Infer gene regulatory networks from RNA-seq data using GRNBoost2 and GENIE3.
npx skills add https://github.com/Yezez9/Research-Agent --skill arboreto-yezez9
Please help me install this Agent Skill. Skill: arboreto Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/arboreto Command: npx skills add https://github.com/Yezez9/Research-Agent --skill arboreto-yezez9
This Skill automates the complex process of inferring gene regulatory networks (GRNs) from gene expression data, helping researchers understand gene interactions and biological pathways.
Use the arboreto skill to infer a gene regulatory network from the file 'expression_data.tsv' and save the output to 'network.tsv'.
High-intent search queries and answers about installing and using this skill.
Gene regulatory networks are inferred from gene expression data using scalable algorithms like GRNBoost2 and GENIE3 to identify transcription factor-target gene relationships from bulk or single-cell RNA-seq transcriptomics data.
The best way to scale gene regulatory network inference for large transcriptomics datasets is to use distributed computation frameworks like Dask, which support multi-core and cluster environments for processing large-scale RNA-seq data.
Yes, you can use GRNBoost2 and GENIE3 to analyze bulk RNA-seq data, as these algorithms support the analysis of transcriptomics data to infer transcription factor-target gene relationships across both bulk and single-cell expression profiles.
Gene regulatory network inference identifies transcription factor-target relationships by processing gene expression matrices to calculate regulatory strength scores between transcription factors and target genes using tree-based machine learning algorithms.
You do not strictly need a Dask cluster to run gene regulatory network inference, but Dask distributed computing is supported to scale inference to large datasets across multi-core and cluster environments.
A limitation of using GRNBoost2 for gene regulatory network inference is computational intensity on large expression matrices, requiring Dask distributed computing to manage memory and processing loads for large-scale single-cell RNA-seq datasets.
RamanEbrahimiInfer gene regulatory networks from RNA-seq data using GRNBoost2 and GENIE3.
ya-wayInfer gene regulatory networks from expression data using GRNBoost2 and GENIE3.
biomaps-infraInfer gene regulatory networks from expression data using GRNBoost2 and GENIE3.
SologaInfer gene regulatory networks from RNA-seq data using GRNBoost2 and GENIE3.
