Data processing pipelines for comprehensive profiling of proteomics samples by label-free LC MS for biomarker discovery
Data processing pipelines for comprehensive profiling of proteomics samples by label-free LC MS for biomarker discovery
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DOI：
10.1016/j.talanta.2010.10.029
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发表时间：
2011-01-30
期刊：
影响因子：
6.1
通讯作者：
Horvatovich, Peter
中科院分区：
文献类型：
--
作者：
Christin, Christin;Bischoff, Rainer;Horvatovich, Peter
过去十年，复杂体液的无标记定量 LC-MS 分析已成为生物标志物和生物知识发现的重要分析工具。对不同类型的质谱仪、质谱仪参数设置和应用的样品制备步骤所获取的数据进行准确的处理、统计分析和验证对于回答复杂的生命科学研究问题和了解疾病发生和发展的分子机制至关重要。本综述通过统计分析和验证深入了解无标签数据处理流程的主要模块，并讨论了最新进展。特别强调质量控制方法、完整工作流程的性能评估以及各个模块的算法。最后，该评论讨论了针对生物信息学知识很少的用户的高通量数据处理和分析解决方案的现状和趋势。 (c) 2010 Elsevier B.V. 保留所有权利。
Label-free quantitative LC-MS profiling of complex body fluids has become an important analytical tool for biomarker and biological knowledge discovery in the past decade. Accurate processing, statistical analysis and validation of acquired data diversified by the different types of mass spectrometers, mass spectrometer parameter settings and applied sample preparation steps are essential to answer complex life science research questions and understand the molecular mechanism of disease onset and developments. This review provides insight into the main modules of label-free data processing pipelines with statistical analysis and validation and discusses recent developments. Special emphasis is devoted to quality control methods, performance assessment of complete workflows and algorithms of individual modules. Finally, the review discusses the current state and trends in high throughput data processing and analysis solutions for users with little bioinformatics knowledge. (c) 2010 Elsevier B.V. All rights reserved.