The use of non-functional clonotypes as a natural calibrator for quantitative bias correction in adaptive immune receptor repertoire profiling
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Federation
- Dmitry Rogachev National Medical and Research Center of Pediatric Hematology, Oncology and Immunology, Russian Federation
- Skolkovo Institute of Science and Technology, Russian Federation
Abstract
High-throughput sequencing of adaptive immune receptor repertoires is a valuable tool for receiving insights in adaptive immunity studies. Several powerful TCR/BCR repertoire reconstruction and analysis methods have been developed in the past decade. However, detecting and correcting the discrepancy between real and experimentally observed lymphocyte clone frequencies is still challenging. Here we discovered a hallmark anomaly in the ratio between read count and clone count-based frequencies of non-functional clonotypes in multiplex PCR-based immune repertoires. Calculating this anomaly, we formulated a quantitative measure of V- and J-genes frequency bias driven by multiplex PCR during library preparation called Over Amplification Rate (OAR). Based on the OAR concept, we developed an original software for multiplex PCR-specific bias evaluation and correction named iROAR: Immune Repertoire Over Amplification Removal (https://github.com/smiranast/iROAR). The iROAR algorithm was successfully tested on previously published TCR repertoires obtained using both 5' RACE (Rapid Amplification of cDNA Ends)-based and multiplex PCR-based approaches and compared with a biological spike-in-based method for PCR bias evaluation. The developed approach can increase the accuracy and consistency of repertoires reconstructed by different methods making them more applicable for comparative analysis.
Data availability
Sequencing data have been deposited in SRA under accession code PRJNA825832. All other sequencing data analyzed during this study are previously published and fully available under links or access numbers included in the manuscript and supporting files.
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Homo sapiens T-cell repertoire - MZ twinsNCBI BioProject, PRJNA214848.
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TCR repertoire in IBD twinsNCBI BioProject, PRJEB27352.
-
Protocol for full length profiling of IG repertoiresNCBI BioProject, PRJNA297771.
-
T cell receptor repertoire sequencing with MIDCIRSNCBI BioProject, PRJNA427746.
-
TCR diversity and clonality of human CD4+ memory T cellsNCBI BioProject, PRJEB31283.
-
Paired TCR alpha:TCR beta sequencing at the single-cell levelNCBI BioProject, PRJNA593622.
Article and author information
Author details
Dmitriy M Chudakov
Funding
Russian Science Foundation (20-75-10091)
- Alexander Komkov
Russian Foundation for Basic Research (20-015-00462)
- Alexander Komkov
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Smirnova et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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The use of non-functional clonotypes as a natural calibrator for quantitative bias correction in adaptive immune receptor repertoire profiling
eLife 12:e69157.
https://doi.org/10.7554/eLife.69157
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