Bioinformatics Architecture for Integrating Genomics Data into Electronic Health Records.

The adequate management of patients' genomic information is essential for any health institution pursuing the Precision Medicine model. Here we approach a bioinformatic architecture that allows the Institution to store its whole genetic test data in a scalable database, and also the integration of that genetic data with the Electronic Health Record through a Clinical Decision Support System. The system complements patient care by suggesting referral to genetic counseling for patients who are potentially at risk of hereditary breast/ovarian cancer, and allowing for proper follow-up of patients with pathogenic variants in BRCA1 or BRCA2 genes. The implemented solution uses the FHIR standard and genetic nomenclatures from the Human Genome Variation Society and the HUGO Gene Nomenclature Committee. The architecture is flexible enough to allow any other health institution to integrate -to their information ecosystem- the whole solution or some of the modules according to its degree of digitization progress.

Development, Implementation and Initial Results of CDSS Recommendations for Patients at Risk of Hereditary Breast Cancer.

Breast cancer represents 23% of all cancers diagnosed among women each year. BRCA1 and BRCA2 are tumor suppressor genes related to the most frequent form of hereditary breast and ovarian cancer, as well as other types of cancer. The aim of this work is to describe the development of Clinical Decision Support Systems (CDSS) for referral to genetic counseling in patients at increased risk of pathogenic variants in BRCA1 and BRCA2, and to describe results during the pilot study implementation (from January 5, 2021 to March 5, 2021). To achieve integration and system interoperability, we used FHIR and CDS-Hooks within the CDSS development. A total of 142 alerts were triggered by the system for 72 physicians in 98 patients. Results showed an acceptance rate for the recommendation of 2.1%, which could improve using intrusive alerts in all of the hooks.

Automatic Integration of Clinical and Genetic Data Using cBioPortal.

Precision medicine seeks to improve the prevention, diagnosis and treatment of patients based on genetic characteristics unique to each person. In oncology, therapeutic decisions have been established based on the genomic characteristics of each patient's tumor. Data integration is key for the successful implementation of precision medicine since it is necessary for both studying a large volume of data from different sources and working with an interdisciplinary and translational vision. In this work, a bioinformatic process was successfully implemented that allows the integration of patients' genomic data, from two molecular biology laboratories, with their clinical data provided by their electronic medical records. For this, the REDCap data capture software, the cBioPortal visualization and analysis software, and a computer tool developed to automate the processing and annotation of the information in REDCap were used to be included in cBioPortal, for the "Map of Tumor Genomic Actionability of Argentina" project.

Building a Genome Archiving and Communication System Integrated into a Health Information Systems.

The use of next-generation sequencing technologies in clinical practice has increased the volume of information that must be stored, processed, and interpreted. In this work, a description of the implementation of a genomic communication and archiving system (GACS) is presented. This GACS will allow us to store, share and search the genomic files and genetic variants obtained as a result of genetic laboratory tests.

APRANK: Computational Prioritization of Antigenic Proteins and Peptides From Complete Pathogen Proteomes.

Availability of highly parallelized immunoassays has renewed interest in the discovery of serology biomarkers for infectious diseases. Protein and peptide microarrays now provide a rapid, high-throughput platform for immunological testing and validation of potential antigens and B-cell epitopes. However, there is still a need for tools to prioritize and select relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize potentially antigenic proteins and peptides in a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We trained and tested this method with a number of bacteria and protozoa causing human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). We have evaluated this integrative method using non-parametric ROC-curves and made an unbiased validation using Onchocerca volvulus as an independent data set. We found that APRANK is successful in predicting antigenicity for all pathogen species tested, facilitating the production of antigen-enriched protein subsets. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.