Multidimensional proteomics for cell biology

被引:0
作者
Mark Larance
Angus I. Lamond
机构
[1] Laboratory for Quantitative Proteomics,
[2] Centre for Gene Regulation and Expression,undefined
[3] College of Life Sciences,undefined
[4] University of Dundee,undefined
来源
Nature Reviews Molecular Cell Biology | 2015年 / 16卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
The proteome is complex as a result of the interconnected, dynamic properties of proteins, which include abundance, isoform expression, subcellular localization, interactions, turnover rate and post-translational modifications, among others.Only through analysing the variation in many of these properties can a full understanding of crucial biological regulatory mechanisms be achieved. Such analyses have so far been restricted by technical limitations and cost.Data analysis and data sharing are crucial to maximise the effect of mass spectrometry-based proteomic analyses, as is making such data available to cell biologists in free to access, web-based and graphically rich formats.Our understanding of cellular processes will be enhanced by predicting the interdependence of protein properties. For example, knowing that a protein with a certain modification, if localized in the cytosol, will be degraded. Future innovations will enable more comprehensive measurement of a wider range of protein properties.
引用
收藏
页码:269 / 280
页数:11
相关论文
共 308 条
  • [111] Stoeber K(2009)Bioconductor: open software development for computational biology and bioinformatics Nature Protoc. 4 44-1722
  • [112] Godovac-Zimmermann J(2003)Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata Genome Res. 13 2498-5761
  • [113] Boisvert FM(2005)SAINT: probabilistic scoring of affinity purification-mass spectrometry data Proc. Natl Acad. Sci. USA 102 15545-886
  • [114] Lam YW(2010)ProHits: integrated software for mass spectrometry-based interaction proteomics PloS ONE 5 e13984-2445
  • [115] Lamont D(2013)ComplexQuant: high-throughput computational pipeline for the global quantitative analysis of endogenous soluble protein complexes using high resolution protein HPLC and precision label-free LC/MS/MS Nucleic Acids Res. 41 D808-1330
  • [116] Lamond AI(2014)Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists Mol. Cell. Proteomics 13 397-873
  • [117] Ziegler YS(2010)Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources Nature Biotech. 28 1248-76
  • [118] Hebert AS(2014)Cytoscape: a software environment for integrated models of biomolecular interaction networks Trends Cell Biol. 24 257-4982
  • [119] Still AJ(2014)Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles eLife 3 e01630-2457
  • [120] McClatchy DB(2015)Enrichment map: a network-based method for gene-set enrichment visualization and interpretation eLife 4 e04534-1703