Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform.
Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform.
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DOI：
10.3390/proteomes8030015
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发表时间：
2020-07-08
期刊：
影响因子：
3.3
通讯作者：
Jagtap PD
中科院分区：
文献类型：
--
作者：
Mehta S;Easterly CW;Sajulga R;Millikin RJ;Argentini A;Eguinoa I;Martens L;Shortreed MR;Smith LM;McGowan T;Kumar P;Johnson JE;Griffin TJ;Jagtap PD
对于基于质谱的肽和蛋白质定量，基于前体质量峰（MS1）强度的无标记定量（LFQ）由于其动态范围、重现性和准确性被认为是可靠的。LFQ可以实现多肽水平的定量，这在蛋白质组学（分析携带翻译后修饰的肽）和多组学研究(如宏蛋白质组学（分析分类群特异性微生物肽）和蛋白质基因组学（分析非规范序列）中很有用。通过Galaxy平台可访问的生物信息学工作流程已被证明对分析此类复杂的多组学研究非常有用。然而，Galaxy平台内的工作流缺乏经过良好测试的LFQ工具。在本研究中，我们评估了moFF和FlashLFQ这两个开源LFQ工具，并在Galaxy平台中实现它们，通过既定的工作流程提供访问和使用。通过严格的测试和与工具开发人员的沟通，我们优化了每个工具的性能。评估的软件特性包括：(a)运行间匹配（MBR）；(b)使用多种文件格式作为输入，以改进量化；(c)容器和/或包装的使用；(d)分析大型数据集所需的参数；(e)软件性能的优化与验证。这项工作建立了一个软件实现、优化和验证的过程，并提供了两个健壮的软件工具，用于在Galaxy平台中进行基于lfq的分析。
For mass spectrometry-based peptide and protein quantification, label-free quantification (LFQ) based on precursor mass peak (MS1) intensities is considered reliable due to its dynamic range, reproducibility, and accuracy. LFQ enables peptide-level quantitation, which is useful in proteomics (analyzing peptides carrying post-translational modifications) and multi-omics studies such as metaproteomics (analyzing taxon-specific microbial peptides) and proteogenomics (analyzing non-canonical sequences). Bioinformatics workflows accessible via the Galaxy platform have proven useful for analysis of such complex multi-omic studies. However, workflows within the Galaxy platform have lacked well-tested LFQ tools. In this study, we have evaluated moFF and FlashLFQ, two open-source LFQ tools, and implemented them within the Galaxy platform to offer access and use via established workflows. Through rigorous testing and communication with the tool developers, we have optimized the performance of each tool. Software features evaluated include: (a) match-between-runs (MBR); (b) using multiple file-formats as input for improved quantification; (c) use of containers and/or conda packages; (d) parameters needed for analyzing large datasets; and (e) optimization and validation of software performance. This work establishes a process for software implementation, optimization, and validation, and offers access to two robust software tools for LFQ-based analysis within the Galaxy platform.
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影响因子：
4.4
作者：
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使用Galaxy框架改善蛋白质组学分析的灵活且可访问的工作流程。
影响因子：
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