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IgIDivA: immunoglobulin intraclonal diversification analysis.

Laura Zaragoza-Infante1,2, Valentin Junet3,4, Nikos Pechlivanis1

  • 1Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.

Briefings in Bioinformatics
|August 31, 2022
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Summary

A new tool, immunoglobulin intraclonal diversification analysis (IgIDivA), comprehensively analyzes somatic hypermutation (SHM) in immunoglobulin (IG) genes. It reveals B cell clone evolution and diversity from high-throughput sequencing data.

Keywords:
B cell receptor immunoglobulingraph metricsgraph networkshigh-throughput sequencingintraclonal diversification

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Area of Science:

  • Immunology
  • Bioinformatics
  • Genetics

Background:

  • Intraclonal diversification (ID) in immunoglobulin (IG) genes is driven by somatic hypermutation (SHM) during B cell responses.
  • Understanding SHM patterns is crucial for B cell ontogeny, evolution, and disease insights.
  • High-throughput sequencing of IG repertoires offers powerful insights, but specialized tools for ID analysis are limited.

Purpose of the Study:

  • To introduce a novel tool, immunoglobulin intraclonal diversification analysis (IgIDivA), for comprehensive ID analysis.
  • To enable in-depth qualitative and quantitative characterization of SHMs within rearranged IG genes.
  • To provide objective metrics and visualizations for determining and comparing ID levels.

Main Methods:

  • Development of the IgIDivA tool for analyzing high-throughput sequencing data of IG genes.
  • Identification and characterization of SHMs, including the establishment of mutational pathways.
  • Application of graph-based metrics and statistical analysis for ID level determination and comparison.
  • Implementation as an R Shiny web application for user accessibility.

Main Results:

  • IgIDivA successfully identifies and characterizes SHMs within IG variable domains.
  • The tool establishes mutational pathways by analyzing connections between identified SHMs.
  • Objective determination and comparison of ID levels are achieved using novel graph-based metrics and statistical analysis.
  • Detailed visualizations of ID are generated through purpose-built graph networks.

Conclusions:

  • IgIDivA provides a robust and comprehensive methodology for analyzing intraclonal diversification in IG genes.
  • The tool enhances the understanding of B cell clone ontogeny and evolution by detailing SHM processes.
  • IgIDivA offers valuable insights into B cell responses in both health and disease contexts.
  • The R Shiny implementation makes IgIDivA readily accessible for researchers studying IG gene diversification.