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Quantifying selection in high-throughput Immunoglobulin sequencing data sets.

Gur Yaari¹, Mohamed Uduman, Steven H Kleinstein

¹Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA.

Nucleic Acids Research

|May 30, 2012

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We developed BASELINe, a Bayesian statistical framework to quantify antigen-driven selection in immunoglobulin sequences. This method advances understanding of adaptive immunity, autoimmunity, and B-cell cancers by analyzing somatic mutation patterns.

Area of Science:

Immunology
Computational Biology
Genetics

Background:

High-throughput immunoglobulin sequencing offers insights into B-cell somatic hypermutation and selection.
Understanding selection is crucial for adaptive immunity, autoimmunity, and B-cell cancers.
Previous methods primarily detected selection, lacking quantitative analysis.

Purpose of the Study:

To develop a statistical framework for quantifying antigen-driven selection in immunoglobulin sequences.
To enable comparative analysis of selection pressures across different B-cell populations.
To provide a more intuitive method for assessing and visualizing immune selection.

Main Methods:

Developed a Bayesian statistical framework named BASELINe (Bayesian estimation of Antigen-driven SELectIoN).

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Analyzed somatic mutation patterns within immunoglobulin sequences.

Applied the framework to next-generation sequencing data.

Main Results:

BASELINe shifts focus from detecting to quantifying selection.
Enables comparative analysis between sequence groups from different germline V(D)J segments.
Revealed distinct selection pressures for memory cells of different isotypes.

Conclusions:

BASELINe provides a robust framework for quantifying immune selection.
The method enhances understanding of adaptive immunity, autoimmunity, and B-cell malignancies.
The framework is adaptable for analyzing various DNA mutation patterns with mutator biases.