Infers gene regulatory networks from transcriptomics data using scalable GRNBoost2 and GENIE3 algorithms.
Arboreto is a high-performance computational tool designed for bioinformaticians to map gene regulatory networks (GRNs) from large-scale gene expression data. It provides parallelized implementations of algorithms like GRNBoost2 and GENIE3, allowing users to identify transcription factor-target gene relationships across bulk or single-cell RNA-seq datasets. This skill facilitates complex genomic analysis by supporting distributed computing via Dask, making it possible to scale from local workstations to high-performance computing clusters while maintaining reproducibility and efficiency.
Key Features
01Support for large-scale single-cell and bulk RNA-seq datasets
02Scalable GRN inference using GRNBoost2 and GENIE3 algorithms
03Transcription factor filtering and importance-based link scoring
04Seamless integration with pySCENIC regulatory workflows
05Distributed computing integration with Dask for cluster scaling
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Use Cases
01Mapping transcription factor-target gene relationships in single-cell RNA-seq
02Scaling gene regulatory network inference to massive datasets using Dask clusters
03Identifying regulatory changes across different experimental conditions
