Infers gene regulatory networks from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Arboreto is a specialized computational tool integrated with Claude Code for bioinformaticians and researchers to map transcription factor-target gene interactions. It enables the reconstruction of complex gene regulatory networks (GRNs) from large-scale bulk or single-cell RNA-seq datasets using high-performance algorithms. By leveraging distributed computing via Dask, Arboreto facilitates autonomous biological discovery, allowing users to identify key regulatory drivers and interpret gene expression patterns across diverse experimental conditions.
주요 기능
01Transcription factor filtering for targeted regulatory mapping
02324 GitHub stars
03Support for large-scale bulk and single-cell RNA-seq datasets
04Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
05Automated scripts for standard genomic analysis workflows
06Distributed computing integration with Dask for cluster scaling
사용 사례
01Integrating GRN inference as a core component of the pySCENIC pipeline
02Reconstructing regulatory networks for disease vs. control comparative analysis
03Identifying transcription factor-target gene relationships in single-cell datasets
