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

Rakbin Sung; Hyeonkyu Kim; Junil Kim; Daewon Lee

doi:10.1093/bioinformatics/btae699

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

Bioinformatics. 2024 Nov 28;40(12):btae699. doi: 10.1093/bioinformatics/btae699.

Authors

Rakbin Sung¹, Hyeonkyu Kim², Junil Kim^{2

3}, Daewon Lee^{1

4}

Affiliations

¹ Department of Applied Art and Technology, College of Art and Technology, Chung-Ang University, Anseong 17546, Republic of Korea.
² Department of Bioinformatics, Soongsil University, Seoul 06978, Republic of Korea.
³ School of Systems Biomedical Science, Soongsil University, Seoul 06978, Republic of Korea.
⁴ School of Art and Technology, College of Art and Technology, Chung-Ang University, Anseong 17546, Republic of Korea.

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.

MeSH terms

Algorithms*
Computational Biology / methods
Entropy
Gene Regulatory Networks
Sequence Analysis, RNA / methods
Single-Cell Analysis / methods
Software*

Abstract

MeSH terms

Grants and funding