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Ultrasensitive allele inference from immune repertoire sequencing data with MiXCR

A. Mikelov, G. Nefediev, A. Tashkeev, OL. Rodriguez, D. Aguilar Ortmans, V. Skatova, M. Izraelson, AN. Davydov, S. Poslavsky, S. Rahmouni, CT. Watson, D. Chudakov, SD. Boyd, D. Bolotin

. 2024 ; 34 (12) : 2293-2303. [pub] 20241223

Jazyk angličtina Země Spojené státy americké

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc25002981

Grantová podpora
U19 AI057229 NIAID NIH HHS - United States
U19 AI167903 NIAID NIH HHS - United States

E-zdroje Online Plný text

NLK Free Medical Journals od 1991 do Před 6 měsíci
Freely Accessible Science Journals od 1991-08-01 do Před 1 rokem
PubMed Central od 1997 do Před 6 měsíci
Europe PubMed Central od 1997 do Před 6 měsíci
Open Access Digital Library od 1991-08-01
Open Access Digital Library od 1991-08-01

Odkazy

PubMed 39433438
DOI 10.1101/gr.278775.123
Knihovny.cz E-zdroje

Allelic variability in the adaptive immune receptor loci, which harbor the gene segments that encode B cell and T cell receptors (BCR/TCR), is of critical importance for immune responses to pathogens and vaccines. Adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread in immunology research making it the most readily available source of information about allelic diversity in immunoglobulin (IG) and T cell receptor (TR) loci. Here, we present a novel algorithm for extrasensitive and specific variable (V) and joining (J) gene allele inference, allowing the reconstruction of individual high-quality gene segment libraries. The approach can be applied for inferring allelic variants from peripheral blood lymphocyte BCR and TCR repertoire sequencing data, including hypermutated isotype-switched BCR sequences, thus allowing high-throughput novel allele discovery from a wide variety of existing data sets. The developed algorithm is a part of the MiXCR software. We demonstrate the accuracy of this approach using AIRR-seq paired with long-read genomic sequencing data, comparing it to a widely used algorithm, TIgGER. We applied the algorithm to a large set of IG heavy chain (IGH) AIRR-seq data from 450 donors of ancestrally diverse population groups, and to the largest reported full-length TCR alpha and beta chain (TRA and TRB) AIRR-seq data set, representing 134 individuals. This allowed us to assess the genetic diversity within the IGH, TRA, and TRB loci in different populations and to establish a database of alleles of V and J genes inferred from AIRR-seq data and their population frequencies with free public access through VDJ.online database.

Citace poskytuje Crossref.org

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