure, by identifying whether companies, especially small and medium enterprises (SMEs), are using, or are prepared to use, HPC to improve their business processes, expansion and sustainability. The result of the analysis provides region-specific guidelines that are meant to improve the HPC landscape in the region. The lessons learned from this research may apply to other similar, and developing, European regions.Design/methodology/approachThis paper explores the use of HPC in the context of the South East region of Ireland and examines whether companies, especially SMEs, are benefiting from the use of publicly available HPC infrastructure in the region. This paper also provides a set of recommendations, of a policy nature, and required actions to increase HPC usage, based on the reality of the region. Therefore, the first step in the process was to understand the HPC landscape in the South East region of Ireland. Interviews were conducted with higher education institute (HEI) staff who were knowledgeable about the HPC infrastructure of their institutes and also about whether collaboration between the HEIs and businesses from the same region exists. The interview findings allowed the proposal of region-specific guidelines to improve the HPC landscape and collaboration in the region. The guidelines were analysed and refined in a focus group with key regional stakeholders from academia, industry and government, who have experience and expertise in high-technology transfer processes happening in the region.FindingsThe findings of the current study strongly suggest that HPC usage by SMEs in the South East region of Ireland is still incipient; and that HPC knowledge is currently inadequately transferred from the HEI hosting the HPC infrastructure to public and private sector organisations based in the region. The findings also demonstrate that there are no courses or training programmes available dedicated to HPC and that the level of collaboration between the HEI hosting the HPC infrastructure and industry in the region is minimal as regards HPC usage and projects. Therefore, there is a need to put specific targeted policies and actions, both from a regional government and HEI perspectives, in place to encourage SMEs to optimise their processes by using HPC.Originality/valueThis research is unique as it provides customised region-specific recommendations (RSR) and feasible actions to encourage industry, especially SMEs, to use HPC and collaborate around it. The literature review identified that there is a lack of studies that can inform policymakers to include HPC in their innovation agenda. Previous research studies specifically focussing on HPC policies are even more scarce. Most of the existing research pertaining to HPC focusses on the technical aspect of HPC; therefore, this research and paper bring a new dimension to existing HPC research. Even though this research was focussed on the South East of Ireland region, the model that generated the RSRs can be extrapolated and applied to other regions that need to develop their HPC landscape and the use of HPC among SMEs in their respective regions.

Evaluating the Impact of a Wall-Type Green Infrastructure on PM10 and NOx Concentrations in an Urban Street Environment

Nature-based solutions can represent beneficial tools in the field of urban transformation for their contribution to important environmental services such as air quality improvement. To evaluate the impact on urban air pollution of a CityTree (CT), an innovative wall-type green infrastructure in passive (deposition) and active (filtration) modes of operation, a study was conducted in a real urban setting in Modena (Italy) during 2017 and 2018, combining experimental measurements with modelling system evaluations. In this work, relying on the computational resources of CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID High Performance Computing infrastructure, we used the air pollution microscale model PMSS (Parallel Micro-SWIFT-Micro SPRAY) to simulate air quality during the experimental campaigns. The spatial characteristics of the impact of the CT on local air pollutants concentrations, specifically nitrogen oxides (NOx) and particulate matter (PM10), were assessed. In particular, we used prescribed bulk deposition velocities provided by the experimental campaigns, which tested the CT both in passive (deposition) and in active (filtration) mode of operation. Our results showed that the PM10 and NOx concentration reductions reach from more than 0.1% up to about 0.8% within an area of 10 × 20 m2 around the infrastructure, when the green infrastructure operates in passive mode. In filtration mode the CT exhibited higher performances in the abatement of PM10 concentrations (between 1.5% and 15%), within approximately the same area. We conclude that CTs may find an application in air quality hotspots within specific urban settings (i.e., urban street canyons) where a very localized reduction of pollutants concentration during rush hours might be of interest to limit population exposure. The optimization of the spatial arrangement of CT modules to increment the “clean air zone” is a factor to be investigated in the ongoing development of the CT technology.

Using HPC infrastructures for deep learning applications in fusion research

In the fusion community, the use of high performance computing (HPC) has been mostly dominated by heavy-duty plasma simulations, such as those based on particle-in-cell and gyrokinetic codes. However, there has been a growing interest in applying machine learning for knowledge discovery on top of large amounts of experimental data collected from fusion devices. In particular, deep learning models are especially hungry for accelerated hardware, such as graphics processing units (GPUs), and it is becoming more common to find those models competing for the same resources that are used by simulation codes, which can be either CPU- or GPU-bound. In this paper, we give examples of deep learning models—such as convolutional neural networks, recurrent neural networks, and variational autoencoders—hat can be used for a variety of tasks, including image processing, disruption prediction, and anomaly detection on diagnostics data. In this context, we discuss how deep learning can go from using a single GPU on a single node to using multiple GPUs across multiple nodes in a large-scale HPC infrastructure.

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.

RNA Editing Detection in HPC Infrastructures.

RNA editing by A-to-I deamination is a relevant co/posttranscriptional modification carried out by ADAR enzymes. In humans, it has pivotal cellular effects and its deregulation has been linked to a variety of human disorders including neurological and neurodegenerative diseases and cancer. Despite its biological relevance, the detection of RNA editing variants in large transcriptome sequencing experiments (RNAseq) is yet a challenging computational task. To drastically reduce computing times we have developed a novel REDItools version able to identify A-to-I events in huge amount of RNAseq data employing High Performance Computing (HPC) infrastructures.Here we show how to use REDItools v2 in HPC systems.

RAP: A Web Tool for RNA-Seq Data Analysis.

Since 1950 main studies of RNA regarded its role in the protein synthesis. Later insights showed that only a small portion of RNA codes for proteins where the rest could have different functional roles. With the advent of Next Generation Sequencing (NGS) and in particular with RNA-seq technology the cost of sequencing production dropped down. Among the NGS application areas, the transcriptome analysis, that is, the analysis of transcripts in a cell, their quantification for a specific developmental stage or treatment condition, became more and more adopted in the laboratories. As a consequence in the last decade new insights were gained in the understanding of both transcriptome complexity and involvement of RNA molecules in cellular processes. For what concerns computational advances, bioinformatics research developed new methods for analyzing RNA-seq data. The comparison among transcriptome profiles from several samples is often a difficult task for nonexpert programmers. Here, in this chapter, we introduce RAP (RNA-Seq Analysis Pipeline), a completely automated web tool for transcriptome analysis. It is a user-friendly web tool implementing a detailed transcriptome workflow to detect differential expressed genes and transcript, identify spliced junctions and constitutive or alternative polyadenylation sites and predict gene fusion events. Through the web interface the researchers can get all this information without any knowledge of the underlying High Performance Computing infrastructure.