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A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data

The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clonal lineages. A high number of clonal lineages in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity reflect antigen selective pres...

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Autores principales: Abdollahi, Nika, Jeusset, Lucile, De Septenville, Anne Langlois, Ripoche, Hugues, Davi, Frédéric, Bernardes, Juliana Silva
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462827/
https://www.ncbi.nlm.nih.gov/pubmed/36037250
http://dx.doi.org/10.1371/journal.pcbi.1010411
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author Abdollahi, Nika
Jeusset, Lucile
De Septenville, Anne Langlois
Ripoche, Hugues
Davi, Frédéric
Bernardes, Juliana Silva
author_facet Abdollahi, Nika
Jeusset, Lucile
De Septenville, Anne Langlois
Ripoche, Hugues
Davi, Frédéric
Bernardes, Juliana Silva
author_sort Abdollahi, Nika
collection PubMed
description The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clonal lineages. A high number of clonal lineages in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity reflect antigen selective pressure. Identifying clonal lineages is fundamental to many repertoire studies, including repertoire comparisons, clonal tracking, and statistical analysis. Several methods have been developed to group sequences from high-throughput B cell repertoire data. Current methods use clustering algorithms to group clonally-related sequences based on their similarities or distances. Such approaches create groups by optimizing a single objective that typically minimizes intra-clonal distances. However, optimizing several objective functions can be advantageous and boost the algorithm convergence rate. Here we propose MobiLLe, a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial groups and iteratively applies two objective functions that optimize cohesion and separation within clonal lineages simultaneously. We show that our method greatly improves clonal lineage grouping on simulated benchmarks with varied mutation rates compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools and can reproduce the results of previous publications. The method based on multi-objective clustering can accurately identify clonally-related antibody sequences and presents the lowest running time among state-of-art tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context. MobiLLe can potentially help unravel the mechanisms involved in developing and evolving B cell malignancies.
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spelling pubmed-94628272022-09-10 A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data Abdollahi, Nika Jeusset, Lucile De Septenville, Anne Langlois Ripoche, Hugues Davi, Frédéric Bernardes, Juliana Silva PLoS Comput Biol Research Article The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clonal lineages. A high number of clonal lineages in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity reflect antigen selective pressure. Identifying clonal lineages is fundamental to many repertoire studies, including repertoire comparisons, clonal tracking, and statistical analysis. Several methods have been developed to group sequences from high-throughput B cell repertoire data. Current methods use clustering algorithms to group clonally-related sequences based on their similarities or distances. Such approaches create groups by optimizing a single objective that typically minimizes intra-clonal distances. However, optimizing several objective functions can be advantageous and boost the algorithm convergence rate. Here we propose MobiLLe, a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial groups and iteratively applies two objective functions that optimize cohesion and separation within clonal lineages simultaneously. We show that our method greatly improves clonal lineage grouping on simulated benchmarks with varied mutation rates compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools and can reproduce the results of previous publications. The method based on multi-objective clustering can accurately identify clonally-related antibody sequences and presents the lowest running time among state-of-art tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context. MobiLLe can potentially help unravel the mechanisms involved in developing and evolving B cell malignancies. Public Library of Science 2022-08-29 /pmc/articles/PMC9462827/ /pubmed/36037250 http://dx.doi.org/10.1371/journal.pcbi.1010411 Text en © 2022 Abdollahi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Abdollahi, Nika
Jeusset, Lucile
De Septenville, Anne Langlois
Ripoche, Hugues
Davi, Frédéric
Bernardes, Juliana Silva
A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data
title A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data
title_full A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data
title_fullStr A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data
title_full_unstemmed A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data
title_short A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data
title_sort multi-objective based clustering for inferring bcr clonal lineages from high-throughput b cell repertoire data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462827/
https://www.ncbi.nlm.nih.gov/pubmed/36037250
http://dx.doi.org/10.1371/journal.pcbi.1010411
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