Abstract
Extreme complexity in the Human Leukocyte Antigens (HLA) system and its nomenclature makes it difficult to interpret and integrate relevant information for HLA associations with diseases, Adverse Drug Reactions (ADR) and Transplantation. PubMed search displays ~ 146,000 studies on HLA reported from diverse locations. Currently, IPD-IMGT/HLA (Robinson et al., Nucleic Acids Research 48:D948–D955, 2019) database houses data on 28,320 HLA alleles. We developed an automated pipeline with a unified graphical user interface HLA-SPREAD that provides a structured information on SNPs, Populations, REsources, ADRs and Diseases information. Information on HLA was extracted from ~ 28 million PubMed abstracts extracted using Natural Language Processing (NLP). Python scripts were used to mine and curate information on diseases, filter false positives and categorize to 24 tree hierarchical groups and named Entity Recognition (NER) algorithms followed by semantic analysis to infer HLA association(s). This resource from 109 countries and 40 ethnic groups provides interesting insights on: markers associated with allelic/haplotypic association in autoimmune, cancer, viral and skin diseases, transplantation outcome and ADRs for hypersensitivity. Summary information on clinically relevant biomarkers related to HLA disease associations with mapped susceptible/risk alleles are readily retrievable from HLASPREAD. The resource is available at URL http://hla-spread.igib.res.in/. This resource is first of its kind that can help uncover novel patterns in HLA gene-disease associations.
Highlights
Human Leukocyte Antigen (HLA) locus consists of six classical genes (HLA-A, −B, −C, −DP, −DQ and -DR) that play an important role in eliciting immune response against pathogens [24] and three non-classical genes (HLA-E, −F and -G) that interact with Natural Killer cells to regulate virus-infected and malignant cells [25]
One of the key highlight of the analysis is that we were able to reduce the complexity of HLA nomenclature by converting all existing old nomenclature in literature to the current format
We listed the ARD’s across populations and identified HLA alleles used as a biomarker
Summary
Human Leukocyte Antigen (HLA) locus consists of six classical genes (HLA-A, −B, −C, −DP, −DQ and -DR) that play an important role in eliciting immune response against pathogens [24] and three non-classical genes (HLA-E, −F and -G) that interact with Natural Killer cells to regulate virus-infected and malignant cells [25]. With the rapid increase in biomedical data, HLA alleles and their associations in multiple diseases, it becomes imperative to create a platform with structured information to query and retrieve relevant information. For understanding and integrating the observations from HLA studies we require knowledge of genomic datasets, i.e. diseases, SNPs, drugs, populations, and ethnic groups along with an understanding of the relationship between them. We have attempted mapping all the old nomenclature to the current allele names This dictionary includes few generic HLA keywords like HLA class I, HLA class II, HLA linked and HLA associated. To ensure the accuracy of the algorithm, the allele and disease keywords present in each sentence were replaced with @ GENE and @DISEASE tags and a parse tree was generated using StanfordCoreNLP python module (Stanford-corenlp-full-2018-10-05 package).
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