ArticleFrontiers in immunology2016
IMPre: An Accurate and Efficient Software for Prediction of T- and B-Cell Receptor Germline Genes and Alleles from Rearranged Repertoire Data.
Article in Frontiers in immunology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 44 citations in OpenAlex.
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- Toll-like receptor 7/8 agonist stimulation rapidly skews the antibody repertoire of B cells in non-human primates.Frontiers in immunology · 2025Article
- Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR.Genome research · 2024Article
- Novel rhesus macaque immunoglobulin germline genes identified by three sequencing approaches.Frontiers in immunology · 2024Article
- AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets.Immunoinformatics (Amsterdam, Netherlands) · 2023Article
- Adaptive immune receptor genotyping using theFrontiers in immunology · 2023Article
- Analysis of IGH allele content in a sample group of rheumatoid arthritis patients demonstrates unrevealed population heterogeneity.Frontiers in immunology · 2023Article
- Adaptive Immune Receptor Repertoire (AIRR) Community Guide to TR and IG Gene Annotation.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Individualized VDJ recombination predisposes the available Ig sequence space.Genome research · 2021Article
- Diversity in immunogenomics: the value and the challenge.Nature methods · 2021Article
- Trace Reconstruction Problems in Computational Biology.IEEE transactions on information theory · 2021Article
- Novel Allele Detection Tool Benchmark and Application With Antibody Repertoire Sequencing Dataset.Frontiers in immunology · 2021Article
- Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species.PLoS computational biology · 2020Article
- OGRDB: a reference database of inferred immune receptor genes.Nucleic acids research · 2020Article
- Poorly Expressed Alleles of Several Human Immunoglobulin Heavy Chain Variable Genes are Common in the Human Population.Frontiers in immunology · 2020Article
- A Novel Framework for Characterizing Genomic Haplotype Diversity in the Human Immunoglobulin Heavy Chain Locus.Frontiers in immunology · 2020Article
- Identification of Subject-Specific Immunoglobulin Alleles From Expressed Repertoire Sequencing Data.Frontiers in immunology · 2019Article
- Article
- The Pipeline Repertoire for Ig-Seq Analysis.Frontiers in immunology · 2019Review
- Inferred Allelic Variants of Immunoglobulin Receptor Genes: A System for Their Evaluation, Documentation, and Naming.Frontiers in immunology · 2019Review
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale study of the properties of T-cell receptor (TCR) and B-cell receptor (BCR) repertoires through next-generation sequencing is providing excellent insights into the understanding of adaptive immune responses. Variable(Diversity)Joining [V(D)J] germline genes and alleles must be characterized in detail to facilitate repertoire analyses. However, most species do not have well-characterized TCR/BCR germline genes because of their high homology. Also, more germline alleles are required for humans and other species, which limits the capacity for studying immune repertoires. Herein, we developed "Immune Germline Prediction" (IMPre), a tool for predicting germline V/J genes and alleles using deep-sequencing data derived from TCR/BCR repertoires. We developed a new algorithm, "Seed_Clust," for clustering, produced a multiway tree for assembly and optimized the sequence according to the characteristics of rearrangement. We trained IMPre on human samples of T-cell receptor beta (TRB) and immunoglobulin heavy chain and then tested it on additional human samples. Accuracy of 97.7, 100, 92.9, and 100% was obtained for TRBV, TRBJ, IGHV, and IGHJ, respectively. Analyses of subsampling performance for these samples showed IMPre to be robust using different data quantities. Subsequently, IMPre was tested on samples from rhesus monkeys and human long sequences: the highly accurate results demonstrated IMPre to be stable with animal and multiple data types. With rapid accumulation of high-throughput sequence data for TCR and BCR repertoires, IMPre can be applied broadly for obtaining novel genes and a large number of novel alleles. IMPre is available at https://github.com/zhangwei2015/IMPre.
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