RT Journal Article
SR Electronic
T1 Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.08.02.454701
DO 10.1101/2021.08.02.454701
A1 Inyoung Kim
A1 Sang Yoon Byun
A1 Sangyeup Kim
A1 Sangyoon Choi
A1 Jinsung Noh
A1 Junho Chung
A1 Byung Gee Kim
YR 2021
UL http://biorxiv.org/content/early/2021/11/29/2021.08.02.454701.abstract
AB Analyzing B cell receptor (BCR) repertoires is immensely useful in evaluating one’s immunological status. Conventionally, repertoire analysis methods have focused on comprehensive assessments of clonal compositions, including V(D)J segment usage, nucleotide insertions/deletions, and amino acid distributions. Here, we introduce a novel computational approach that applies deep-learning-based protein embedding techniques to analyze BCR repertoires. By selecting the most frequently occurring BCR sequences in a given repertoire and computing the sum of the vector representations of these sequences, we represent an entire repertoire as a 100-dimensional vector and eventually as a single data point in vector space. We demonstrate that this new approach enables us to not only accurately cluster BCR repertoires of coronavirus disease 2019 (COVID-19) patients and healthy subjects but also efficiently track minute changes in immune status over time as patients undergo treatment. Furthermore, using the distributed representations, we successfully trained an XGBoost classification model that achieved a mean accuracy rate of over 87% given a repertoire of CDR3 sequences.Competing Interest StatementThe authors have declared no competing interest.