SJARACNe

SJARACNe reconstructs gene regulatory networks from high-throughput gene expression and large-scale transcriptomic profiles using an enhanced implementation of the ARACNe algorithm.

Key Features:

• Scalability: Processes large-scale transcriptomic datasets and thousands of samples to enable network inference from big data.
• Improved Computational Performance: Reduces time and memory usage relative to previous ARACNe implementations for more efficient network reconstruction.
• Preservation of Accuracy: Maintains the network inference accuracy characteristic of the original ARACNe algorithm.
• Implementation Languages: Computational core implemented in C++ with a Python scripting wrapper compatible with Python 3.6.1 or higher.

Scientific Applications:

• Systems biology network reconstruction: Reverse engineering of regulatory and signaling gene networks from high-throughput gene expression and transcriptomic data.

Methodology:

Implements an enhanced version of the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) to infer networks from gene expression profiles; computational core in C++ with a Python 3.6.1+ wrapper.