Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA.
Electrophoresis. 2018 Jul;39(13):1543-1557. doi: 10.1002/elps.201700401. Epub 2018 Mar 1.
Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.
蛋白质组学数据处理、注释和分析在大规模高通量的 Bottom-up 蛋白质组学实验中经常会遇到重大障碍。鉴于最近基于蛋白质的大型数据集的增加，计算机工具开发的努力也空前增加；以至于，要跟上特定学术年度的所有进展变得越来越困难。然而，这些工具使植物蛋白质组学社区受益，因为它们可以解决数据分析和可视化中的关键问题，因为这些不断发展的开源和社区开发的工具在未来的研究工作中具有潜力。这篇综述将介绍和总结 2016-2017 年间开发和发布的 50 多个软件工具、数据库和资源，分为以下几类：数据预处理和分析工具、统计分析工具、肽鉴定工具、数据库和光谱库以及数据可视化和解释工具。最后，作者还将讨论为社区进行数据存档和验证数据集的工作。此外，作者还描述了当前最常用的蛋白质组学工具，旨在向新手读者介绍这个组学发现平台。
