Infers gene regulatory networks (GRNs) from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Arboreto is a high-performance computational library designed to identify transcription factor-target gene relationships within gene expression datasets. By leveraging parallelized algorithms that scale from local machines to multi-node clusters via Dask, it enables researchers to reconstruct complex regulatory landscapes from both bulk and single-cell RNA-seq data. It is particularly useful for large-scale genomic studies where computational efficiency and the identification of high-confidence regulatory interactions are critical for downstream biological interpretation.
주요 기능
01Support for large-scale transcriptomics (bulk and single-cell RNA-seq)
02Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
03Importance scoring for ranking and filtering high-confidence regulatory links
04Direct integration with pySCENIC for regulon identification workflows
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06Distributed computing capabilities via Dask for multi-core and cluster execution
사용 사례
01Building consensus networks using multi-seed robustness analysis for scientific validation
02Identifying cell-type-specific transcription factor targets in single-cell datasets
03Analyzing regulatory network dynamics across different treatment conditions
