The RD-Connect Genome-Phenome Analysis Platform: Accelerating diagnosis, research, and gene discovery for rare diseases.

Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.

Optimizing parameters of an open-source airway segmentation algorithm using different CT images.

BACKGROUND: Computed tomography (CT) helps physicians locate and diagnose pathological conditions. In some conditions, having an airway segmentation method which facilitates reconstruction of the airway from chest CT images can help hugely in the assessment of lung diseases. Many efforts have been made to develop airway segmentation algorithms, but methods are usually not optimized to be reliable across different CT scan parameters. METHODS: In this paper, we present a simple and reliable semi-automatic algorithm which can segment tracheal and bronchial anatomy using the open-source 3D Slicer platform. The method is based on a region growing approach where trachea, right and left bronchi are cropped and segmented independently using three different thresholds. The algorithm and its parameters have been optimized to be efficient across different CT scan acquisition parameters. The performance of the proposed method has been evaluated on EXACT'09 cases and local clinical cases as well as on a breathing pig lung phantom using multiple scans and changing parameters. In particular, to investigate multiple scan parameters reconstruction kernel, radiation dose and slice thickness have been considered. Volume, branch count, branch length and leakage presence have been evaluated. A new method for leakage evaluation has been developed and correlation between segmentation metrics and CT acquisition parameters has been considered. RESULTS: All the considered cases have been segmented successfully with good results in terms of leakage presence. Results on clinical data are comparable to other teams' methods, as obtained by evaluation against the EXACT09 challenge, whereas results obtained from the phantom prove the reliability of the method across multiple CT platforms and acquisition parameters. As expected, slice thickness is the parameter affecting the results the most, whereas reconstruction kernel and radiation dose seem not to particularly affect airway segmentation. CONCLUSION: The system represents the first open-source airway segmentation platform. The quantitative evaluation approach presented represents the first repeatable system evaluation tool for like-for-like comparison between different airway segmentation platforms. Results suggest that the algorithm can be considered stable across multiple CT platforms and acquisition parameters and can be considered as a starting point for the development of a complete airway segmentation algorithm.

Remote visualization of large-scale genomic alignments for collaborative clinical research and diagnosis of rare diseases.

The Solve-RD project objectives include solving undiagnosed rare diseases (RD) through collaborative research on shared genome-phenome datasets. The RD-Connect Genome-Phenome Analysis Platform (GPAP), for data collation and analysis, and the European Genome-Phenome Archive (EGA), for file storage, are two key components of the Solve-RD infrastructure. Clinical researchers can identify candidate genetic variants within the RD-Connect GPAP and, thanks to the developments presented here as part of joint ELIXIR activities, are able to remotely visualize the corresponding alignments stored at the EGA. The Global Alliance for Genomics and Health (GA4GH) htsget streaming application programming interface (API) is used to retrieve alignment slices, which are rendered by an integrated genome viewer (IGV) instance embedded in the GPAP. As a result, it is no longer necessary for over 11,000 datasets to download large alignment files to visualize them locally. This work highlights the advantages, from both the user and infrastructure perspectives, of implementing interoperability standards for establishing federated genomics data networks.

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data.

Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics.

A guide to writing systematic reviews of rare disease treatments to generate FAIR-compliant datasets: building a Treatabolome.

BACKGROUND: Rare diseases are individually rare but globally affect around 6% of the population, and in over 70% of cases are genetically determined. Their rarity translates into a delayed diagnosis, with 25% of patients waiting 5 to 30 years for one. It is essential to raise awareness of patients and clinicians of existing gene and variant-specific therapeutics at the time of diagnosis to avoid that treatment delays add up to the diagnostic odyssey of rare diseases' patients and their families. AIMS: This paper aims to provide guidance and give detailed instructions on how to write homogeneous systematic reviews of rare diseases' treatments in a manner that allows the capture of the results in a computer-accessible form. The published results need to comply with the FAIR guiding principles for scientific data management and stewardship to facilitate the extraction of datasets that are easily transposable into machine-actionable information. The ultimate purpose is the creation of a database of rare disease treatments ("Treatabolome") at gene and variant levels as part of the H2020 research project Solve-RD. RESULTS: Each systematic review follows a written protocol to address one or more rare diseases in which the authors are experts. The bibliographic search strategy requires detailed documentation to allow its replication. Data capture forms should be built to facilitate the filling of a data capture spreadsheet and to record the application of the inclusion and exclusion criteria to each search result. A PRISMA flowchart is required to provide an overview of the processes of search and selection of papers. A separate table condenses the data collected during the Systematic Review, appraised according to their level of evidence. CONCLUSIONS: This paper provides a template that includes the instructions for writing FAIR-compliant systematic reviews of rare diseases' treatments that enables the assembly of a Treatabolome database that complement existing diagnostic and management support tools with treatment awareness data.

From research to rapid response: mass COVID-19 testing by volunteers at the Centre for Genomic Regulation.

The COVID-19 pandemic has posed and is continuously posing enormous societal and health challenges worldwide. The research community has mobilized to develop novel projects to find a cure or a vaccine, as well as to contribute to mass testing, which has been a critical measure to contain the infection in several countries. Through this article, we share our experiences and learnings as a group of volunteers at the Centre for Genomic Regulation (CRG) in Barcelona, Spain. As members of the ORFEU project, an initiative by the Government of Catalonia to achieve mass testing of people at risk and contain the epidemic in Spain, we share our motivations, challenges and the key lessons learnt, which we feel will help better prepare the global society to address similar situations in the future.

Evaluation of a novel tracking system in a breathing lung model.

We present the evaluation of an electromagnetic position tracking system for use with virtual bronchoscopy systems. Our system utilises a planar magnetic coil array and commercially available search coil sensors. Experimental results show the EM tracking accuracy to be in the range of 11.5mm, which is comparable to both commercial and research systems. The use of a bench-top breathing lung model is used to verify system operation in the in vitro setting. A novel fiducial-free registration method is implemented to reduce errors resulting from inaccurate landmark identification commonly associated with point-based registration. After registration, there is good agreement between the measured position of the sensor probe during endoscopic navigation and the lung airways as visualised in a 3D model of the phantom.