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Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data

Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA sequencing (scRNA-seq) data. This need emerged from the expansion of scRNA-seq techniques, the increasing interest in antibody-based drug development and the importance of BCR repert...

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Autores principales: Andreani, Tommaso, Slot, Linda M, Gabillard, Samuel, Strübing, Carsten, Reimertz, Claus, Yaligara, Veeranagouda, Bakker, Aleida M, Olfati-Saber, Reza, Toes, René E M, Scherer, Hans U, Augé, Franck, Šimaitė, Deimantė
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278041/
https://www.ncbi.nlm.nih.gov/pubmed/35855325
http://dx.doi.org/10.1093/nargab/lqac049
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author Andreani, Tommaso
Slot, Linda M
Gabillard, Samuel
Strübing, Carsten
Reimertz, Claus
Yaligara, Veeranagouda
Bakker, Aleida M
Olfati-Saber, Reza
Toes, René E M
Scherer, Hans U
Augé, Franck
Šimaitė, Deimantė
author_facet Andreani, Tommaso
Slot, Linda M
Gabillard, Samuel
Strübing, Carsten
Reimertz, Claus
Yaligara, Veeranagouda
Bakker, Aleida M
Olfati-Saber, Reza
Toes, René E M
Scherer, Hans U
Augé, Franck
Šimaitė, Deimantė
author_sort Andreani, Tommaso
collection PubMed
description Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA sequencing (scRNA-seq) data. This need emerged from the expansion of scRNA-seq techniques, the increasing interest in antibody-based drug development and the importance of BCR repertoire changes in cancer and autoimmune disease progression. However, a comprehensive assessment of performance-influencing factors such as the sequencing depth, read length or number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology is still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three experimental SMART-seq datasets. In addition, we validated that the BCRs assembled in silico recognize their intended targets when expressed as monoclonal antibodies. We observed that methods such as BALDR, BASIC and BRACER showed the best overall performance across the tested datasets and conditions, whereas only BASIC demonstrated acceptable results on very short read libraries. Furthermore, the de novo assembly-based methods BRACER and BALDR were the most accurate in reconstructing BCRs harboring different degrees of SHMs in the variable domain, while TRUST4, MiXCR and BASIC were the fastest. Finally, we propose guidelines to select the best method based on the given data characteristics.
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spelling pubmed-92780412022-07-18 Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data Andreani, Tommaso Slot, Linda M Gabillard, Samuel Strübing, Carsten Reimertz, Claus Yaligara, Veeranagouda Bakker, Aleida M Olfati-Saber, Reza Toes, René E M Scherer, Hans U Augé, Franck Šimaitė, Deimantė NAR Genom Bioinform Methods and Benchmark Surveys Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA sequencing (scRNA-seq) data. This need emerged from the expansion of scRNA-seq techniques, the increasing interest in antibody-based drug development and the importance of BCR repertoire changes in cancer and autoimmune disease progression. However, a comprehensive assessment of performance-influencing factors such as the sequencing depth, read length or number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology is still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three experimental SMART-seq datasets. In addition, we validated that the BCRs assembled in silico recognize their intended targets when expressed as monoclonal antibodies. We observed that methods such as BALDR, BASIC and BRACER showed the best overall performance across the tested datasets and conditions, whereas only BASIC demonstrated acceptable results on very short read libraries. Furthermore, the de novo assembly-based methods BRACER and BALDR were the most accurate in reconstructing BCRs harboring different degrees of SHMs in the variable domain, while TRUST4, MiXCR and BASIC were the fastest. Finally, we propose guidelines to select the best method based on the given data characteristics. Oxford University Press 2022-07-13 /pmc/articles/PMC9278041/ /pubmed/35855325 http://dx.doi.org/10.1093/nargab/lqac049 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods and Benchmark Surveys
Andreani, Tommaso
Slot, Linda M
Gabillard, Samuel
Strübing, Carsten
Reimertz, Claus
Yaligara, Veeranagouda
Bakker, Aleida M
Olfati-Saber, Reza
Toes, René E M
Scherer, Hans U
Augé, Franck
Šimaitė, Deimantė
Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data
title Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data
title_full Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data
title_fullStr Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data
title_full_unstemmed Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data
title_short Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data
title_sort benchmarking computational methods for b-cell receptor reconstruction from single-cell rna-seq data
topic Methods and Benchmark Surveys
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278041/
https://www.ncbi.nlm.nih.gov/pubmed/35855325
http://dx.doi.org/10.1093/nargab/lqac049
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