isobar

isobar performs preprocessing, normalization, statistical modeling, and report generation for quantitative mass spectrometry proteomics data labeled with isobaric tags such as iTRAQ and TMT.

Key Features:

• Quantitative Comparison: Supports simultaneous quantitative comparison of 2 to 8 biological samples in a single mass spectrometric run using isobaric tags.
• Statistical Modeling: Implements statistical methods that account for variability across individual spectra and entire biological samples and integrates classical experimental designs with replicates.
• Performance Characterization: Has been tested on complex controlled-ratio test samples with ratios ranging from 100:1 to 1:100.
• Input Formats and Environment: Accepts input from Mascot, Phenyx, and mzIdentML formats and is distributed as an R Bioconductor package.
• PTM-Centric Analysis (isobar-PTM): Processes iTRAQ or TMT labeled peptides for PTM localization validation, robust ratio computation, and statistical assessment.
• Report Generation and Annotation: Produces spreadsheet reports that present quantitative results and annotate publicly available modification sites.

Scientific Applications:

• Quantitative Proteomics: Enables precise quantification and comparison of protein abundance across multiple samples using isobaric labeling.
• PTM Mapping: Supports large-scale identification and statistical analysis of post-translational modifications such as phosphorylation and acetylation.
• Cross-platform and Multi-laboratory Studies: Applicable to complex datasets generated by different mass spectrometry platforms and laboratories.
• Method Validation: Facilitates evaluation of quantification performance using controlled-ratio test samples.

Methodology:

Computational methods explicitly include preprocessing, normalization, statistical modeling that accounts for spectral and sample-level variability, ratio computation, PTM localization validation, statistical assessment, and spreadsheet report generation; supports Mascot, Phenyx, and mzIdentML inputs and is implemented as an R Bioconductor package.