Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance. Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.
%0 Journal Article
%1 McArt2013CudaMap
%A McArt, Darragh G.
%A Bankhead, Peter
%A Dunne, Philip D.
%A Salto-Tellez, Manuel
%A Hamilton, Peter
%A Zhang, Shu-Dong
%D 2013
%J BMC Bioinformatics
%K cuda gene-expression high-performance-computing
%N 1
%P 305+
%R 10.1186/1471-2105-14-305
%T cudaMap: a GPU accelerated program for gene expression connectivity mapping
%U http://dx.doi.org/10.1186/1471-2105-14-305
%V 14
%X Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance. Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.
@article{McArt2013CudaMap,
abstract = {Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop {PC}, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using {sscMap}, a popular Java application that runs on standard {CPUs} (Central Processing Units). Here, we describe new software, {cudaMap}, which has been implemented using {CUDA} {C/C}++ to harness the computational power of {NVIDIA} {GPUs} (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. {cudaMap} can identify candidate therapeutics from the same signature in just over thirty seconds when using an {NVIDIA} Tesla C2050 {GPU}. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between {GPU} assisted performance and {CPU} executions as the computational load increases for high accuracy evaluation of statistical significance. Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of {GPUs} represents a major avenue of local accelerated computing. {cudaMap} will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. {cudaMap} is open source and can be freely downloaded from {http://purl.oclc.org/NET}/{cudaMap}.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {McArt, Darragh G. and Bankhead, Peter and Dunne, Philip D. and Salto-Tellez, Manuel and Hamilton, Peter and Zhang, Shu-Dong},
biburl = {https://www.bibsonomy.org/bibtex/27766cd37732a6e5f02cdcd14f0efea43/karthikraman},
citeulike-article-id = {12718523},
citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-14-305},
citeulike-linkout-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3852931/},
citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/24112435},
citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=24112435},
day = 11,
doi = {10.1186/1471-2105-14-305},
interhash = {4e49e079065349cba66d6610eaf887bd},
intrahash = {7766cd37732a6e5f02cdcd14f0efea43},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {cuda gene-expression high-performance-computing},
month = oct,
number = 1,
pages = {305+},
pmcid = {PMC3852931},
pmid = {24112435},
posted-at = {2013-11-04 06:35:43},
priority = {2},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {{cudaMap}: a {GPU} accelerated program for gene expression connectivity mapping},
url = {http://dx.doi.org/10.1186/1471-2105-14-305},
volume = 14,
year = 2013
}