An unbiased comparison of immunoglobulin sequence aligners

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is vital for studying immune system dynamics.
• Accurate rearranged immunoglobulin (Ig) sequence alignment is essential for reliable AIRR-seq data analysis.
• Current lack of a unified benchmarking standard hinders objective comparison of existing Ig sequence aligners.

Purpose of the Study:

• To introduce GenAIRR, a modular simulation framework for generating realistic Ig sequences with ground truths.
• To establish a standardized and unbiased method for benchmarking Ig sequence alignment tools.
• To facilitate objective comparisons of different Ig sequence aligners.

Main Methods:

• Developed GenAIRR, a simulation framework that models V(D)J recombination, somatic hypermutation, and sequence corruptions.
• Generated diverse Ig sequence datasets using the GenAIRR framework.
• Comprehensively assessed prominent Ig sequence aligners using rigorous evaluation criteria and GenAIRR-produced datasets.

Main Results:

• GenAIRR realistically simulates the complexities of Ig sequence generation and variations.
• Comprehensive assessment revealed distinct performance characteristics for various Ig sequence aligners.
• Established a basis for unbiased benchmarking of immunogenetics computational tools.

Conclusions:

• GenAIRR provides a robust platform for generating high-quality simulated AIRR-seq data.
• The framework and evaluation criteria enable objective benchmarking of Ig sequence alignment tools.
• This work will advance the accuracy of Ig sequence alignment, improving the understanding of adaptive immunity.