Gene set analysis in the cloud

Lu Zhang; Shengchang Gu; Yuan Liu; Bingqiang Wang; Francisco Azuaje

doi:10.1093/bioinformatics/btr630

Gene set analysis in the cloud

Lu Zhang
, Shengchang Gu
, Yuan Liu
, Bingqiang Wang
, Francisco Azuaje^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Research › peer-review

33 Citations (Scopus)

Abstract

Summary: Cloud computing offers low cost and highly flexible opportunities in bioinformatics. Its potential has already been demonstrated in high-throughput sequence data analysis. Pathway-based or gene set analysis of expression data has received relatively less attention. We developed a gene set analysis algorithm for biomarker identification in the cloud. The resulting tool, YunBe, is ready to use on Amazon Web Services. Moreover, here we compare its performance to those obtained with desktop and computing cluster solutions.

Original language	English
Article number	btr630
Pages (from-to)	294-295
Number of pages	2
Journal	Bioinformatics
Volume	28
Issue number	2
DOIs	https://doi.org/10.1093/bioinformatics/btr630
Publication status	Published - Jan 2012

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10.1093/bioinformatics/btr630

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title = "Gene set analysis in the cloud",

abstract = "Summary: Cloud computing offers low cost and highly flexible opportunities in bioinformatics. Its potential has already been demonstrated in high-throughput sequence data analysis. Pathway-based or gene set analysis of expression data has received relatively less attention. We developed a gene set analysis algorithm for biomarker identification in the cloud. The resulting tool, YunBe, is ready to use on Amazon Web Services. Moreover, here we compare its performance to those obtained with desktop and computing cluster solutions.",

author = "Lu Zhang and Shengchang Gu and Yuan Liu and Bingqiang Wang and Francisco Azuaje",

note = "Funding Information: Funding: National Research Fund (FNR) of Luxembourg (AM2c Programme).",

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AU - Azuaje, Francisco

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AB - Summary: Cloud computing offers low cost and highly flexible opportunities in bioinformatics. Its potential has already been demonstrated in high-throughput sequence data analysis. Pathway-based or gene set analysis of expression data has received relatively less attention. We developed a gene set analysis algorithm for biomarker identification in the cloud. The resulting tool, YunBe, is ready to use on Amazon Web Services. Moreover, here we compare its performance to those obtained with desktop and computing cluster solutions.

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Gene set analysis in the cloud

Abstract

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