Search

The nomenclatures used to describe HLA and killer‐cell immunoglobulin‐like receptor (KIR) alleles distinguish unique nucleotide and peptide sequences, and patterns of expression, but are insufficient for describing genotyping results, as description of ambiguities and relations across loci require terminology beyond allele names. The genotype list (GL) String grammar describes genotyping results for genetic systems with defined nomenclatures, like HLA and KIR, documenting what is known and unknown about a given genotyping result. However, the accuracy of a GL String is dependent on the reference database version under which it was generated. Here, we describe the GL string code (GLSC) system, which associates each GL String with meta‐data describing the specific reference context in which the GL String was created, and in which it should be interpreted. GLSC is a defined syntax for exchanging GL Strings in the context of a specific gene‐family namespace, allele‐name code‐system, and pertinent reference database version. GLSC allows HLA and KIR genotyping data to be transmitted, parsed and interpreted in the appropriate context, in an unambiguous manner, on modern data‐systems, including Health Level 7 Fast Healthcare Interoperability Resource systems. Technical specification for GLSC can be found at https://glstring.org. [ABSTRACT FROM AUTHOR]

Background: It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data.Objective: To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG.Methods: BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models.Results: With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains.Interpretation: The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov . [ABSTRACT FROM AUTHOR]

Genotype list (GL) Strings use a set of hierarchical character delimiters to represent allele and genotype ambiguity in HLA and KIR genotypes in a complete and accurate fashion. A RESTful web service called genotype list service was created to allow users to register a GL string and receive a unique identifier for that string in the form of a URI. By exchanging URIs and dereferencing them through the GL service, users can easily transmit HLA genotypes in a variety of useful formats. The GL service was developed to be secure, scalable, and persistent. An instance of the GL service is configured with a nomenclature and can be run in strict or non-strict modes. Strict mode requires alleles used in the GL string to be present in the allele database using the fully qualified nomenclature. Non-strict mode allows any GL string to be registered as long as it is syntactically correct. The GL service source code is free and open source software, distributed under the GNU Lesser General Public License (LGPL) version 3 or later. [ABSTRACT FROM AUTHOR]

The development of next-generation sequencing (NGS) technologies for HLA and KIR genotyping is rapidly advancing knowledge of genetic variation of these highly polymorphic loci. NGS genotyping is poised to replace older methods for clinical use, but standard methods for reporting and exchanging these new, high quality genotype data are needed. The Immunogenomic NGS Consortium, a broad collaboration of histocompatibility and immunogenetics clinicians, researchers, instrument manufacturers and software developers, has developed the Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING) reporting guidelines. MIRING is a checklist that specifies the content of NGS genotyping results as well as a set of messaging guidelines for reporting the results. A MIRING message includes five categories of structured information – message annotation, reference context, full genotype, consensus sequence and novel polymorphism – and references to three categories of accessory information – NGS platform documentation, read processing documentation and primary data. These eight categories of information ensure the long-term portability and broad application of this NGS data for all current histocompatibility and immunogenetics use cases. In addition, MIRING can be extended to allow the reporting of genotype data generated using pre-NGS technologies. Because genotyping results reported using MIRING are easily updated in accordance with reference and nomenclature databases, MIRING represents a bold departure from previous methods of reporting HLA and KIR genotyping results, which have provided static and less-portable data. More information about MIRING can be found online at miring.immunogenomics.org . [ABSTRACT FROM AUTHOR]

We present an electronic format for exchanging data for HLA and KIR genotyping with extensions for next-generation sequencing (NGS). This format addresses NGS data exchange by refining the Histoimmunogenetics Markup Language (HML) to conform to the proposed Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING) reporting guidelines ( miring.immunogenomics.org ). Our refinements of HML include two major additions. First, NGS is supported by new XML structures to capture additional NGS data and metadata required to produce a genotyping result, including analysis-dependent (dynamic) and method-dependent (static) components. A full genotype, consensus sequence, and the surrounding metadata are included directly, while the raw sequence reads and platform documentation are externally referenced. Second, genotype ambiguity is fully represented by integrating Genotype List Strings, which use a hierarchical set of delimiters to represent allele and genotype ambiguity in a complete and accurate fashion. HML also continues to enable the transmission of legacy methods (e.g. site-specific oligonucleotide, sequence-specific priming, and Sequence Based Typing (SBT)), adding features such as allowing multiple group-specific sequencing primers, and fully leveraging techniques that combine multiple methods to obtain a single result, such as SBT integrated with NGS. [ABSTRACT FROM AUTHOR]

Aim The 17th International HLA and Immunogenetics Workshops (IHIW) focuses on the application of next generation sequencing (NGS) technologies for histocompatibility and immunogenomic clinical and research goals. However, the scope and sheer quantity of NGS data raises challenges for their analysis, collection, exchange and storage. The 17th IHIW has adopted a centralized approach to these issues, with the goals of developing tools, services and systems to capture and manage these NGS data, and leveraging the vast store of sequence and allele data in the IPD-IMGT/HLA Database. Methods We worked with NGS platform and software developers to define NGS typing reports that record NGS data in a uniform fashion. The IHIW database and data-storage facility houses typing reports, FASTQ files, subject and specimen data, and pedigrees, and manages the accounts and data-access privileges for 17th IHIW participants. IHIW database tools and services are scripted in the Perl, R or Python languages. Results We defined three distinct typing report formats [Histoimmunogenetics Markup Language (HML), IHIW eXtensible Markup Language (XML), and GenDx XML formats] that record HLA genotyping data and meta-data in equivalent fashion. Our tools convert HML and GenDx XML to IHIW XML, automate and streamline the submission of IHIW XML reports, validate each typing report upon submission, and automate the standardized reporting of novel polymorphisms. All 17th IHIW HLA data are standardized to IPD-IMGT/HLA Database version 3.25.0, with genotype data generated under other database versions converted to 3.25.0. Conclusions The 17th IHIW database applies our standards, tools and services to consume, validate and store structured NGS HLA data generated by GenDx, Illumina, Immucor, Omixon and One Lambda in an automated fashion. The HLA genotyping data and meta-data in these typing reports are validated and standardized for use in 17th IHIW projects, and the database itself will be available for future workshops. [ABSTRACT FROM AUTHOR]

Aim HLA typing via next generation sequencing (NGS) identifies polymorphisms in introns and untranslated regions (UTRs), and many novel synonymous and non-coding HLA sequence variants. NGS therefore poses challenges for the application of HLA nomenclature to sequence variants in non-exonic gene regions; to maintain the utility of the IMGT/HLA Database, full-gene sequences (all coding and non-coding regions) are required for the assignment of allele names to non-exon sequences, but not all NGS HLA typing methods generate full-gene sequences. Given this limitation, we have developed the “feature service”, a free, public web-service that accepts the submission of pre-curated sequences for individual features of HLA and KIR genes. Methods The feature service is available at http://feature.nmdp-bioinformatics.org , and allows full or partial HLA and KIR consensus sequence to be processed, accessioned and persisted so that each unique sequence for a particular locus, term (exon, intron, UTR, etc.) and rank (2,3,4, etc.) is assigned a unique identifier for analysis. Rank distinguishes features identified with the same term. For example, HLA-A exon 1 is defined as locus = HLA-A, term = exon, rank = 1; HLA-A exon 2 uses the same locus and term identifiers, but is assigned rank = 2; HLA-A intron 2, uses the same locus and rank, but is assigned term = intron, etc. The service uses JSON POST and GET operations to allow rapid, automated sequence data submission and retrieval, and any Gene Ontology term can be submitted as a feature service term. Results The service has been populated with 47,253 unique exon, intron and UTR feature sequences for the 14,473 alleles in IMGT/HLA Database version 3.24.0 and 8192 unique feature sequences for the 753 alleles in IPD-KIR Database version 2.6.1. Features representing full-gene NGS HLA genotyping for 25,000 samples generated by two independent laboratories have been submitted as well. Conclusions By identifying the locus, term, rank and accession number for the gene feature sequences of each allele, the gene sequences of known and novel alleles can be accurately described in the absence of HLA nomenclature. By sharing HLA and KIR gene sequences in this way, they can be applied for clinical and research purposes prior to curation by the IPD databases. [ABSTRACT FROM AUTHOR]