SUMMARY: Reconstructing gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data is fundamental for understanding cellular dynamics at the molecular level but requires sophisticated workflows. Here, we introduce CellCraft, a web-based application designed to streamline GRN inference. CellCraft integrates multiple GRN reconstruction tools, including TENET, within a unified web application featuring an intuitive graphical user interface. Notably, CellCraft provides a visual programming interface that simplifies the design and execution of complex multistep analyses, thereby enhancing accessibility and facilitating the visualization and interpretation of computational experiments. Furthermore, its modular plugin architecture ensures extensibility, enabling the incorporation of newly developed single-cell analysis algorithms. Consequently, CellCraft provides a user-friendly and extensible application for integrative GRN analysis of scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: CellCraft is available on GitHub at https://github.com/cxinsys/cellcraft. The source code has been archived on Zenodo at 10.5281/zenodo.17865848.

SUMMARY: SCODE reconstructs gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data using an ordinary differential equation (ODE) model, and has been successfully applied to a wide range of scRNA-seq datasets, including mouse, human, and plant cells. However, its computational performance is limited when processing large datasets due to its sequential execution flow and repeated optimization loops. To overcome this limitation, we have developed FastSCODE, a batch computing version of the SCODE algorithm optimized for acceleration on manycore processors such as GPUs. FastSCODE performs batch computation on multiple gene expression profiles and optimizes the parameters of a linear ODE model using manycore computing. Compared to the original implementation, FastSCODE achieves up to 6000× improvement in performance (from about one month to 10 min) on the CeNGEN scRNA-seq dataset when using multiple GPUs. AVAILABILITY AND IMPLEMENTATION: FastSCODE is publicly available on GitHub at https://github.com/cxinsys/fastscode.

SUMMARY: TENET reconstructs gene regulatory networks from single-cell RNA sequencing (scRNAseq) data using the transfer entropy (TE), and works successfully on a variety of scRNAseq data. However, TENET is limited by its long computation time for large datasets. To address this limitation, we propose FastTENET, an array-computing version of TENET algorithm optimized for acceleration on manycore processors such as GPUs. FastTENET counts the unique patterns of joint events to compute the TE based on array computing. Compared to TENET, FastTENET achieves up to 973× performance improvement. AVAILABILITY AND IMPLEMENTATION: FastTENET is available on GitHub at https://github.com/cxinsys/fasttenet.