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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Published on: December 28, 2015

A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis.

Amrit Dhar^1,2, Duncan K Ralph², Vladimir N Minin³

¹Department of Statistics, University of Washington, Seattle, Washington, United States of America.

Plos Computational Biology

|August 18, 2020

Summary

This summary is machine-generated.

Understanding B cell receptor (BCR) development is key for designing vaccines against mutable pathogens. This study introduces a novel Bayesian phylogenetic hidden Markov model (phylo-HMM) to model BCR sequence evolution, integrating V(D)J rearrangement.

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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

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Area of Science:

Immunology
Computational Biology
Bioinformatics

Background:

The human body produces diverse B cell receptors (BCRs) to neutralize pathogens.
Understanding BCR development is crucial for designing vaccines against highly mutable viruses like influenza and HIV.
Current BCR sequence analysis methods often model V(D)J rearrangement, mutation, and selection processes separately.

Purpose of the Study:

To introduce a novel Bayesian phylogenetic inference approach for BCR sequences.
To integrate V(D)J rearrangement modeling with phylogenetic analysis of BCR sequence evolution.
To account for uncertainties in unobserved variables, including the phylogenetic tree.

Main Methods:

Development of a novel phylogenetic hidden Markov model (phylo-HMM).
Integration of a naive rearrangement model with a phylogenetic model for BCR sequence evolution.
Utilizing posterior distribution sampling to account for uncertainty in unobserved variables.

Main Results:

The proposed phylo-HMM approach provides a unified framework for BCR sequence analysis.
This method effectively models the complexities of B cell diversification, including V(D)J rearrangement.
The approach accounts for uncertainty in phylogenetic tree inference.

Conclusions:

The novel phylo-HMM offers a more comprehensive method for analyzing BCR sequence evolution.
This integrated approach can improve our understanding of BCR diversity and aid vaccine design.
Bayesian phylogenetic inference using phylo-HMMs represents a significant advancement in immunoinformatics.