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本文引用的文献

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A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis.酵母蛋白质组的全质谱图谱应用于数量性状分析。
Nature. 2013 Feb 14;494(7436):266-70. doi: 10.1038/nature11835. Epub 2013 Jan 20.
2
iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates.iProphet:高通量蛋白质组学数据的多层次综合分析可提高肽段和蛋白质的鉴定率和错误评估。
Mol Cell Proteomics. 2011 Dec;10(12):M111.007690. doi: 10.1074/mcp.M111.007690. Epub 2011 Aug 29.
3
Investigation of an albumin-enriched fraction of human serum and its albuminome.人血清富含白蛋白组分及其白蛋白组的研究。
Proteomics Clin Appl. 2007 Jan 1;1(1):73-88. doi: 10.1002/prca.200600276.
4
High-throughput generation of selected reaction-monitoring assays for proteins and proteomes.高通量生成用于蛋白质和蛋白质组的选择反应监测分析方法。
Nat Methods. 2010 Jan;7(1):43-6. doi: 10.1038/nmeth.1408. Epub 2009 Dec 6.
5
Characterization of the human plasma phosphoproteome using linear ion trap mass spectrometry and multiple search engines.采用线性离子阱质谱联用和多个搜索引擎对人血浆磷酸化蛋白质组进行鉴定。
J Proteome Res. 2010 Feb 5;9(2):876-84. doi: 10.1021/pr900780s.
6
Artificial decoy spectral libraries for false discovery rate estimation in spectral library searching in proteomics.用于蛋白质组学中基于光谱库搜索的错误发现率估计的人工诱饵光谱库。
J Proteome Res. 2010 Jan;9(1):605-10. doi: 10.1021/pr900947u.
7
A list of candidate cancer biomarkers for targeted proteomics.一份用于靶向蛋白质组学的候选癌症生物标志物清单。
Biomark Insights. 2007 Feb 7;1:1-48.
8
A guide to the Proteomics Identifications Database proteomics data repository.蛋白质组学鉴定数据库蛋白质组学数据储存库指南。
Proteomics. 2009 Sep;9(18):4276-83. doi: 10.1002/pmic.200900402.
9
Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry.串联质谱产生的超大蛋白质组学数据集的蛋白质鉴定假发现率。
Mol Cell Proteomics. 2009 Nov;8(11):2405-17. doi: 10.1074/mcp.M900317-MCP200. Epub 2009 Jul 16.
10
Effect of dynamic exclusion duration on spectral count based quantitative proteomics.动态排除时间对基于谱计数的定量蛋白质组学的影响。
Anal Chem. 2009 Aug 1;81(15):6317-26. doi: 10.1021/ac9004887.

高可信度的人类血浆蛋白质组参考集,PeptideAtlas 估计其肽段浓度。

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas.

机构信息

Institute for Systems Biology, Seattle, WA 98109, USA.

出版信息

Mol Cell Proteomics. 2011 Sep;10(9):M110.006353. doi: 10.1074/mcp.M110.006353. Epub 2011 Jun 1.

DOI:10.1074/mcp.M110.006353
PMID:21632744
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3186192/
Abstract

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.

摘要

人血浆可以相对无创地获得,并且包含来自身体大多数(如果不是全部)组织的蛋白质。因此,广泛的、定量的血浆蛋白质目录是发现疾病生物标志物的重要起点。2005 年,我们表明,使用不同的样品制备和分析技术的不同蛋白质组学测量方法会鉴定出明显不同的蛋白质组,并且只有通过结合来自许多不同实验的数据,才能综合编制血浆蛋白质组。我们应用了为串联 MS 测量的肽的分析和集成非常大且多样化的数据集而开发的先进计算方法,现在已经编译了一个高可信度的人类血浆蛋白质组参考集,其中包含的鉴定蛋白质是以前的高可信度蛋白质组的两倍多。它包括一个按照明确定义的方案在不同冗余级别上的蛋白质鉴定层次结构,我们将其作为一个标准,可以应用于任何蛋白质组数据集,以促进跨蛋白质组分析。此外,为了使用诸如选择反应监测等技术开发基于血液的诊断方法,我们使用谱计数提供了蛋白质浓度的粗略估计。我们鉴定了 20433 个独特的肽,从中推断出 1929 个蛋白质序列,具有 1%的错误发现率。我们通过 PeptideAtlas 提供了这个资源,PeptideAtlas 是一个大型的、多器官的、公共访问的肽综合目录,其中包含了世界各地实验室进行的串联 MS 实验中鉴定的肽。