ArticlePloS one2013
HLA typing from RNA-seq data using hierarchical read weighting [corrected].
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 74 citations in OpenAlex.
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Strategies for neoantigen screening and immunogenicity validation in cancer immunotherapy (Review).International journal of oncology · 2025Review
- Article
- A novel framework for human leukocyte antigen (HLA) genotyping using probe capture-based targeted next-generation sequencing and computational analysis.Computational and structural biotechnology journal · 2024Article
- Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis.Arthritis research & therapy · 2024Article
- Integrative HLA typing of tumor and adjacent normal tissue can reveal insights into the tumor immune response.BMC medical genomics · 2024Article
- A rigorous benchmarking of alignment-based HLA typing algorithms for RNA-seq data.bioRxiv : the preprint server for biology · 2024Article
- Comprehensive HLA Typing from a Current Allele Database Using Next-Generation Sequencing Data.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy.Frontiers in immunology · 2024Review
- Leading the pack: Best practices in comparative canine cancer genomics to inform human oncology.Veterinary and comparative oncology · 2023Review
- Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data.BMC genomics · 2023Article
- A Kmer-based paired-end readiScience · 2023Article
- A current and future perspective on T cell receptor repertoire profiling.Frontiers in genetics · 2023Review
- RNA2HLA: HLA-based quality control of RNA-seq datasets.Briefings in bioinformatics · 2021Article
- HLA RNA Sequencing With Unique Molecular Identifiers Reveals High Allele-Specific Variability in mRNA Expression.Frontiers in immunology · 2021Article
- Approaching Genetics Through the MHC Lens: Tools and Methods for HLA Research.Frontiers in genetics · 2021Review
- Benchmarking the Human Leukocyte Antigen Typing Performance of Three Assays and Seven Next-Generation Sequencing-Based Algorithms.Frontiers in immunology · 2021Article
- Computational Prediction and Validation of Tumor-Associated Neoantigens.Frontiers in immunology · 2020Review
- Bioinformatics Strategies, Challenges, and Opportunities for Next Generation Sequencing-Based HLA Genotyping.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2019Review
Corrections and comments
- Erratum issued
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Correctly matching the HLA haplotypes of donor and recipient is essential to the success of allogenic hematopoietic stem cell transplantation. Current HLA typing methods rely on targeted testing of recognized antigens or sequences. Despite advances in Next Generation Sequencing, general high throughput transcriptome sequencing is currently underutilized for HLA haplotyping due to the central difficulty in aligning sequences within this highly variable region. Here we present the method, HLAforest, that can accurately predict HLA haplotype by hierarchically weighting reads and using an iterative, greedy, top down pruning technique. HLAforest correctly predicts >99% of allele group level (2 digit) haplotypes and 93% of peptide-level (4 digit) haplotypes of the most diverse HLA genes in simulations with read lengths and error rates modeling currently available sequencing technology. The method is very robust to sequencing error and can predict 99% of allele-group level haplotypes with substitution rates as high as 8.8%. When applied to data generated from a trio of cell lines, HLAforest corroborated PCR-based HLA haplotyping methods and accurately predicted 16/18 (89%) major class I genes for a daughter-father-mother trio at the peptide level. Major class II genes were predicted with 100% concordance between the daughter-father-mother trio. In fifty HapMap samples with paired end reads just 37 nucleotides long, HLAforest predicted 96.5% of allele group level HLA haplotypes correctly and 83% of peptide level haplotypes correctly. In sixteen RNAseq samples with limited coverage across HLA genes, HLAforest predicted 97.7% of allele group level haplotypes and 85% of peptide level haplotypes correctly.
Indexed as
Identifiers
What OpenQuestion holds
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.