B-P.14: InterProScan 6: a modern large-scale protein function annotation pipeline
InterProScan integrates predictive models from the InterPro consortium to annotate protein sequences with domains, families, and functional sites and is widely used in genome and metagenome annotation pipelines. It underpins large-scale annotation efforts at resources such as UniProt, Ensembl, and MGnify and is routinely applied in comparative genomics and functional annotation workflows. While InterProScan 5 has been extensively adopted over more than a decade, its architecture increasingly limited scalability, portability, and integration with modern workflow systems.
InterProScan 6 addresses these limitations through a complete reimplementation as a Nextflow-based workflow. The pipeline supports execution across local systems, high-performance computing (HPC) clusters, and cloud platforms with native container support via Docker, Singularity, and Apptainer. Pipeline logic is decoupled from signature data, allowing multiple InterPro releases to coexist within shared installations and enabling on-demand retrieval of required datasets. A redesigned Matches API provides access to precomputed InterPro annotations for UniParc sequences, allowing transparent reuse of existing results during execution.
Benchmarking across nine reference proteomes, from bacteria to complex eukaryotes, shows consistent wall-clock runtime reductions relative to InterProScan 5, with approximately two-fold speedups on large eukaryotic proteomes. When annotation reuse via the Matches API is available, end-to-end runtimes fall to minutes. Concordance analysis across the full Swiss-Prot dataset shows that InterProScan 6 reproduces InterProScan 5 results with near-identical precision and sensitivity across all InterPro member databases.
By combining workflow-native design, flexible data management, and reuse of precomputed annotations, InterProScan 6 enables efficient, reproducible, and scalable protein function annotation for growing genomic and metagenomic datasets.
Co-authors: Emma Hobbs, Laise Cavalcanti Florentino, Alex Bateman
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