Search

OBJECTIVES: This study aims to evaluate the reliability and clinical utility of NS3 sequencing in hepatitis C virus (HCV) 1-infected patients who were candidates to start a PI-containing regimen. METHODS: NS3 protease sequencing was performed by in-house-developed HCV-1 subtype-specific protocols. Phylogenetic analysis was used to test sequencing reliability and concordance with previous genotype/subtype assignment by commercial genotyping assays. RESULTS: Five hundred and sixty-seven HCV plasma samples with quantifiable HCV-RNA from 326 HCV-infected patients were collected between 2011 and 2014. Overall, the success rate of NS3 sequencing was 88.9%. The success rate between the two subtype protocols (HCV-1a/HCV-1b) was similarly high for samples with HCV-RNA >3 log IU/mL (>92% success rate), while it was slightly lower for HCV-1a samples with HCV-RNA ≤3 log IU/mL compared with HCV-1b samples. Phylogenetic analysis confirmed the genotype/subtype given by commercial genotyping assays in 92.9% (303/326) of cases analysed. In the remaining 23 cases (7.1%), 1 was HCV-1g (previously defined as subtype 1a), 1 was HCV-4d (previously defined as genotype 1b) and 1 was HCV-1b (previously defined as genotype 2a/2c). In the other cases, NS3 sequencing precisely resolved the either previous undetermined/discordant subtype 1 or double genotype/subtype assignment by commercial genotyping assays. Resistance-associated variants (RAVs) to PI were detected in 31.0% of samples. This prevalence changed according to PI experience (17.1% in PI-naive patients versus 79.2% in boceprevir/telaprevir/simeprevir-failing patients). Among 96 patients with available virological outcome following boceprevir/telaprevir treatment, a trend of association between baseline NS3 RAVs and virological failure was observed (particularly for HCV-1a-infected patients: 3/21 failing patients versus 0/22 achieving sustained virological response; P = 0.11). CONCLUSIONS: HCV-NS3 sequencing provides reliable results and at the same time gives two clinically relevant pieces of information: a correct subtype/genotype assignment and the detection of variants that may interfere with the efficacy of PI.

Background & Aims: Despite the excellent efficacy of direct-acting antivirals (DAA) reported in clinical trials, virological failures can occur, often associated with the development of resistance-associated substitutions (RASs). This study aimed to characterize the presence of clinically relevant RASs to all classes in real-life DAA failures. Methods: Of the 200 virological failures that were analyzed in 197 DAA-treated patients, 89 with pegylated-interferon+ribavirin (PegIFN+RBV) and 111 without (HCV-1a/1b/1g/2/3/4=58/83/1/6/24/25; 56.8% treatment experienced; 65.5% cirrhotic) were observed. Sanger sequencing of NS3/NS5A/NS5B was performed by home-made protocols, at failure (N= 200) and whenever possible at baseline (N= 70). Results: The majority of the virological failures were relapsers (57.0%), 22.5% breakthroughs, 20.5% non-responders. RAS prevalence varied according to IFN/RBV use, DAA class, failure type and HCV genotype/subtype. It was 73.0% in IFN group vs 49.5% in IFN free, with the highest prevalence of NS5A-RASs (96.1%), compared to NS3-RASs (75.9% with IFN, 70.5% without) and NS5B-RASs (66.6% with IFN, 20.4% without, in sofosbuvir failures). In the IFN-free group, RASs were higher in breakthrough/non-responders than in relapsers (90.5% vs 40.0%, P= 2 DAA classes showed multiclass resistance, including 11/11 NS3+NS5A failures. Furthermore, 20.0% of patients had baseline-RASs, which were always confirmed at failure. Conclusions: In our failure setting, RAS prevalence was remarkably high in all genes, with a partial exception for NS5B, whose limited resistance is still higher than previously reported. This multiclass resistance advocates for HCV resistance testing at failure, in all three genes for the best second-line therapeutic tailoring.

Gene-environment interaction, through abnormal intestinal adsorption, has been proposed as possible mechanism for autism pathogenesis in those patients lacking of causative genetic variants. Haptoglobin (HP) is a haemoglobin binding and acute-phase plasma protein, encoded by two co-dominant alleles, HP-1 and HP-2, producing pre-HP-1 and pre-HP-2 proteins that mature in HP-1 and HP-2, respectively. Due to a 1.7Kb copy number variation in the HP gene, the HP-2 allele has two extra exons with respect to HP-1 (Fig. 1). Thus the HP protein is a dimer in homozygous subjects for HP-1 allele and is multimer in homozygous HP-2. Endogenous pre-HP-2 protein deregulates intestinal tight-junctions through EGFR and PAR2 activation, increases intestinal permeability and has been associated with autoimmune and inflammatory diseases as well as with psychiatric conditions (Fasano, 2011; Sturgeon and Fasano, 2016). Since the association between HP alleles and autism has just been investigated in a very small sample size of patients and controls (Rose et al., 2018), we genotyped, by PCR analysis, HP in a cohort of Italian patients with autism (n=406) and in controls (n=367). The aim was to evaluate the possible association of HP-2 and autism spectrum disorder (ASD). Contrary to what we expected, HP-1 allele distribution was different between patients and controls (36.3% and 29.4%, respectively) and significantly associated with autism (P=0.0041). Since a subgroup of patients and controls have already been genotyped by Illumina Human Omni-15-8 v.1.0 and Affy-6.0 chips, respectively, we are trying to impute HP alleles from flanking SNP haplotypes. HP alleles will therefore be predicted in publicly available large cohorts of patients with autism.

Motivation NGS has revolutionized the genomic research. Technological advances have reduced the sequencing costs while have notably increased the sequence throughput. Typically, artifacts of different nature that arise during the sequencing process affect NGS data. As these artifacts may influence the downstream analyses, data quality control (QC) becomes mandatory. Different QC tools have been proposed. Without claiming to be exhaustive, let us cite some of the most popular tools, i.e., FASTQC [1], FASTX-Toolkit [2], NGS QC Toolkit [3], PRINSEQ [4]. FASTQC is a widely adopted tool for quality assessment. It provides a set of analyses to assess the raw data according to multiple aspects. It also provides a GUI to report all analyses highlighting problems in the data. FASTX-Toolkit is a collection of command line tools for FASTA/FASTQ file processing including some quality statistics. It also provides tools for quality filtering/trimming. NGS QC Toolkit is a standalone tool for QC that includes tools for sequence trimming and statistics calculation. It is also equipped with modules to generate statistics in graphical format. PRINSEQ is a web-based and standalone tool that can be used to generate summary statistics of sequence and quality data and to filter and trim sequences. It should be pointed out that the massive amounts of generated sequences make QC computationally intensive. Despite that, only some tools implement a multicore processing strategy to deal with that computational challenge. In our opinion, even though these tools implement very useful features, their implementation does not permit to efficiently analyze large amounts of NGS data. We deem that QC tasks can be efficiently parallelized on manycore architectures as GPUs. GPUs are devices equipped with hundreds of cores able to handle thousands of threads simultaneously, so that a very high level of parallelism can be reached. In this work, we present G-FastQC (GPU Fast Quality Control) a GPU-based tool for quality assessment, filtering, and trimming of NGS data. Methods G-FastQC has been devised to be massively parallelized on NVIDIA GPUs. It supports single- and paired-end libraries generated with Illumina platforms. G-FastQC implements a set of analyses to perform QC checks on raw data. These analyses allow to assess the data according to aspects related to the quality scores and content of the sequences. In particular, G-FastQC allows to calculate the: average quality values across all bases at each position; quality score distribution over all sequences; GC content across the whole length of each sequence; GC content across all bases; sequence content across all bases at each position.To help users to analyze the data quality, G-FastQC has been integrated with an interactive web-based interface built using Shiny[5] that allows to plot graphs of the performed analyses.G-FastQC also supports quality filtering and trimming. As for quality filtering, G-FastQC allows to filter sequences based on the amount of both low quality and N nucleotides as well as to filter sequences based on the GC content. It can also be used to mask the nucleotides with a quality score lower than a given threshold. As for trimming, G-FastQC implements an operator based on a sliding window approach. It analyzes the amount of low quality nucleotides in a sliding window. When the amount of these nucleotides is higher than a given threshold, G-FastQC trims all nucleotides from the start of the window to the 3'. Trimmed sequences with length lower than a given threshold can be automatically discarded. The same sliding window approach has been used to implement an operator aimed at masking these nucleotides rather than trimming them. It should be pointed out, that unlike the above-mentioned tools, G-FastQC has been designed to perform analysis of several datasets in a single run. Results Experiments carried out on a 12 cores Intel Xeon CPU E5-2667 2.90 GHz and an NVIDIA Tesla k20c show that G-FastQC outperforms notably the other tools in terms of computing time. For instance, in the task of filtering low-quality sequences, it has been 21.4x/28.3x faster than FASTX-Toolkit/NGS QC Toolkit analyzing a dataset consisting of 50M of 100 bp reads. Similar results have been obtained comparing the performance of G-FastQC with those of the other tools to generate the quality reports, and in the tasks of filtering/trimming the raw data. References 1. http://www.bioinformatics.abraham.ac.uk/projects/fastqc/ 2. http://hannonlab.cshl.edu/fastx_toolkit 3. Patel RK, Jain M (2012) NGS QC Toolkit: a toolkit for quality control of next generation sequencing data, PloS one, 7(2), e30619. 4. Schmieder R, Robert E (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27(6), 863-864. 5. https://shiny.rstudio.com

Motivation BBMRI-ERIC mission is to construct a pan-European biobanking infrastructure, building on existing infrastructure, resources and technologies, specifically complemented with innovative components and properly embedded into European ethical, legal and societal frameworks. BBMRI.it is the national node member of the BBMRI-ERIC infrastructures involving the majority biobanks and biological resource centers located in Italy. The Italian node is actually composed by more than 18 universities, 23 IRCCS, 40 hospitals, associations of patients. The activities of BBMRI.it are aimed at harmonizing the standard operating procedures (SOPs) of biobanks, implementing the quality management system and encouraging public/private partnerships. For this purpose and in collaboration with the BBMRI.it partners we have developed a dedicated web portal (http://www.bbmri.it) to construct and operate a sustainable infrastructure for biological information in Italy to support biomedical research and its translation to medicine in Italy. Methods The BBMRI.it web portal [1] has been developed in order to collect the data and to ensure the easiest way to interact with the central node of BBMRI-ERIC infrastructures. For the development of this portal we have using the Liferay community Web Portal [2]. Based on the Liferay services, we have organized a public area of the portal for publishing all the information regarding the activities of the BBMRI.it node in Italy and Europe. The portal provide also a private area, only available via Login for the registered user in order to perform these specific services: A) To collect the initial information from the Biobank's we have developed the Survey for Biobank's accreditation using the Java Vaadin Framework [3] (Version 7.1.15). By means of this service users can add all Biobanks information online and submit to the evaluation committee the formal request to be accredited in the BBMRI.it network. All the information are stored into a specific database managed by a dedicate middleware developed with the Hibernate Framework [3]. B) The BBMRI.it Directory Service has been developed in order to allow the users to search on-line the public information regarding all the Biobanks in Italy and in Europe by the Directory 3.1 based on the MIABIS standard. This Service is realized using the Java PrimeFaces Framework [4] (Version 5.2). Thought this application is it possible to store and retrieve the information, based on the biobanks accepted in the Italian Network. This system is able to collect the data and to share the information with the BBMRI-ERIC Directory 3.1. This application is divided in 4 categories: Biobanks Networks, Biobanks, Collections and Contacts. A specific middleware for management of data has been implemented in order to manage the data with a RDBMS interconnection locally and with the BBMRI-ERIC Directory; this allow the utilization of multiple data connectors for sharing information with an unique user interface. C) The BBMRI.it Biobank Manager service, is available for the users administration in order to validate the submitted data; this operation consist in different steps: Step 1) The registered Biobank after the admission in BBMRI.it Network is granted with the permission for access to the BBMRI.it Directory Service in order to complete the information requested. Step 2) The data already present in the survey are automatically added into the BBMRI.it Directory Structure in order to simplify the process of share the information with the BBMRI-ERIC Directory. Step 3) Finally the biobanks owner is informed via email about the final acceptance in BBMRI.it. All the services described have been integrated into the portal as a portlets and can be utilized by the users in according with their role in the portal. Results We have developed the BBMRI.it Portal to facilitate the collection of data and the collaboration among the biobanks. This portal represent the centralize resource for the storage and the analysis of the information related the biobanks. By using the Directory application is possible to interact and share the information with the central European directory server. Actually the BBMRI.it Directory stores total information about more than 70 biobanks. Finally for the bioinformatics analysis of the Omics data generated from the Biobanks samples we have also developed a platform hosted on a computer cluster at the Institute of Biomedical Technologies (ITB/CNR) in Milan able to combines several Bioinformatics and Systems Biology tools. References [1] BBMRI.it Project web Site: http://www.bbmri.it [2] Liferay Portal Site: http://www.liferay.com [3] Vaadin Site: https://vaadin.com/home [3] Hibernate Site: http://www.hibernate.org [4] Primefaces: http://www.primefaces.org

Motivation Nowadays, advances in technology has arisen in a huge amount of data in both biomedical research and healthcare systems. This growing amount of data gives rise to the need for new research methods and analysis techniques. Analysis of these data offers new opportunities to define novel diagnostic processes. Therefore, a greater integration between healthcare and biomedical data is essential to devise novel predictive models in the field of biomedical diagnosis. In this context, the digitalization of clinical exams and medical records is becoming essential to collect heterogeneous information. Analysis of these data by means of big data technologies will allow a more in depth understanding of the mechanisms leading to diseases, and contextually it will facilitate the development of novel diagnostics and personalized therapeutics. The recent application of big data technologies in the medical fields will offer new opportunities to integrate enormous amount of medical and clinical information from population studies. Therefore, it is essential to devise new strategies aimed at storing and accessing the data in a standardized way. Moreover, it is important to provide suitable methods to manage these heterogeneous data. Methods In this work, we present a new information technology infrastructure devised to efficiently manage huge amounts of heterogeneous data for disease prevention and precision medicine. A test set based on data produced by a clinical and diagnostic laboratory has been built to set up the infrastructure. When working with clinical data is essential to ensure the confidentiality of sensitive patient data. Therefore, the set up phase has been carried out using "anonymous data". To this end, specific techniques have been adopted with the aim to ensure a high level of privacy in the correlation of the medical records with important secondary information (e.g., date of birth, place of residence). It should be noted that the rigidity of relational databases does not lend to the nature of these data. In our opinion, better results can be obtained using non-relational (NoSQL) databases. Starting from these considerations, the infrastructure has been developed on a NoSQL database with the aim to combine scalability and flexibility performances. In particular, MongoDB [1] has been used as it fits better to manage different types of data on large scale. In doing so, the infrastructure is able to provide an optimized management of huge amounts of heterogeneous data, while ensuring high speed of analysis. Results The presented infrastructure exploits big data technologies in order to overcome the limitations of relational databases when working with large and heterogeneous data. The infrastructure implements a set of interface procedures aimed at preparing the metadata for importing data in a NOSQL DB. Moreover, data can also be represented as a graph using Neo4j [2]; The Neo4J DB allows you to emphasize and enhance the connections between the data and facilitate the retrieve and navigation of data (Fig 1). Experimental tests on huge amount of data show that our infrastructure exhibits performances in terms of speed and scalability unachievable with relational databases. These performances are mainly related to ability of the infrastructure to index any type of field as well as to customize the queries. In particular, the high flexibility to customize the queries increases the search performance and specificity of the results. As for future work, we planned to implement new functions and operators to perform specialized statistics analysis on big data. References [1] http://www.mongodb.org [2] http://neo4j.com/

Introduction Advances in next-generation sequencing technologies is facilitating the sequencing and de-novo assembly of genomes from different eukaryotic and prokaryotic species. The growing number of fully assembled sequences is providing new opportunities to study the genome of different species. In addition, the availability of genome sequences of related species provides valuable opportunities to compare them with the aim to study their evolution. In this context, researchers are increasingly relying on comparative genomics to explore the genomic signals that control gene function across many species with the aim to better understand the structure and function of genes. This information helps researchers to develop new approaches for treating of diseases. To perform these analyses researchers need to use a variety of tools to annotate, analyze, explore, and compare genome sequences. Specialized tools and with different features have been proposed to the scientific community. However, the use of these tools may be difficult for biologists. In general, to facilitate the work of researchers, these tools should adopt common standards to ensure interoperability among them [1]. However, tools and analytical methods do not always use common standards making difficult the interoperability. Moreover, researchers may need to replicate scientific analyses presented in a paper, as well as to apply modifications, and to update data according to their specific needs. This may involve the analysis of huge amount of data, posing new computational challenges. Another problem is related to the sharing of data. In fact, typically, each tool generates and stores data using formats that are incompatible with the other ones. To this end, researchers involved in these analyses should have access to a single platform designed with the aim to ensure direct interoperability among the tools while supporting automated computation. According to these considerations, we implemented a standard-based HPC platform aimed at providing support at researchers involved in the tasks of annotating, analyzing, and visualizing eukaryotic and prokaryotic genomes. Methods The platform has been conceived with the aim to provide support for storage, annotation and analysis of both eukaryotic and prokaryotic genomes. To this end, it has been built on a hardware infrastructure intended to enable big-data classes of applications which consists of a massive storage platform of 1.6 PB, 2040 CPU cores, 16 NVIDIA K20 GPUs, and 2 big memory nodes (i.e., 1 node equipped with 1TB and 1 node equipped with 512GB of memory). Most of the tools integrated in the platform are components of the Generic Model Organism Database (GMOD) project [2]. GMOD is a collection of interconnected tools and databases widely used by the scientific community that includes several components for managing, annotating and visualizing genomic data. Many of the GMOD components are mature tools with several years of development and testing driven by diverse groups of developers, scientists, and laboratories that use and/or improve these components every day. In particular, the platform supports the following GMOD components: Maker [3][4], Chado[5], WebApollo [6], Gbrowse [7][8], SynView [9], and Galaxy [10]. Moreover, the platform also provides support to the (not GMOD component) GA4GH API [11] released by the Global Alliance for Genomics and Health [12] to share genomic data. In the following of this section, the above tools are briefly described. ? Maker is an annotation pipeline that can be used for de-novo annotation of genomes as well as for updating of existing annotations with the aim to reflect new evidence. It should be pointed out that Maker is MPI-capable for rapid parallelization across computer clusters. This feature makes it also suitable to annotate large genomes. Its annotation pipeline identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations with evidencebased quality values. Maker outputs annotations in the standard GFF3 format that can be directly loaded into relational databases as Chado and genome browsers that adhere to the GMOD standards. ? Chado is a relational database schema that underlies many GMOD installations. It is used to represent different type of biological data. It has been designed to handle biological knowledge related to sequence, sequence comparisons, phenotypes, genotypes, ontologies, publications, and phylogeny. ? WebApollo is a web-based manual annotation environment for distributed community. It allows multiple users to annotate parts of the same sequence concurrently. To this end, any change made by other users is notified in the user's browser window. WebApollo keeps tracks of all changes made that can be approved or rejected by an administrator. ? GBrowse is a powerful and mature genome browser. It supports most of genome browser features, including qualitative and quantitative tracks, track uploading, track sharing, track downloading, and interactive track configuration. ? SynView is a interactive and customizable comparative genomic visualization tool based on GBrowse. It is able to display both the genomes comparison and the associated functional annotations in the same working environment. ? Galaxy is an open web-based scientific workflow system for data intensive biomedical research easily accessible to researchers that do not have programming experience. It provides an environment that helps researchers to run bioinformatics analysis tools as well as ad-hoc defined workflows. ? GA4GH API have been defined with the aim to allow a interoperable exchange of genomic information among multiple organizations and on multiple platforms. This is a freely available open standard for interoperability, that uses common web protocols to support serving and sharing of data on DNA sequences, genomic annotations and genomic variations. The API allow to create a data source that can be easily integrated in genomic analysis pipeline as well as integrated into specialized genomics platforms. Results The above tools have been properly installed and configured to work in the platform. The platform provides different access points. Authorized users can access to Maker to annotate their sequences as well as for updating of existing ones. Annotation data obtained with Maker are loaded into a Chado database and integrated into both WebApollo and GBrowse. Users can access to WebApollo for manual annotation, and to GBrowse to explore the annotated sequences as well as to compare genome sequences with SynView. GBrowse has been configured to show different tracks to explore alignments, protein-coding genes, CDS, mRNA, and other regions, as well as the GC content of the analyzed sequences. Users can customize the tracks as well as download them or upload other tracks for visualization. Moreover, GBrowse has also been configured to send data to Galaxy. Using this feature, users can visualize selected tracks on a given part of a sequence, and with a single click can send these data to Galaxy for analysis. As for Galaxy, it has been configured to run jobs on the previously described hardware infrastructure and to provide support for both CPU- and GPU-based tools [13]. It should be pointed out that bioinformatics is exploring new computational approaches based on the use of hardware accelerators such as the GPUs. Use of GPUs over the last years has resulted in significant increases in the performance of certain applications. Despite GPUs are increasingly used most laboratories do not have access to a GPU cluster or server. In this context, it is very important to provide useful services to use these tools with the aim to ensure reproducibility of specific analyses. It should be observed that Galaxy supports different distributed resource managers with the aim to enable different clusters. In our opinion, SLURM [14] represents the most suitable workload manager to manage and control jobs. SLURM is a highly configurable workload and resource manager and it is currently used on six of the ten most powerful computers in the world including the Piz Daint, utilizing over 5000 NVIDIA Tesla K20 GPUs. Users can interactively perform and refine their analyses using Galaxy. In particular, users can send or upload data into Galaxy from the other access points of the platform, analyze them, and store the results ignoring the implementation details of the underlying computing infrastructure. A GA4GH server has also been installed with the aim to share and make easy to integrate annotation data into external genomic analysis pipelines. It should be pointed out that the GA4GH API allows to retrieve genomic data according to different criteria that can be combined to meet the needs of the researcher. Then, to facilitate the work of researchers, specialized tools have been added to Galaxy to retrieve annotation data from the GA4GH server. Currently, the platform is used to support some initiatives of the InterOmics Project [15] aimed at studying human cancer, microbiome, and some plant genomes. Conclusions We presented a dedicated platform for genome annotation and analysis. The platform has been implemented with the aim to help researchers to store, annotate, explore, and analyze genomic data. The main goal that has driven the design of the platform has been to facilitate analysis and computational reproducibility. To this end, the platform integrates interconnected tools that use common standards while ensuring automated computation on a robust hardware infrastructure that support both CPU- and GPU-based computation. The GA4Gh open standard has been used to share genomic data. The platform is intended to be dynamic, therefore it will be integrated with other specialized software solutions that will be useful for the genome analysis. Currently, we are working to integrate other specialized comparative genomics tools of the GMOD project as CMAP [16] and SynBrowse [17].

Motivation Recent advances in genome sequencing and biological data analysis technologies used in bioinformatics have led to a fast and continuous increase in biological data. The difficulty of managing the huge amounts of data currently available to researchers and the need to have results within a reasonable time have led to the use of distributed and parallel computing infrastructures for their analysis. Recently, bioinformatics is exploring new approaches based on the use of hardware accelerators as GPUs. From an architectural perspective, GPUs are very different from traditional CPUs. Indeed, the latter are devices composed of few cores with lots of cache memory able to handle a few software threads at a time. Conversely, the former are devices equipped with hundreds of cores able to handle thousands of threads simultaneously, so that a very high level of parallelism can be reached. Use of GPUs over the last years has resulted in significant increases in the performance of certain applications. Despite GPUs are increasingly used in bioinformatics most laboratories do not have access to a GPU cluster or server. In this context, it is very important to provide useful services to use these tools. Methods A web-based platform has been implemented with the aim to enable researchers to perform their analysis through dedicated GPU-based computing resources. To this end, a GPU cluster equipped with 16 NVIDIA Tesla k20c cards has been configured. The infrastructure has been built upon the Galaxy technology [1]. Galaxy is an open web-based scientific workflow system for data intensive biomedical research accessible to researchers that do not have programming experience. Let us recall that Galaxy provides a public server, but it does not provide support to GPU-computing. By default, Galaxy is designed to run jobs on local systems. However, it can also be configured to run jobs on a cluster. The front-end Galaxy application runs on a single server, but tools are run on cluster nodes instead. To this end, Galaxy supports different distributed resource managers with the aim to enable different clusters. For the specific case, in our opinion SLURM [2] represents the most suitable workload manager to manage and control jobs. SLURM is a highly configurable workload and resource manager and it is currently used on six of the ten most powerful computers in the world including the Piz Daint, utilizing over 5000 NVIDIA Tesla K20 GPUs. Results GPU-based tools [3] devised by our group for quality control of NGS data have been used to test the infrastructure. Initially, this activity required to make changes to the tools with the aim to optimize the parallelization on the cluster according to the adopted workload manager. Successively, the tools have been converted into web-based services accessible through the Galaxy portal. Currently, we are working to optimize the workload manager configuration. As for future work, we planned to share through Galaxy other GPU-based tools for NGS analyses released by our group [4][5] as well as specialized workflows created using these and other validated tools imported from the Galaxy ToolShed repository. These activities will be carried-out through the European ELIXIR project. 1. Goecks, J, Nekrutenko, A, Taylor, J and The Galaxy Team. "Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences". Genome Biol. 2010 Aug 25;11(8):R86. 2. http://slurm.schedmd.com/ 3. Manconi, A et al. "G-CNV: a GPU-based tool for preparing data to detect CNVs with readdepth methods". Frontiers in Bioengineering and Biotechnology, 2015, 3. 4. Manconi, A et al. "GPU-BSM: A GPU-based tool to map bisulfite-treated reads". PLoS ONE 9(5): e97277. doi:10.1371/journal.pone.0097277, 2014. 5. Manconi, A et al. "A tool for mapping single nucleotide polymorphisms using graphics processing units". BMC Bioinformatics, 15(Suppl 1), S10, 2014

Precision medicine is an emerging and novel approach for both disease treatment and prevention. Precision medicine allows to classify individuals into subpopulations that differ in their susceptibility to a particular disease with the aim to tailor the medical treatment to the individual characteristics of each patient. To provide precision medicine to patients researchers needs to analyze huge amounts of heterogeneous data from both biomedical research and healthcare systems. The growing amount of these data gives rise to the need for new research methods and analysis techniques. In this paper we present a infrastructure that exploits new strategies aimed at storing, accessing, and analyzing efficiently these heterogeneous data.

The HIRMA project (Hepatocarcinoma Innovative Research Markers) aims at identifying and characterizing innovative markers related to the biological role of hepatotropic viruses (HBC, HBV and HDV) in the development of hepatocellular carcinoma, which is the third leading cause of cancer death worldwide. To achieve this result 30 research and hospital centres were involved to create an Italian collaborative network able to share and manage the information regarding a population of patients and their biological samples stored in biobanks. In order to allow the collection, the management and the search of the clinical data a dedicated centralized web portal (http://www.hirma.it) has been developed

Understanding the structure, function and development of the human genome is a key factor to improve the quality of life. In order to achieve this goal developing and using a modern ICT infrastructure is essential, and can exploit next, generation High Performance Computing (HPC) systems beyond the Petaflop scale in a collaborative and efficient way. The genetic linkage analysis of Single Nucleotide Polymorphism (SNP) markers has recently become a very popular for genetic epidemiology and population studies, aiming to discover the genetic correlation in complex diseases. The high computational cost and memory requirements of the major algorithms proposed in the literature make analyses of medium/large data sets very hard on a single CPU. A Grid based facility has hence been set up upon a high-performance infrastructure, the EGEE Grid, in order to create a tool for achieving whole-genome linkage analysis.

Background Sex is a major source of diversity among patients and a sex-informed approach is becoming a new paradigm in precision medicine. We aimed to describe sex diversity in myelodysplastic syndromes in terms of disease genotype, phenotype, and clinical outcome. Moreover, we sought to incorporate sex information into the clinical decision-making process as a fundamental component of patient individuality. Methods In this multicentre, observational cohort study, we retrospectively analysed 13 284 patients aged 18 years or older with a diagnosis of myelodysplastic syndrome according to 2016 WHO criteria included in the EuroMDS network (n=2025), International Working Group for Prognosis in MDS (IWG-PM; n=2387), the Spanish Group of Myelodysplastic Syndromes registry (GESMD; n=7687), or the Dusseldorf MDS registry (n=1185). Recruitment periods for these cohorts were between 1990 and 2016. The correlation between sex and genomic features was analysed in the EuroMDS cohort and validated in the IWG-PM cohort. The effect of sex on clinical outcome, with overall survival as the main endpoint, was analysed in the EuroMDS population and validated in the other three cohorts. Finally, novel prognostic models incorporating sex and genomic information were built and validated, and compared to the widely used revised International Prognostic Scoring System (IPSS-R). This study is registered with ClinicalTrials.gov, NCT04889729. Findings The study included 7792 (58middot7%) men and 5492 (41middot3%) women. 10 906 (82middot1%) patients were White, and race was not reported for 2378 (17middot9%) patients. Sex biases were observed at the single-gene level with mutations in seven genes enriched in men (ASXL1, SRSF2, and ZRSR2 p

PubMedID: 30962619 The domestication of wild emmer wheat led to the selection of modern durum wheat, grown mainly for pasta production. We describe the 10.45 gigabase (Gb) assembly of the genome of durum wheat cultivar Svevo. The assembly enabled genome-wide genetic diversity analyses revealing the changes imposed by thousands of years of empirical selection and breeding. Regions exhibiting strong signatures of genetic divergence associated with domestication and breeding were widespread in the genome with several major diversity losses in the pericentromeric regions. A locus on chromosome 5B carries a gene encoding a metal transporter (TdHMA3-B1) with a non-functional variant causing high accumulation of cadmium in grain. The high-cadmium allele, widespread among durum cultivars but undetected in wild emmer accessions, increased in frequency from domesticated emmer to modern durum wheat. The rapid cloning of TdHMA3-B1 rescues a wild beneficial allele and demonstrates the practical use of the Svevo genome for wheat improvement. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc. Western Grains Research Foundation Ministry of Agriculture - Saskatchewan Israel Science Foundation: 1137/17 031A536 FP7 Food, Agriculture and Fisheries, Biotechnology: DROPS ID244347 Natural Sciences and Engineering Research Council of Canada InterOmics PB05 Genome Prairie United States-Israel Binational Science Foundation: 2015409 Genome Canada Saskatchewan Canola Development Commission 2819103915 PIR01_00017 3060-21000-038-00-D We acknowledge the funding support of: the Italian Ministry of Education and Research with projects CNR Flagship InterOmics PB05 (L.M., A.C., G.S.), PON ELIXIR CNR-BiOmics PIR01_00017 (L.M., M.G., M.Mo.) and PON ISCOCEM (P.D.); CREA project Interomics (L.C.); Fondazione in rete per la ricerca agroalimentare AGER project From Seed to Pasta (R.T.); FP7-KBBE Project DROPS ID244347 (R.T.); Genome Canada (A.G.S., C.P.); the Western Grain Research Foundation (A.G.S., C.P.); the Manitoba Wheat and Barley Commission (A.G.S., C.P.); the Saskatchewan Wheat Development Commission (A.G.S., C.P.); the Alberta Wheat Development Commission (A.G.S., C.P.); the Saskatchewan Ministry of Agriculture (A.G.S., C.P.); the administrative support of Genome Prairie (A.G.S., C.P.); Canadian Triticum Applied Genomics -CTAG2-(A.G.S., C.P.); Binational Science Foundation grant no. 2015409 (I.H., A.D.); Israel Science Foundation grant no. 1137/17 (A.D.); USDA-Agricultural Research Service Current Research Information System project 3060-21000-038-00-D (J.D.F., S.S.X.); German Federal Ministry of Food and Agriculture grant no. 2819103915 (N.S., K.F.X.M.); German Ministry of Education and Research grant no. 031A536 (K.F.X.M.); and Natural Sciences and Engineering Council of Canada grant nos. SPG 336119-06 and RGPIN 92787 (G.J.T., C.P.). The authors are grateful to E. Elias (North Dakota State University) for providing nine DW cultivars, included in the Global Tetraploid Wheat Collection and E. Scarpella (University of Alberta, Edmonton, Canada) for assistance with confocal microscopy.