PhosPiR: an automated phosphoproteomic pipeline in R. Issue 1 (8th December 2021)

• Record Type: Journal Article
• Authors: Hong, Ye; Flinkman, Dani; Suomi, Tomi; Pietilä, Sami; James, Peter; Coffey, Eleanor; Elo, Laura L
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-12-08
• Subjects: phosphoproteomics -- proteomics -- pipeline -- bioinformatics -- statistics -- data visualization; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab510
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.