Matteo Gabetta

Transcriptome based identification of mouse cumulus cell markers that predict the developmental competence of their enclosed antral oocytes

BackgroundThe cumulus cells (CCs) enveloping antral and ovulated oocytes have been regarded as putative source of non-invasive markers of the oocyte developmental competence. A number of studies have indeed observed a correlation between CCs gene expression, embryo quality, and final pregnancy outcome. Here, we isolated CCs from antral mouse oocytes of known developmental incompetence (NSN-CCs) or competence (SN-CCs) and compared their transcriptomes with the aim of identifying distinct marker transcripts.ResultsGlobal gene expression analysis highlighted that both types of CCs share similar transcriptomes, with the exception of 422 genes, 97.6% of which were down-regulated in NSN-CCs vs. SN-CCs. This transcriptional down-regulation in NSN-CCs was confirmed by qRT-PCR analysis of CC-related genes (Has2, Ptx3, Tnfaip6 and Ptgs2). Only ten of the 422 genes were up-regulated with Amh being the most up-regulated in NSN-CCs, with an average 4-fold higher expression when analysed by qRT-PCR.ConclusionsThe developmental incompetence (NSN) or competence (SN) of antral oocytes can be predicted using transcript markers expressed by their surrounding CCs (i.e., Has2, Ptx3, Tnfaip6, Ptgs2 and Amh). Overall, the regulated nature of the group of genes brought out by whole transcriptome analysis constitutes the molecular signature of CCs associated either with developmentally incompetent or competent oocytes and may represent a valuable resource for developing new molecular tools for the assessment of oocyte quality and to further investigate the complex bi-directional interaction occurring between CCs and oocyte.

Text Mining approaches for automated literature knowledge extraction and representation.

Due to the overwhelming volume of published scientific papers, information tools for automated literature analysis are essential to support current biomedical research. We have developed a knowledge extraction tool to help researcher in discovering useful information which can support their reasoning process. The tool is composed of a search engine based on Text Mining and Natural Language Processing techniques, and an analysis module which process the search results in order to build annotation similarity networks. We tested our approach on the available knowledge about the genetic mechanism of cardiac diseases, where the target is to find both known and possible hypothetical relations between specific candidate genes and the trait of interest. We show that the system i) is able to effectively retrieve medical concepts and genes and ii) plays a relevant role assisting researchers in the formulation and evaluation of novel literature-based hypotheses.

BigQ: a NoSQL based framework to handle genomic variants in i2b2

BackgroundPrecision medicine requires the tight integration of clinical and molecular data. To this end, it is mandatory to define proper technological solutions able to manage the overwhelming amount of high throughput genomic data needed to test associations between genomic signatures and human phenotypes. The i2b2 Center (Informatics for Integrating Biology and the Bedside) has developed a widely internationally adopted framework to use existing clinical data for discovery research that can help the definition of precision medicine interventions when coupled with genetic data. i2b2 can be significantly advanced by designing efficient management solutions of Next Generation Sequencing data.ResultsWe developed BigQ, an extension of the i2b2 framework, which integrates patient clinical phenotypes with genomic variant profiles generated by Next Generation Sequencing. A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations. We report an evaluation of the query performance of our system on more than 11 million variants, showing that the implemented solution scales linearly in terms of query time and disk space with the number of variants.ConclusionsIn this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data. The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations.

A Unified Medical Language System (UMLS) based system for Literature-Based Discovery in medicine.

Literature-Based Discovery (LBD) is a technique that can be used in translational research to connect the very sparse and huge information available in scientific publications in order to extract new knowledge. This paper presents an LBD system based on the open discovery paradigm exploiting NLP techniques and UMLS medical concepts mapping, to provide a set of tools useful to discover unknown relationships. The system has been evaluated on the problem of discovering new candidate genes potentially related to dilated cardiomyopathies (DCM), and can be used in any medical context to connect different type of concepts. The validation of the system involves reproducing the discovery of genes currently associated to DCM. Validation showed that the system is able to discover many gene-disease associations by using the literature available before their first publication in a scientific article.

Combining clinical and genomics queries using i2b2 – Three methods

We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical “Big Data” challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.

Engineering Principles in Biomedical Informatics

Engineering is one of the main pillars of biomedical informatics, providing design principles, methods, and tools for the effective implementation of computational solutions in health care. The basic engineering approach consists of a number of phases, comprising modeling, designing, testing, and verifying. Such an approach has become widely applied in biomedical informatics. In this chapter, we analyze three different engineering approaches crucial for biomedical informatics: (1) the design of computational solutions that use the Unified Modeling Language (UML); (2) the representation, simulation, and learning of careflow systems; and, finally, (3) the role that engineering has in data mining, with a specific focus on temporal data and dynamical systems, as well as on principles for engineering the data analysis process.

Supporting Translational Research on Inherited Cardiomyopathies through Information Technology

OBJECTIVES The INHERITANCE project, funded by the European Commission, is aimed at studying genetic or inherited Dilated cardiomyopathies (DCM) and at understanding the impact and management of the disease within families that suffer from heart conditions that are caused by DCMs. The biomedical informatics research activity of the project aims at implementing information technology solutions to support the project team in the different phases of their research, in particular in genes screening prioritization and new gene-disease association discovery. METHODS In order to manage the huge quantity of scientific, clinical and patient data generated by the project several advanced biomedical informatics tools have been developed. The paper describes a layer of software instruments to support translation of the results of the project in clinical practice as well as to support the scientific discovery process. This layer includes data warehousing, intelligent querying of the phenotype data, integrated search of biological data and knowledge repositories, text mining of the relevant literature, and case based reasoning. RESULTS At the moment, a set of 1,394 patients and 9,784 observations has been stored into the INHERITANCE data warehouse. The literature database contains more than 1,100,000 articles retrieved from the Pubmed and generically related to cardiac diseases, already analyzed for extracting medical concepts and genes. CONCLUSIONS After two years of project the data warehouse has been completely set up and the text mining tools for automatic literature analysis have been implemented and tested. A first prototype of the decision support tool for knowledge discovery and gene prioritization is available, but a more complete release is still under development.

ONCO-i2b2: improve patients selection through case-based information retrieval techniques

The University of Pavia (Italy) and the IRCCS Fondazione Salvatore Maugeri hospital in Pavia have recently started an information technology initiative to support clinical research in oncology called ONCO-i2b2. This project aims at supporting translational research in oncology and exploits the software solutions implemented by the Informatics for Integrating Biology and the Bed-side (i2b2) research center. The ONCO-i2b2 software is designed to integrate the i2b2 infrastructure with the hospital information system, with the pathology unit and with a cancer biobank that manages both plasma and cancer tissue samples. Exploiting the medical concepts related to each patient, we have developed a novel data mining procedure that allows researchers to easily identify patients similar to those found with the i2b2 query tool, so as to increase the number of patients, compared to the patient set directly retrieved by the query. This allows physicians to obtain additional information that can support new insights in the study of tumors.

Information technology solutions to support translational research on inherited cardiomyopathies.

The INHERITANCE project, funded by the European Commission, is aimed at studying genetic or inherited Dilated cardiomyopathies (DCM) and at understanding the impact and management of the condition within families that suffer from heart conditions that are caused by DCMs. The project is supported by a number of advanced biomedical informatics tools, including data warehousing, automated literature search and decision support. The paper describes the design of these tools and the current status of implementation.

Translational bioinformatics: challenges and opportunities for case-based reasoning and decision support

Translational bioinformatics is bioinformatics applied to human health. Although, up to now, its main focus has been to support molecular medicine research, translational bioinformatics has now the opportunity to design clinical decision support systems based on the combination of -omics data and internet-based knowledge resources. The paper describes the state-of-art of translational bioinformatics highlighting challenges and opportunities for decision support tools and case-based reasoning. It finally reports the design of a new system for supporting diagnosis in dilated cardiomyopathy. The system is able to combine text mining, literature search and case-based retrieval.