Work

FastTENET: an accelerated TENET algorithm based on manycore computing in Python

Year: 2024

Type: article

Abstract: 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.

Source: Bioinformatics

DOI: 10.1093/bioinformatics/btae699

Authors: Rakbin Sung, Hyeonkyu Kim, Junil Kim, Daewon Lee

Institutions: Chung-Ang University, Soongsil University

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $4,188