Fast multiclonal clusterization of V(D)J recombinations from high-throughput sequencing

Area of Science:

• Immunology
• Bioinformatics
• Genomics

Background:

• V(D)J recombination is crucial for lymphocyte diversity and serves as a biomarker for pathologies like leukemia.
• Quantifying minimal residual disease in leukemia patients relies on V(D)J recombination analysis.
• The complete spectrum of lymphocyte diversity remains incompletely understood.

Purpose of the Study:

• To develop novel algorithms for processing high-throughput sequencing data to identify and quantify V(D)J junctions and lymphocyte clones.
• To enhance the understanding of lymphocyte diversity and improve disease monitoring in leukemia.

Main Methods:

• Development of new algorithms for processing high-throughput sequencing (HTS) data.
• Utilizing a seed heuristic for fast and scalable extraction of V(D)J junctions without initial germline alignment.
• Application of algorithms to TR gamma HTS data and simulated hypermutation data.

Main Results:

• Successfully extracted unnamed V(D)J junctions and clustered them into clones for quantification.
• Identified the dominant clone and additional previously undetected clones in acute lymphoblastic leukemia patient data.
• Demonstrated the effectiveness of the methods on simulated hypermutation data.

Conclusions:

• The developed algorithms offer novel insights into HTS data analysis for leukemia and quantitative assessment of immunological profiles.
• The methods are implemented in an open-source C++ program named Vidjil.
• Provides a powerful tool for advancing research in immunology and clinical diagnostics.