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Likelihood-Based Inference of B Cell Clonal Families
The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called “rearrangement” forming progenitor B cells, then a Darwinian process of lineage diversification and selection called “af...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066976/ https://www.ncbi.nlm.nih.gov/pubmed/27749910 http://dx.doi.org/10.1371/journal.pcbi.1005086 |
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author | Ralph, Duncan K. Matsen, Frederick A. |
author_facet | Ralph, Duncan K. Matsen, Frederick A. |
author_sort | Ralph, Duncan K. |
collection | PubMed |
description | The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called “rearrangement” forming progenitor B cells, then a Darwinian process of lineage diversification and selection called “affinity maturation.” The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem “clonal family inference.” In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets. |
format | Online Article Text |
id | pubmed-5066976 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50669762016-10-27 Likelihood-Based Inference of B Cell Clonal Families Ralph, Duncan K. Matsen, Frederick A. PLoS Comput Biol Research Article The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called “rearrangement” forming progenitor B cells, then a Darwinian process of lineage diversification and selection called “affinity maturation.” The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem “clonal family inference.” In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM) framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets. Public Library of Science 2016-10-17 /pmc/articles/PMC5066976/ /pubmed/27749910 http://dx.doi.org/10.1371/journal.pcbi.1005086 Text en © 2016 Ralph, Matsen 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 Ralph, Duncan K. Matsen, Frederick A. Likelihood-Based Inference of B Cell Clonal Families |
title | Likelihood-Based Inference of B Cell Clonal Families |
title_full | Likelihood-Based Inference of B Cell Clonal Families |
title_fullStr | Likelihood-Based Inference of B Cell Clonal Families |
title_full_unstemmed | Likelihood-Based Inference of B Cell Clonal Families |
title_short | Likelihood-Based Inference of B Cell Clonal Families |
title_sort | likelihood-based inference of b cell clonal families |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5066976/ https://www.ncbi.nlm.nih.gov/pubmed/27749910 http://dx.doi.org/10.1371/journal.pcbi.1005086 |
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