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Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data
Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell rece...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347241/ https://www.ncbi.nlm.nih.gov/pubmed/32574157 http://dx.doi.org/10.1371/journal.pcbi.1007977 |
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author | Nouri, Nima Kleinstein, Steven H. |
author_facet | Nouri, Nima Kleinstein, Steven H. |
author_sort | Nouri, Nima |
collection | PubMed |
description | Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell receptor (BCR) sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse junction region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the AGPLv3 license. |
format | Online Article Text |
id | pubmed-7347241 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73472412020-07-20 Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data Nouri, Nima Kleinstein, Steven H. PLoS Comput Biol Research Article Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell receptor (BCR) sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse junction region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the AGPLv3 license. Public Library of Science 2020-06-23 /pmc/articles/PMC7347241/ /pubmed/32574157 http://dx.doi.org/10.1371/journal.pcbi.1007977 Text en © 2020 Nouri, Kleinstein http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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 Nouri, Nima Kleinstein, Steven H. Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data |
title | Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data |
title_full | Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data |
title_fullStr | Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data |
title_full_unstemmed | Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data |
title_short | Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data |
title_sort | somatic hypermutation analysis for improved identification of b cell clonal families from next-generation sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347241/ https://www.ncbi.nlm.nih.gov/pubmed/32574157 http://dx.doi.org/10.1371/journal.pcbi.1007977 |
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