MiAIRR 2.0: "A revision of the Adaptive Immune Receptor Repertoire Minimal Standard featuring a single-cell extension"

High-throughput sequencing of adaptive immune receptor repertoires (AIRR-seq) offers novel insights into the diversity and dynamics of IG and TCR and is becoming routinely used  for the study of adaptive immunity. Increasing numbers of researchers around the world are creating and analyzing AIRR-seq data, thereby driving the need for the definition of common methods to annotate, access and share datasets. To address these needs, the AIRR Community was established in 2015 with the goal of making AIRR-seq data FAIR (findable, accessible, interoperable and reusable). Implementing the definitions of the AIRR Community, the “iReceptor plus” platform is under ongoing development to facilitate the storage, integration and controlled sharing of AIRR-seq data.

Three working groups of the AIRR Community – Minimal Standards, Data Representation and Common Repository – are currently developing standards and recommendations for metadata annotation, exchange formats and interfaces of a federated database infrastructure, respectively. In 2017, the Minimal Standards Working Group proposed a set of guidelines for AIRR-seq data annotation, provided as a checklist of data elements to include with AIRR study data submissions (MiAIRR). Here we present a revised version (2.0) of MiAIRR, which adds consistent controlled vocabularies and ontologies and introduces keywords to enhance the findability of the datasets, along with numerous minor changes based on the feedback provided by the wider community. Furthermore, MiAIRR 2.0 introduces the single-cell extension (SCXT), which defines key concepts to describe sequencing experiments at the single-cell level. Finally, we present an implementation of the SCXT into the “iReceptor plus” platform via the integration of a REST API for querying single cell data. These new features will expand the capabilities of the previously bulk-sequencing centered MiAIRR data standard to the single-cell level.