STIG

STIG generates artificial T-cell receptor (TCR) repertoires and simulated sequencing data to enable quantitative evaluation of T cell receptor repertoire inference methods.

Key Features:

• Customizable Virtual Repertoires: Creates virtual T-cell repertoires with configurable biological parameters to reproduce the diversity and characteristics of natural immune responses.
• Simulated Sequencing Data Generation: Generates sequencing reads from simulated repertoires with traceable attribution of individual reads to their originating T-cell receptor clonotypes.
• Robust Evaluation Framework: Provides known repertoires with defined biological and sequencing characteristics for rigorous comparison and benchmarking of TCR repertoire inference tools.
• Implementation in Python 3: Implemented in Python 3.

Scientific Applications:

• Benchmarking TCR inference methods: Enables performance evaluation and comparison of T cell repertoire inference tools using simulated ground-truth repertoires and reads.
• Immunological research and validation: Supports studies of host immune responses in infections, tumors, and autoimmune diseases by providing biologically inspired simulated repertoires for method development and validation.

Methodology:

Simulates TCR sequences based on germline gene segment rearrangements (akin to processes in B cell receptors), simulates clonal expansion, and generates sequencing reads with explicit mapping of reads back to clonotypes.