Tools for interpreting large-scale protein profiling in microbiology.
Tools for interpreting large-scale protein profiling in microbiology.
复制标题
DOI：
10.1177/154405910808701113
复制
发表时间：
2008-11
影响因子：
7.6
通讯作者：
Hackett M
中科院分区：
文献类型：
--
作者：
Hendrickson EL;Lamont RJ;Hackett M
微生物系统的定量蛋白质组分析产生大量的数据集，这些数据集很难解释，而且很耗时。幸运的是，许多用于分析大型转录组微阵列数据集的数据显示和基因聚类工具也适用于蛋白质组。丰度比与总信号或光谱计数的图可以突出随机误差和假定变化的区域。基因组序列中基因的物理顺序的数据可以突出潜在的操纵子。在转录组织的基本水平上，识别操纵子可以深入了解调控途径，并为蛋白质组学结果提供确凿的证据。分类和聚类算法可以根据蛋白质在不同条件下的丰度变化将其分组在一起，有助于识别有趣的表达模式，但通常对大规模蛋白质组分析中通常产生的噪声数据效果不佳。生物学解释可以更直接地通过将差异蛋白质丰度数据叠加到代谢途径上来辅助，从而指示具有改变的活性的途径。更广泛地说，本体工具检测不同代谢途径、分子功能和细胞定位的蛋白质丰度水平的改变。在实践中，途径分析和本体论受到与感兴趣的生物体相关联的数据库管理水平的限制。
Quantitative proteome 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 versus 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 like that typically generated in a large-scale proteome 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.
登录
查看更多内容
DOI：
10.1073/pnas.0701157104
发表时间：
2007-05-22
影响因子：
11.1
作者：
Hendrickson, Erik L.;Haydock, Andrew K.;Leigh, John A.
通讯作者：
Leigh, John A.
影响因子：
--
作者：
Lamont, Richard J.;Meila, Marina;Hackett, Murray
通讯作者：
Hackett, Murray
影响因子：
14.9
作者：
Huang DW;Sherman BT;Tan Q;Kir J;Liu D;Bryant D;Guo Y;Stephens R;Baseler MW;Lane HC;Lempicki RA
通讯作者：
Lempicki RA
影响因子：
48
作者：
Bammler, T;Beyer, RP;Zarbl, H
通讯作者：
Zarbl, H
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y