Arboreto — инференция генных регуляторных сетей

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arboreto is a Claude Code skill that infers gene regulatory networks (GRNs) from gene expression data using the scalable GRNBoost2 and GENIE3 algorithms. Built on the Arboreto Python library from Aerts Lab, it parallelizes tree-based ensemble regression via Dask, scaling from local multi-core machines to remote clusters. The core task is identifying which transcription factors regulate which target genes based on expression patterns across cells or samples, supporting both bulk RNA-seq and single-cell RNA-seq workflows. Output is a DataFrame of TF–target links with importance scores, ready for filtering or downstream tools such as pySCENIC. Bioinformaticians analyzing large-scale transcriptomics datasets to reconstruct regulatory architecture will find this skill directly applicable.