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

• 批准号: BB/I00095X/1
• 负责人: Andrew Jones
• 金额: $ 23.8万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI00095X%2F1
• 关键词: Integrated; Open; Source; Software; Resource

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，一个生命系统，如植物、动物、器官或细胞，可以被认为是一个复杂的机器。构成这台机器的基本成分是分子，其中有几种主要类型——基因、蛋白质和代谢物。为了了解这些分子是如何共同作用，形成我们周围复杂的生命系统的，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-鉴定和定量蛋白质的科学。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品，用酶将这些蛋白质消化成更小的成分（肽），然后用质谱分析（MS）分析所有这些肽。然后可以通过计算分析从每个肽获得的质谱来进行蛋白质鉴定-通常通过将观察到的光谱与数据库中的光谱进行比较，将肽映射到蛋白质上。蛋白质的数量通常由质谱峰强度计算，或者简单地考虑从每个蛋白质中观察到多少多肽。根据进行分析的实验室、被分析的样本或实验的总体目标，在这个一般的分析图式中有很多变化。实验方案之间可能存在差异的因素包括蛋白质分离方法（有些人使用凝胶，有些人使用液相色谱法），不同类型的质谱法，不同的搜索数据库（有些是从蛋白质序列模拟的，其他是实验获得的光谱库），以及不同的定量方法（例如，有各种标记方法用于在分析过程中区分不同样品的肽）。这种过多的定量蛋白质组学方法对蛋白质组学从业者有两个主要的缺点。首先，设计用于共享蛋白质组学数据的标准数据格式是一个挑战，因为需要捕获的实验参数太多，不同的协议需要不同的参数。其次，对于每种不同的方案，可能需要对数据进行不同的计算分析，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，其中每种工具都可以针对特定类型的质谱仪，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。在本提案中描述的工作中，英国四个主要的蛋白质组信息学专业中心的目标是共同努力，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。该软件将涵盖定量蛋白质组学数据分析的整个范围，从原始质谱提取丰度数据到统计分析，并将结果存入公共蛋白质组学数据库PRIDE。实现此功能所需的一个关键组件将是用于链接数据分析的每个步骤的标准数据格式。因此，作为该项目的一部分，我们将为完成必要的定量数据标准作出重大贡献。总的来说，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，将证明宝贵的蛋白质组学从业者寻求分析和分享他们的定量蛋白质组学数据，无论他们使用的具体定量协议。

项目成果

Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets.

• DOI: 10.1038/nmeth.3902
• 发表时间: 2016-08
• 期刊: Nature methods
• 影响因子: 48
• 作者: Griss J;Perez-Riverol Y;Lewis S;Tabb DL;Dianes JA;Del-Toro N;Rurik M;Walzer MW;Kohlbacher O;Hermjakob H;Wang R;Vizcaíno JA
• 通讯作者: Vizcaíno JA

De novo derivation of proteomes from transcriptomes for transcript and protein identification.

• DOI: 10.1038/nmeth.2227
• 发表时间: 2012-12
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Evans, Vanessa C.;Barker, Gary;Heesom, Kate J.;Fan, Jun;Bessant, Conrad;Matthews, David A.
• 通讯作者: Matthews, David A.

MRMaid: The SRM Assay Design Tool for Arabidopsis and Other Species.

• DOI: 10.3389/fpls.2012.00164
• 发表时间: 2012
• 期刊: Frontiers in plant science
• 影响因子: 5.6
• 作者: Fan J;Mohareb F;Jones AM;Bessant C
• 通讯作者: Bessant C

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
• 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

The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience.

• DOI: 10.1074/mcp.o113.036681
• 发表时间: 2014-10
• 期刊: Molecular & cellular proteomics : MCP
• 作者: Griss J;Jones AR;Sachsenberg T;Walzer M;Gatto L;Hartler J;Thallinger GG;Salek RM;Steinbeck C;Neuhauser N;Cox J;Neumann S;Fan J;Reisinger F;Xu QW;Del Toro N;Pérez-Riverol Y;Ghali F;Bandeira N;Xenarios I;Kohlbacher O;Vizcaíno JA;Hermjakob H
• 通讯作者: Hermjakob H