TopPICR

TopPICR provides an R package that implements a pipeline for label-free proteoform quantification and processing of top-down LC-MS/MS proteomics data.

Key Features:

• Extends TopPIC: Builds on the predecessor TopPIC to enable downstream quantification of identified proteoforms.
• Label-free proteoform quantification: Implements a comprehensive label-free quantification workflow for intact proteoforms.
• Filtering identifications: Retains high-confidence protein identifications for subsequent analysis.
• Inferring protein accessions: Determines a minimal set of protein accessions that explain observed sequences.
• Aligning retention times: Aligns retention times across samples to improve consistency and comparability.
• Recalibrating measured masses: Corrects mass measurement errors to ensure accurate mass values.
• Clustering features across data sets: Groups similar features from different data sets to facilitate comparative analysis.
• Compiling feature intensities (match-between-runs): Uses a match-between-runs approach to compile feature intensities across samples.
• MSnSet output: Produces an MSnSet object compatible with Bioconductor packages for downstream analysis.
• Proteoform visualization: Provides visualization to contextualize proteoforms within the parent protein sequence.
• Preserves proteoform-level information: Maintains information about post-translational modifications, isoforms, and proteolytic processing inherent to top-down proteomics.

Scientific Applications:

• Breast tumor xenograft analysis: Applied to top-down LC-MS/MS data from 10 human-in-mouse xenografts of luminal and basal breast tumor samples.
• Top-down proteomics studies: Enables identification and label-free quantification of intact proteoforms to address challenges in interpreting complex mass spectra and quantification.

Methodology:

Workflow steps explicitly include filtering identifications, inferring a minimal set of protein accessions, aligning retention times, recalibrating measured masses, clustering features across data sets, compiling feature intensities via match-between-runs, and exporting results as an MSnSet object.