ArticleBioinformatics (Oxford, England)2020
arcasHLA: high-resolution HLA typing from RNAseq.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 1 of them a synthesis that pooled it.
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Who cites it
130 citing papers in PubMed, 1 synthesis or guideline pooled it, 208 citations in OpenAlex.
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- Decoding neoantigen-encoding tumor-specific transcripts unveils a shared target reservoir for immunotherapy in hepatocellular carcinoma.Journal for immunotherapy of cancer · 2026Article
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- Lung mucosal and systemic responses at single-cell resolution in an aerosolized Mycobacterium bovis BCG human challenge model.Cell reports. Medicine · 2026Article
- HLA class I expression shapes the tumor immune microenvironment and influences prognosis in prostate cancer.Prostate cancer and prostatic diseases · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Attention-based deep learning for analysis of pathology images and gene expression data in lung squamous premalignant lesions.Genome medicine · 2026Article
- Review
- ImmuSeeker: deep mining of immune-related gene family signatures through lineage reconstruction.Genome biology · 2026Article
- Tumour-intrinsic features shape T cell differentiation through precursor to symptomatic multiple myeloma.Nature communications · 2026Article
- Epitope Generation Gateway: A biological context-dependent approach to cancer vaccine development.GigaScience · 2026Article
- Transcriptome profile of delayed mortality in patients with invasive group A Streptococcal disease.PloS one · 2026Article
- A reproducible pipeline for TCR β-chain repertoire analysis from thoracic tumour RNA-seq and ICI-associated cardiac immunotoxicity.Frontiers in immunology · 2026Article
70 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
motivationThe human leukocyte antigen (HLA) locus plays a critical role in tissue compatibility and regulates the host response to many diseases, including cancers and autoimmune di3orders. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional modifications and bias due to amplification.
resultsHere, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA-sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two-field resolution for Class I genes, and over 99.7% for Class II. Furthermore, we evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. AVAILABILITY AND IMPLEMENTATION: arcasHLA is available at https://github.com/RabadanLab/arcasHLA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.