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An Integrated Open Source Software Resource for Quantitative Proteomics

用于定量蛋白质组学的集成开源软件资源

基本信息

批准号：
BB/I000631/1
负责人：
Simon Hubbard
金额：
$ 28.33万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FI000631%2F1
关键词：
Integrated Open Source Software Resource

项目摘要

In a scientific sense, a living system such as a plant, animal, organ or cell can be considered to be a complex machine. The basic components that make up this machine are molecules, of which there are several main types - genes, proteins and metabolites. To understand how these molecules work together to produce the complex living systems that we see around us we need to have analytical methods capable of detecting and quantifying these molecules. This proposal deals with one aspect of this analysis - proteomics - the science of identifying and quantifying proteins. The most popular approach in proteomics is to simplify a sample by separating all the proteins, digesting those proteins with an enzyme into much smaller components (peptides) and then analysing all these peptides with mass spectrometry (MS). Identification of proteins can then be carried out by computational analysis of the mass spectrum acquired from each peptide - peptides are usually mapped to proteins by comparison of observed spectra to those in a database. Protein quantity is typically calculated from mass spectral peak intensities, or by simply considering how many peptides have been observed from each protein. Within this general analytical schema there are a great many variations according to the laboratory that is doing the analysis, the samples being analysed, or the overall aim of the experiment. Factors that may differ between experimental protocols include the protein separation method (some people use gels, others liquid chromatography), different types of mass spectrometry, different search databases (some are simulated from protein sequences, others are libraries of experimentally acquired spectra), and different methods of quantitation (for instance there are various methods of labelling which are used to distinguish peptides from different samples during the analysis). This plethora of quantitative proteomic methods has two major disadvantages for proteomics practitioners. Firstly, it is a challenge to devise standard data formats for sharing proteomic data because there are so many experimental parameters to capture and different parameters are required for different protocols. Secondly, for each different protocol it can be necessary to perform a different computational analysis of the data - this has led to the development of many different software tools, particularly for quantitative proteomics in which each tool can be specific for a particular type of mass spectrometer, a particular type of labelling or a particular quantitation algorithm. The resulting array of incompatible software is bewildering to the typical proteomics practitioner, and because effort is spread across many tools there is limited resource to optimise the robustness and usability of each individual tool. In the work described in this proposal the four main centres of proteome informatics expertise in the UK aim to work together to develop an integrated suite of analysis and statistical processing tools for all popular variants of quantitative proteomics. The software will cover the whole range of quantitative proteomic data analysis, from extracting abundance data from the original MS spectra through to statistical analysis and deposition of results into the public proteomic data repository, PRIDE. A key component needed to get this working will be standard data formats to link each step of the data analysis. We will therefore be making a substantial contribution to the completion of the necessary quantitative data standards as part of this project. Overall, we aim to produce a robust, easy to use, standards-compliant software suite that will prove invaluable for proteomics practitioners seeking to analyse and share their quantitative proteomic data, regardless of the specific quantitative protocol they use.

在科学意义上，植物、动物、器官或细胞等生命系统可以被认为是一台复杂的机器。构成这台机器的基本组件是分子，其中有几种主要类型--基因、蛋白质和代谢物。为了了解这些分子如何协同工作，产生我们周围看到的复杂的生命系统，我们需要有能够检测和量化这些分子的分析方法。这项建议涉及这种分析的一个方面--蛋白质组学--识别和量化蛋白质的科学。蛋白质组学中最流行的方法是通过分离所有蛋白质来简化样本，用酶将这些蛋白质消化成更小的成分(多肽)，然后用质谱仪(MS)分析所有这些多肽。然后，可以通过计算分析从每个多肽获得的质谱图来识别蛋白质--多肽通常通过将观察到的光谱与数据库中的光谱进行比较来映射到蛋白质。蛋白质数量通常是根据质谱峰强度或简单地考虑从每个蛋白质中观察到多少肽来计算的。在这个一般的分析方案中，根据进行分析的实验室、被分析的样品或实验的总体目标，有许多不同之处。不同实验方案之间可能存在差异的因素包括蛋白质分离方法(一些人使用凝胶，另一些人使用高效液相色谱)、不同类型的质谱学、不同的搜索数据库(一些是从蛋白质序列模拟而来的，另一些是实验获得的光谱的库)，以及不同的定量方法(例如，在分析过程中有用于区分不同样品的多肽的各种标记方法)。这种过多的定量蛋白质组学方法对蛋白质组学从业者来说有两个主要的缺点。首先，设计用于共享蛋白质组数据的标准数据格式是一个挑战，因为有如此多的实验参数需要采集，并且不同的协议需要不同的参数。其次，对于每个不同的协议，可能需要对数据执行不同的计算分析--这导致了许多不同软件工具的开发，特别是对于定量蛋白质组学，其中每个工具可以针对特定类型的质谱计、特定类型的标记或特定的定量算法。由此产生的一系列不兼容的软件让典型的蛋白质组学从业者感到困惑，而且由于工作分散在许多工具上，因此优化每个工具的健壮性和可用性的资源有限。在这项提案中描述的工作中，英国的四个主要蛋白质组信息学专业中心旨在共同努力，为所有流行的定量蛋白质组学变体开发一套综合的分析和统计处理工具。该软件将涵盖所有定量蛋白质组数据分析，从从原始MS谱中提取大量数据，到统计分析和将结果存储到公共蛋白质组数据库Pride中。实现这一点所需的一个关键组件将是将数据分析的每一步联系起来的标准数据格式。因此，作为这一项目的一部分，我们将为完成必要的量化数据标准作出重大贡献。总体而言，我们的目标是生产一个强大的、易于使用的、符合标准的软件套件，对于寻求分析和共享他们的定量蛋白质组数据的蛋白质组学从业者来说，这将被证明是无价的，无论他们使用的是哪种具体的定量方案。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Computational phosphoproteomics: from identification to localization.

DOI：
10.1002/pmic.201400372
发表时间：
2015-03
期刊：
PROTEOMICS
影响因子：
3.4
作者：
Lee, Dave C. H.;Jones, Andrew R.;Hubbard, Simon J.
通讯作者：
Hubbard, Simon J.

The mzIdentML data standard for mass spectrometry-based proteomics results.

DOI：
10.1074/mcp.m111.014381
发表时间：
2012-07
期刊：
Molecular & cellular proteomics : MCP
影响因子：
0
作者：
Jones AR;Eisenacher M;Mayer G;Kohlbacher O;Siepen J;Hubbard SJ;Selley JN;Searle BC;Shofstahl J;Seymour SL;Julian R;Binz PA;Deutsch EW;Hermjakob H;Reisinger F;Griss J;Vizcaíno JA;Chambers M;Pizarro A;Creasy D
通讯作者：
Creasy D

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies.