Tools for interpreting large-scale protein profiling in microbiology.
Quantitative proteomic analysis of microbial systems generates large datasets that can be difficult and time-consuming to interpret. Fortunately, many of the data display and gene-clustering tools developed to analyze large transcriptome microarray datasets are also applicable to proteomes. Plots of...
| Publicado en: | Journal of Dental Research Vol. 87; no. 11; pp. 1004 - 1016 |
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| Autores principales: | , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Nov2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105584612&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105584612 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00220345 1H7 jtl: Journal of Dental Research issn: 00220345 maglogo: Y pubinfo: dt: Nov2008 vid: 87 iid: 11 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 105584612 NLM18946006 2010118801 10.1177/154405910808701113 NLM18946006 PMC2660853 105584612 ppf: 1004 ppct: 12 formats: tig: atl: Tools for interpreting large-scale protein profiling in microbiology. aug: au: Hendrickson EL Lamont RJ Hackett M Hendrickson, E L Lamont, R J Hackett, M affil: Departments of Chemical Engineering, Universityof Washington, Box 355014, Seattle, WA 98195, USA sug: subj: Bacterial Proteins Analysis Genetic Techniques Molecular Probe Techniques Methods Proteomics Methods Bacteria Classification Classification Cluster Analysis Gram-Negative Anaerobic Bacteria Metabolic Networks and Pathways Resource Databases ab: Quantitative proteomic analysis of microbial systems generates large datasets that can be difficult and time-consuming to interpret. Fortunately, many of the data display and gene-clustering tools developed to analyze large transcriptome microarray datasets are also applicable to proteomes. Plots of abundance ratio vs. total signal or spectral counts can highlight regions of random error and putative change. Displaying data in the physical order of the genes in the genome sequence can highlight potential operons. At a basic level of transcriptional organization, identifying operons can give insights into regulatory pathways as well as provide corroborating evidence for proteomic results. Classification and clustering algorithms can group proteins together by their abundance changes under different conditions, helping to identify interesting expression patterns, but often work poorly with noisy data such as typically generated in a large-scale proteomic analysis. Biological interpretation can be aided more directly by overlaying differential protein abundance data onto metabolic pathways, indicating pathways with altered activities. More broadly, ontology tools detect altered levels of protein abundance for different metabolic pathways, molecular functions, and cellular localizations. In practice, pathway analysis and ontology are limited by the level of database curation associated with the organism of interest. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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