ImmunoDataAnalyzer: a bioinformatics pipeline for processing barcoded and UMI tagged immunological NGS data

Area of Science:

• Immunology
• Bioinformatics
• Genomics

Background:

• Next-generation sequencing (NGS) is crucial for high-throughput DNA sequencing and in-depth immune repertoire analysis.
• Studying T-cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases.
• Accurate processing and evaluation of immune repertoire NGS data are vital for comprehending immune responses.

Purpose of the Study:

• To develop an automated pipeline, ImmunoDataAnalyzer (IMDA), for analyzing immunological NGS data.
• To integrate various immune repertoire analysis tools into a single, user-friendly workflow.
• To facilitate the analysis of V(D)J recombination for improved understanding of immune repertoire structure and specificity.

Main Methods:

• Developed ImmunoDataAnalyzer (IMDA) as an automated pipeline for immunological NGS data.
• Implemented automated pre-processing for barcoded and UMI-tagged immune repertoire NGS data.
• Included functionalities for clonotype assembly, calculation of key repertoire figures, and comparison of multiple repertoires.

Main Results:

• IMDA automates the analysis of TCR and IG repertoires from raw NGS reads.
• The pipeline provides comprehensive analysis including clonality, diversity, V(D)J gene usage, and sample similarity.
• Generated output facilitates interpretation and can be used as input for machine learning models.

Conclusions:

• IMDA simplifies immune repertoire data analysis, reducing manual effort.
• The workflow emphasizes quality control and ease of use, even for non-bioinformaticians.
• IMDA is an open-source tool that aids in detecting potential sample preparation issues like swaps or contamination.