IMperm: a fast and comprehensive IMmune Paired-End Reads Merger for sequencing data

Area of Science:

• Immunology
• Bioinformatics
• Genomics

Background:

• The adaptive immune receptor repertoire (AIRR) is crucial for immune responses.
• AIRR sequencing aids cancer immunotherapy and minimal residual disease (MRD) detection.
• Paired-end (PE) reads from AIRR sequencing require merging for comprehensive analysis.

Purpose of the Study:

• To develop a specialized software tool for merging immune sequencing PE reads.
• To address the challenges posed by the diverse nature of AIRR data.
• To improve the accuracy and efficiency of AIRR data processing.

Main Methods:

• Developed IMperm, a software package utilizing a k-mer-and-vote strategy for rapid overlap detection.
• IMperm handles various PE read types, adapter contamination, and low-quality/non-overlapping reads.
• Evaluated IMperm's performance on simulated and real sequencing data.

Main Results:

• IMperm demonstrated superior performance compared to existing tools on both simulated and sequencing data.
• The software successfully processed MRD detection data for leukemia and lymphoma.
• Identified 19 novel MRD clones in 14 leukemia patients from existing datasets.
• Validated IMperm's utility on genomic and cell-free DNA datasets.

Conclusions:

• IMperm is an efficient and versatile tool for merging immune sequencing PE reads.
• The software enhances MRD detection capabilities in hematological malignancies.
• IMperm offers a low-resource, high-performance solution for AIRR data analysis.