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CellCraft: an extensible visual programming application for gene regulatory network inference.

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CellCraft simplifies gene regulatory network (GRN) inference from single-cell RNA sequencing (scRNA-seq) data. This user-friendly web application integrates multiple tools and offers a visual programming interface for complex analyses.

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Reconstructing gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is crucial for understanding cellular dynamics.
  • Existing workflows for GRN inference are often complex and require specialized expertise.

Purpose of the Study:

  • To introduce CellCraft, a novel web-based application designed to streamline GRN inference from scRNA-seq data.
  • To provide a user-friendly and extensible platform for integrative GRN analysis.

Main Methods:

  • CellCraft integrates multiple GRN reconstruction tools, including TENET.
  • It features an intuitive graphical user interface (GUI) and a visual programming interface.
  • A modular plugin architecture allows for extensibility and incorporation of new algorithms.

Main Results:

  • CellCraft simplifies the design and execution of complex multi-step analyses for GRN inference.
  • It enhances accessibility to sophisticated computational experiments.
  • Facilitates visualization and interpretation of GRN analysis results.

Conclusions:

  • CellCraft offers a user-friendly and extensible solution for GRN analysis of scRNA-seq datasets.
  • It democratizes complex computational biology workflows.
  • Aids in advancing the understanding of cellular dynamics at the molecular level.