Open AccessGraphics processing unit‐based alignment of protein interaction networks
Author(s) -
Xie Jiang,
Zhou Zhonghua,
Ma Jin,
Xiang Chaojuan,
Nie Qing,
Zhang Wu
Publication year - 2015
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2014.0052
Subject(s) - graphics processing unit , computer science , parallel computing , computational science , graphics , general purpose computing on graphics processing units , mode (computer interface) , computer graphics (images) , operating system
Network alignment is an important bridge to understanding human protein–protein interactions (PPIs) and functions through model organisms. However, the underlying subgraph isomorphism problem complicates and increases the time required to align protein interaction networks (PINs). Parallel computing technology is an effective solution to the challenge of aligning large‐scale networks via sequential computing. In this study, the typical Hungarian‐Greedy Algorithm (HGA) is used as an example for PIN alignment. The authors propose a HGA with 2‐nearest neighbours (HGA‐2N) and implement its graphics processing unit (GPU) acceleration. Numerical experiments demonstrate that HGA‐2N can find alignments that are close to those found by HGA while dramatically reducing computing time. The GPU implementation of HGA‐2N optimises the parallel pattern, computing mode and storage mode and it improves the computing time ratio between the CPU and GPU compared with HGA when large‐scale networks are considered. By using HGA‐2N in GPUs, conserved PPIs can be observed, and potential PPIs can be predicted. Among the predictions based on 25 common Gene Ontology terms, 42.8% can be found in the Human Protein Reference Database. Furthermore, a new method of reconstructing phylogenetic trees is introduced, which shows the same relationships among five herpes viruses that are obtained using other methods.