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Bioinformatics. 2018 Nov 2. doi: 10.1093/bioinformatics/bty907. [Epub ahead of print]

SJARACNe: a scalable software tool for gene network reverse engineering from big data.

Author information

Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
Department of Systems Biology, Columbia University, New York, NY, USA.



Over the last two decades, we have observed an exponential increase in the number of generated array or sequencing-based transcriptomic profiles. Reverse engineering of biological networks from high-throughput gene expression profiles has been one of the grand challenges in systems biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective and widely-used tools to address this challenge. However, existing ARACNe implementations do not efficiently process big input data with thousands of samples. Here we present an improved implementation of the algorithm, SJARACNe, to solve this big data problem, based on sophisticated software engineering. The new scalable SJARACNe package achieves a dramatic improvement in computational performance in both time and memory usage and implements new features while preserving the network inference accuracy of the original algorithm. Given that large-sampled transcriptomic data is increasingly available and ARACNe is extremely demanding for network reconstruction, the scalable SJARACNe will allow even researchers with modest computational resources to efficiently construct complex regulatory and signalling networks from thousands of gene expression profiles.

Availability and Implementation:

SJARACNe is implemented in Cā€‰++ (computational core) and Python (pipelining scripting wrapper,ā€‰>ā€‰=3.6.1). It is freely available at

Supplementary information:

Supplementary data are available at bioinformatics online.

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