Automated and traceable processing for large-scale high-throughput sequencing facilities

Scaling up production in medium and large high-throughput sequencing facilities presents a number of challenges. As the rate of samples to process increases, manually performing and tracking the center’s operations becomes increasingly difficult, costly and error prone, while processing the massive amounts of data poses significant computational challenges. We present our ongoing work to automate and track all data-related procedures at the CRS4 Sequencing and Genotyping Platform, while integrating state-of-the-art processing technologies such as Hadoop, OMERO, iRODS, and Galaxy into our automated workflows. Currently, the core system is in its testing phase and it is on schedule to be in production use at CRS4 by May 2013. The results thus far obtained are encouraging and the authors are confident that the CRS4 Platform will increase its efficiency and capacity thanks to this system. In the near future, the integration components will be released as as open source software.23-24Pubblicat

Sistema di diagnosi collaborativa per cardiologia pediatrica

In questo rapporto viene dimostrato come è possibile costruire, partendo da tecnologie COTS, un sistema di telemedicina in grado di permettere ad un gruppo di esperti localizzati centralmente di fornire a personale clinico remoto supporto e guida in tempo reale per procedure ecografiche complesse. La prima applicazione del sistema è nella ecocardiologia neonatale, in particolare per la valutazione della pericolosità di potenziali patologie cardiache in neonati immediatamente dopo il parto

Soluzioni Open Source per la linearizzazione del problema di integrazione di applicativi nei sistemi informativi ospedalieri

2008-04-17Sardegna Ricerche, Edificio 2, Località Piscinamanna 09010 Pula (CA) - ItaliaPAAL 2008 - Pubblica Amministrazione Aperta e Libera: dalle tecnologie aperte alla libera circolazione dei contenuti digital

CyTest – An Innovative Open-source Platform for Training and Testing in Cythopathology

Abstract 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

Cohort profile: the Turin prostate cancer prognostication (TPCP) cohort

IntroductionProstate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research.MethodsThe TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study’s prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks.ResultsThe cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage.DiscussionThis study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa

OMERO.biobank: un approccio flessibile per la gestione dati nell’ambito della biologia sperimentale

Collana seminari interni 2012, Number 20120523.Strumenti in grado di gestire dati eterogenei e multidimensionali sono fondamentali nei settori di ricerca in cui il volume delle informazioni coinvolte tende a crescere velocemente e la natura delle stesse può cambiare o evolvere con il passare del tempo. OMERO (OME Remote Objects) è un’infrastruttura software sviluppata dal consorzio Open Microscopy dedicata alla gestione delle immagini provenienti da sorgenti di microscopia digitale dotata di un motore per la gestione dei dati altamente flessibile e formente personalizzabile. OMERO permette infatti di aggiungere nuovi concetti al set di modelli già presenti nell’infrastruttura di base e di estendere qualsiasi modello mediante un approccio simile all’ereditarietà tipico dei linguaggi di programmazione ad oggetti; l’aspetto della persistenza dei dati viene completamente mascherato da un middleware che permette di accedere alle informazioni mediante più linguaggi di programmazione (Python, Java e C++). Un’altra importante funzionalità è la possibilità di memorizzare informazioni strutturabili in formato tabulare in strutture dati estremamente efficienti basate su PyTables in grado di gestire quantità di dati che possono raggiungere dimensioni pari a 1 TeraByte. Durante questo seminario verrà presentato OMERO.biobank: un sistema orientato ai dati di tipo clinico e biologico sviluppato utilizzando ed estendendo le caratteristiche di base di OMERO descritte precedentemente. OMERO.biobank è uno strumento realizzato per la gestione di grosse quantità di informazioni estremamente eterogenee ed ha come scopo principale il supporto a studi longitudinali condotti su un numero molto elevato di pazienti e volontari (nell’ordine delle decine di migliaia di persone). Verranno illustrate le varie fasi che hanno portato alla creazione di tale sistema, dalla definizione e modellazione mediante formalismi computabili delle informazioni di cui si desidera tener traccia, alla scelta delle funzionalità offerte da OMERO in grado di garantire la migliore e più efficiente gestione delle varie tipologie di dati memorizzate. Si procederà inoltre ad illustrare la fase di sviluppo di un insieme di strumenti finalizzati all’utilizzo di OMERO.biobank come driver per operazioni di calcolo complesse implementate con moderni algoritmi di calcolo distribuito

A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data.

This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called "Constant Load" and "Constant Number of Records", with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes

Mind map of a set of annotations produced by a variant calling procedure.

<p>Dashed lines highlight recurrent blocks.</p

PyEHR: Query flow.

<p>The query evaluation process starts from nine o’ clock and proceeds clockwise.</p

PyEHR with Elasticsearch, CNR. Query time vs number of nodes for different types of count query at level 5.

<p>Each point is the mean query time, while the bar shows the standard deviation of the measurements.</p