Welcome to the LIgO documentation!¶
LIgO is a Python tool for simulation of adaptive immune receptors (AIRs) and repertoires (AIRRs) with known ground-truth immune signals for the development and benchmarking of AIRR-based machine learning. To get started using LigO right now, check out our Quickstart tutorial.
Why should you use LIgO?¶
LIgO makes simulations reproducible, all simulation parameters are specified through a YAML file.
LIgO contains different types of immune events and immune signals, including (gapped) k-mers, PWMs, specific V and J genes.
LIgO supports simulation of signal-specific AIRs using rejection sampling or signal implantation and preserves the AIRR generation probability distribution.
LIgO simulates synthetic AIRR (BCRs and TCRs) data both on the receptor and repertoire level, the single and paired-chain level.
LIgO guides the user and helps to set optimal simulation parameters.
LIgO outputs detailed information about presence and position(s) of immune signal(s) for every AIR in AIRR-compliant format.
Please check out LIgO manuscript (biorxiv link) for more information!