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Integrating B cell lineage information into statistical tests for detecting selection in Ig sequences.

Mohamed Uduman1, Mark J Shlomchik, Francois Vigneault

  • 1Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520;

Journal of Immunology (Baltimore, Md. : 1950)
|December 31, 2013
PubMed
Summary

Detecting selection in B cell immunoglobulin (Ig) sequences is crucial for understanding immune responses. A new hybrid method improves selection detection sensitivity using lineage tree structures and binomial statistics.

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • Detecting selection in B cell immunoglobulin (Ig) sequences is vital for understanding immune responses, including affinity maturation.
  • Current sequence-based methods using replacement/silent mutation ratios have limited sensitivity.
  • Lineage tree shape analysis, while promising, is confounded by experimental factors like sequencing depth.

Purpose of the Study:

  • To develop a more sensitive method for detecting selection in B cell Ig sequences.
  • To integrate lineage tree information with established statistical frameworks.

Main Methods:

  • Proposed a hybrid method combining lineage tree structure with binomial statistical analysis.
  • Applied the binomial framework to mutations identified via lineage tree construction.
  • Validated the method using simulated and experimental datasets.

Main Results:

  • The hybrid method demonstrates increased sensitivity in detecting selection compared to traditional approaches.
  • Successfully identified selection signals in both simulated and real-world Ig sequencing data.
  • The approach is well-suited for analyzing large-scale, high-throughput sequencing data.

Conclusions:

  • The proposed hybrid method offers a more reliable and sensitive approach to detecting selection in B cell Ig sequences.
  • This method overcomes limitations of previous sequence-based and tree-shape analyses.
  • It is expected to be particularly valuable for analyzing large Ig sequencing datasets from high-throughput technologies.