Graphics processing units in bioinformatics, computational biology and systems biology.

Marco S Nobile,Paolo Cazzaniga,Daniela Besozzi,Andrea Tangherloni

doi:10.1093/bib/bbw058

Abstract

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools.

Highlights

FastPaSS [4] is a tool that accelerates the identification of a spectrum in a spectral library by means of the SpectraST similarity scoring algorithm [14]
One approach for genome-wide analysis consists in the extraction of information from a given interaction network, formalized as a graph whose vertexes represent the biochemical elements, and edges between vertexes correspond either to a physical/functional interaction or to some kind of correlation
These networks can be analyzed from a topological perspective, in order to capture some information on the network structure [3]

Summary

Spectral Analysis

A field of Computational Biology where GPU acceleration can yield a relevant speed-up is related to the analysis of spectral data derived from, e.g., mass-spectrometry experiments. One approach for genome-wide analysis consists in the extraction of information from a given interaction network, formalized as a graph whose vertexes represent the biochemical elements, and edges between vertexes correspond either to a physical/functional interaction or to some kind of correlation These networks can be analyzed from a topological perspective, in order to capture some information on the network structure [3]. Since this methodology relies on genome-wide information, it is in general computationally challenging To this aim, GBOOST [25] was developed to perform gene-gene interaction analysis of large genome data, showing a 40× speed-up and reducing the running time of a GWAS from 2.5 days down to a few hours. To the best of our knowledge, PANET and Mendel-GPU are the only tools for computational analysis of biological systems implemented exclusively for the OpenCL framework

Bayesian Inference

Movement Tracking

Quantum Chemistry

Further General Techniques