Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata

被引：64

作者：

Gatto, Laurent ^{[1
,2
]}

Breckels, Lisa M. ^{[1
,2
]}

Wieczorek, Samuel ^{[3
]}

Burger, Thomas ^{[3
]}

Lilley, Kathryn S. ^{[2
]}

机构：

[1] Univ Cambridge, Dept Biochem, Computat Prote Unit, Cambridge CB2 1QR, England

[2] Univ Cambridge, Dept Biochem, Cambridge Ctr Prote, Cambridge CB2 1QR, England

[3] Univ Grenoble Alpes, CEA iRSTV BGE, INSERM U1038, CNRS FR3425, F-38054 Grenoble, France

来源：

BIOINFORMATICS | 2014年 / 30卷 / 09期

基金：

英国生物技术与生命科学研究理事会;

关键词：

ORGANELLE; LOCALIZATION; PREDICTION; PROTEINS;

D O I：

10.1093/bioinformatics/btu013

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Motivation: Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools. Results: Here we present , a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing.

引用

页码：1322 / 1324

页数：3

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