用于高通量蛋白质组学的网格环境。

A grid environment for high-throughput proteomics.

作者信息

Cannataro Mario, Barla Annalisa, Flor Roberto, Jurman Giuseppe, Merler Stefano, Paoli Silvano, Tradigo Giuseppe, Veltri Pierangelo, Furlanello Cesare

机构信息

University Magna Graecia, Catanzaro, Italy.

出版信息

IEEE Trans Nanobioscience. 2007 Jun;6(2):117-23. doi: 10.1109/tnb.2007.897495.

DOI:10.1109/tnb.2007.897495

PMID:17695745

Abstract

We connect in a grid-enabled pipeline an ontology-based environment for proteomics spectra management with a machine learning platform for unbiased predictive analysis. We exploit two existing software platforms (MS-Analyzer and BioDCV), the emerging proteomics standards, and the middleware and computing resources of the EGEE Biomed VO grid infrastructure. In the setup, BioDCV is accessed by the MS-Analyzer workflow as a Web service, thus providing a complete grid environment for proteomics data analysis. Predictive classification studies on MALDI-TOF data based on this environment are presented.

摘要

我们在一个支持网格的管道中，将用于蛋白质组学谱管理的基于本体的环境与用于无偏预测分析的机器学习平台连接起来。我们利用两个现有的软件平台（MS-Analyzer和BioDCV）、新兴的蛋白质组学标准以及EGEE生物医学虚拟组织网格基础设施的中间件和计算资源。在设置过程中，MS-Analyzer工作流程将BioDCV作为Web服务进行访问，从而为蛋白质组学数据分析提供一个完整的网格环境。本文展示了基于此环境对基质辅助激光解吸电离飞行时间（MALDI-TOF）数据进行的预测分类研究。