Search

This deliverable aims at designing and evaluating a curation support system to help clinicians to decide which treatments should be considered given a set of somatic/germline mutations and some particular clinical conditions (e.g. diagnosis). It builds on top of previous deliverables from WP5, in particular D5.1 [1], as well as on the services delivered by WP1-3, such as the WP1 dataset search services and WP3 content normalization services. In the use-case scenario described in D5.1, the end-user was first invited to select some datasets. User queries are then distributed across a federated Beacon network, comprising data from UKBB (EMBL-EBI), CoLaus (Switzerland), H3 (South Africa) and CHILD (Canada). Beyond that, the search pipeline now integrates subtask T5.3.2 (Scoring service to assess pathogenicity scales of variants) directly as an information displayed to the user, but also as a feature of the treatment recommendation function. The scoring and care planning services are evaluated based on various scoring functions (e.g., cohort distributions, SIFT, Polyphen, literature counts). The evaluation shows a moderate association between the pathogenetic scoring proposed by T5.3.2 and the baseline scores selected for comparison. We also report on the efficiency of the pipeline.

CINECA aims to support federated queries and analyses of distributed cohorts across continents. But human health datasets are extremely diverse; many different types of data are collected for many different kinds of health studies by many different health research communities. As a result, different cohort datasets often use different ontologies to describe similar kinds of entities, or represent concepts, such as genomic variation differently. CINECA must span this diversity of data representations in order to achieve its goals of connecting health research cohort data. The work of WP3 partially addresses discoverability of datasets by defining a standard minimal cohort-level data representation which will be common across all cohorts; but that does not address cohort-level data that falls outside of the minimal common data model, nor does it address the representation of patient-level data. WP1’s role is to design and deploy API access to both cohort- and patient-level data, and a fundamental functionality of the infrastructure is to allow the user to find the appropriate dataset independently of the ontology used to map locally the different cohorts or indifferently of the format and syntax used to describe the variants. This report describes the work done on query expansion, by implementing and demonstrating a query expansion service API that improves findability and searchability of distributed cohort data. Multiple kinds of query expansions are available for enabling further data integration and interoperability, including horizontal expansion, i.e., across ontological systems, and vertical expansion, i.e., within sublevels of the same ontological resource.

With new generation sequencing, software tools are essential to efficiently integrate molecular biological data around phenotypic concepts. The purpose of this work was to enhance the access to proteins from diseases. The first step consisted in creating an automatic tool to link disease-related human proteins and variants to a medical controlled vocabulary and make this mapping accessible through a web interface. Then another tool was proposed to filter networks of protein-protein interactions with single clinical traits (phenotypes) found in Mendelian disorders. The aim was to help formulating hypotheses on the function of proteins and interactions by isolating a spatio-temporal context, crucial given the modular nature of cell biology.