vcf2fhir: a utility to convert VCF files into HL7 FHIR format for genomics-EHR integration

Abstract

Background VCF formatted files are the lingua franca of next-generation sequencing, whereas HL7 FHIR is emerging as a standard language for electronic health record interoperability. A growing number of FHIR-based clinical genomics applications are emerging. Here, we describe an open source utility for converting variants from VCF format into HL7 FHIR format. Results vcf2fhir converts VCF variants into a FHIR Genomics Diagnostic Report. Conversion translates each VCF row into a corresponding FHIR-formatted variant in the generated report. In scope are simple variants (SNVs, MNVs, Indels), along with zygosity and phase relationships, for autosomes, sex chromosomes, and mitochondrial DNA. Input parameters include VCF file and genome build (‘GRCh37’ or ‘GRCh38’); and optionally a conversion region that indicates the region(s) to convert, a studied region that lists genomic regions studied by the lab, and a non-callable region that lists studied regions deemed uncallable by the lab. Conversion can be limited to a subset of VCF by supplying genomic coordinates of the conversion region(s). If studied and non-callable regions are also supplied, the output FHIR report will include ‘region-studied’ observations that detail which portions of the conversion region were studied, and of those studied regions, which portions were deemed uncallable. We illustrate the vcf2fhir utility via two case studies. The first, 'SMART Cancer Navigator', is a web application that offers clinical decision support by linking patient EHR information to cancerous gene variants. The second, 'Precision Genomics Integration Platform', intersects a patient's FHIR-formatted clinical and genomic data with knowledge bases in order to provide on-demand delivery of contextually relevant genomic findings and recommendations to the EHR. Conclusions Experience to date shows that the vcf2fhir utility can be effectively woven into clinically useful genomic-EHR integration pipelines. Additional testing will be a critical step towards the clinical validation of this utility, enabling it to be integrated in a variety of real world data flow scenarios. For now, we propose the use of this utility primarily to accelerate FHIR Genomics understanding and to facilitate experimentation with further integration of genomics data into the EHR.

Introducing HL7 FHIR Genomics Operations: a developer-friendly approach to genomics-EHR integration

“…We generally think of two main types of pilots—those that implement operations against a genomic data repository, and those that use operations in an application (eg, an app or a CDS pipeline). Early pilots, such as the Face Sheet app (described here 32 ) and the PGx CDS pipeline 46 demonstrated the feasibility of querying a genomic data store in real time. The reference implementation 30 was itself a sizable pilot, providing valuable insights into optimal operation design and normalization strategies.…”

“…You can experiment with predefined queries (see postman collection) or create your own queries based on available data; the “Examples and Exercises” page provides scenarios that demonstrate various capabilities of each Operation. Exercises are provided for those that want to test their understanding; the “Software Utilities” page describes software utilities used primarily to help load data into the database that underlies the reference implementation (eg, vcf2json uses vcf2fhir 32 logic to import VCF data) and to support fast normalization (eg, by replicating portions of NCBI SPDI variation services 13 ); the “Genomics Apps” page provides some simple genomics applications built using the FHIR Genomics Operations; the “Replicating the Reference Implementation” page describes how to fully replicate the reference implementation (code and data). The reference implementation is written in python and deployed on Heroku, 33 with data stored in MongoDB.…”

Towards the Representation of Genomic Data in HL7 FHIR and OMOP CDM

“…But, this problem can be solved using a self-generated vocabulary of desired databases. Although it is already possible to store genomic data using FHIR standards and apply some precision medicine modules on the data using SMART on FHIR 12 [5], the mainly supported FHIR version is DSTU2, which is not compatible with the FHIR version R4 that we are using. Therefore we suggest OMOP CDM as the standard to store the genomic data, use this data for further analysis and thus ensure our active role in medical studies as part of the OHDSI community in the future.…”

“…For example, the Observation-genetics profile is extended from Observation resource and is used to interpret variants from the sequence resource [11]. A tool, namely VCF2FHIR, is implemented based on the profiles, which are defined in General Genomic Reporting from Genomics Reporting Implementation Guide for converting VCF data into HL7 FHIR R4 format [12]. This tool is also part of the SMART Cancer Navigator, which needs to be connected to an EHR system [13].…”

Re-analysis of genomic data: An overview of the mechanisms and complexities of clinical adoption

“…67 Although VCF files may be more manageable, this format was designed for a research setting 68 As a result, there are concerns about how the variability associated with VCF files will work within modern, interoperable health care systems. 69 Resources to bridge this gap are being developed; 69 however, for the reanalysis of genomic data to become mainstream, there must be synthesis of bioinformatics and health informatics.…”