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.

Multiomix: a cloud-based platform to infer cancer genomic and epigenomic events associated with gene expression modulation.

MOTIVATION: Large-scale cancer genome projects have generated genomic, transcriptomic, epigenomic and clinicopathological data from thousands of samples in almost every human tumor site. Although most omics data and their associated resources are publicly available, its full integration and interpretation to dissect the sources of gene expression modulation require specialized knowledge and software. RESULTS: We present Multiomix, an interactive cloud-based platform that allows biologists to identify genetic and epigenetic events associated with the transcriptional modulation of cancer-related genes through the analysis of multi-omics data available on public functional genomic databases or user-uploaded datasets. Multiomix consists of an integrated set of functions, pipelines and a graphical user interface that allows retrieval, aggregation, analysis and visualization of different omics data sources. After the user provides the data to be analyzed, Multiomix identifies all significant correlations between mRNAs and non-mRNA genomics features (e.g. miRNA, DNA methylation and CNV) across the genome, the predicted sequence-based interactions (e.g. miRNA-mRNA) and their associated prognostic values. AVAILABILITY AND IMPLEMENTATION: Multiomix is available at https://www.multiomix.org. The source code is freely available at https://github.com/omics-datascience/multiomix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BioPlat: a software for human cancer biomarker discovery.

SUMMARY: Development of effective tools such as oligo-microarrays and next-generation sequencing methods for monitoring gene expression on a large scale has resulted in the discovery of gene signatures with prognostic/predictive value in various malignant neoplastic diseases. However, with the exponential growth of gene expression databases, biologists are faced with the challenge of extracting useful information from these repositories. Here, we present a software package, BioPlat (Biomarkers Platform), which allows biologists to identify novel prognostic and predictive cancer biomarkers based on the data mining of gene expression signatures and gene expression profiling databases. BioPlat has been designed as an easy-to-use and flexible desktop software application, which provides a set of analytical tools related to data extraction, preprocessing, filtering, gene expression signature calculation, in silico validation, feature selection and annotation that leverage the integration and reuse of gene expression signatures in the context of follow-up data. AVAILABILITY AND IMPLEMENTATION: BioPlat is a platform-independent software implemented in Java and supported on GNU/Linux and MS Windows, which is freely available for download at http://www.cancergenomics.net.