Proteomics Wants cRacker: Automated Standardized Data Analysis of LC–MS Derived Proteomic Data

The large-scale analysis of thousands of proteins under
various
experimental conditions or in mutant lines has gained more and more
importance in hypothesis-driven scientific research and systems biology
in the past years. Quantitative analysis by large scale proteomics
using modern mass spectrometry usually results in long lists of peptide
ion intensities. The main interest for most researchers, however,
is to draw conclusions on the protein level. Postprocessing and combining
peptide intensities of a proteomic data set requires expert knowledge,
and the often repetitive and standardized manual calculations can
be time-consuming. The analysis of complex samples can result in very
large data sets (lists with several 1000s to 100 000 entries of different
peptides) that cannot easily be analyzed using standard spreadsheet
programs. To improve speed and consistency of the data analysis of
LC–MS derived proteomic data, we developed cRacker. cRacker
is an R-based program for automated downstream proteomic data analysis
including data normalization strategies for metabolic labeling and
label free quantitation. In addition, cRacker includes basic statistical
analysis, such as clustering of data, or ANOVA and t tests for comparison between treatments. Results are presented in
editable graphic formats and in list files.

过去数年间，在基于假说的科学研究与系统生物学领域，对数千种蛋白质在各类实验条件或突变株系中的大规模分析愈发受到重视。借助现代质谱技术开展的大规模蛋白质组学定量分析，通常会生成海量肽段离子强度列表。不过，多数研究者的核心关注点在于获取蛋白质层面的研究结论。对蛋白质组数据集的肽段强度进行后处理与整合，需要具备专业知识支撑，且这类重复性强、流程标准化的手动计算往往耗时不菲。复杂样本的分析往往会生成极为庞大的数据集（包含数千至十万余条不同肽段的条目列表），这类数据难以通过标准电子表格程序完成便捷分析。为提升液相色谱-质谱（LC–MS）衍生蛋白质组数据分析的效率与一致性，我们研发了cRacker工具。cRacker是一款基于R语言开发的程序，可实现蛋白质组数据的自动化下游分析，涵盖代谢标记与无标记定量的数据标准化策略。此外，该工具还集成了基础统计分析功能，包括数据聚类、方差分析（ANOVA）以及用于实验组间比较的t检验。分析结果可导出为可编辑的图形格式与列表文件。