Inference of B cell clonal families using heavy/light chain pairing information.

Autor: Ralph DK; Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America., Matsen FA 4th; Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.; Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America.; Department of Statistics, University of Washington, Seattle, Washington, United States of America.; Howard Hughes Medical Institute, Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2022 Nov 28; Vol. 18 (11), pp. e1010723. Date of Electronic Publication: 2022 Nov 28 (Print Publication: 2022).
DOI: 10.1371/journal.pcbi.1010723
Abstrakt: Next generation sequencing of B cell receptor (BCR) repertoires has become a ubiquitous tool for understanding the antibody-mediated immune response: it is now common to have large volumes of sequence data coding for both the heavy and light chain subunits of the BCR. However, until the recent development of high throughput methods of preserving heavy/light chain pairing information, these samples contained no explicit information on which heavy chain sequence pairs with which light chain sequence. One of the first steps in analyzing such BCR repertoire samples is grouping sequences into clonally related families, where each stems from a single rearrangement event. Many methods of accomplishing this have been developed, however, none so far has taken full advantage of the newly-available pairing information. This information can dramatically improve clustering performance, especially for the light chain. The light chain has traditionally been challenging for clonal family inference because of its low diversity and consequent abundance of non-clonal families with indistinguishable naive rearrangements. Here we present a method of incorporating this pairing information into the clustering process in order to arrive at a more accurate partition of the data into clonally related families. We also demonstrate two methods of fixing imperfect pairing information, which may allow for simplified sample preparation and increased sequencing depth. Finally, we describe several other improvements to the partis software package.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2022 Ralph, Matsen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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