IsobariQ

IsobariQ performs quantitative analysis of mass spectrometry-based proteomics data from isobaric labeling methods (IPTL, iTRAQ, TMT) by extracting reporter- and fragment-based intensities and applying statistical normalization for accurate relative quantification.

Key Features:

• Supported labeling techniques: Supports Isobaric Peptide Tandem Labeling (IPTL), iTRAQ (Isobaric Tags for Relative and Absolute Quantitation), and TMT (Tandem Mass Tag) experiments.
• Reporter-ion quantification: Extracts and quantifies relative intensities of reporter ions in the low-mass region for iTRAQ and TMT.
• Fragment-spectrum quantification (IPTL): Leverages MS/MS fragment spectra and isobaric peptide termini labeling to quantify reporter information distributed across the spectrum for IPTL.
• Statistical normalization: Implements Variance Stabilizing Normalization (VSN) algorithms to address heterogeneity of variance in proteomics datasets.
• R implementation: Employs the R programming language for analysis and statistical processing.
• Experimental validation: Validated on experimental datasets including 6-plex TMT and IPTL experiments.
• Protease-substrate identification: Identifies protein substrates resulting from protease cleavage, exemplified by detection of caspase targets in apoptosis studies.

Scientific Applications:

• Isobaric-label quantification: Relative quantification of proteins in IPTL, iTRAQ, and TMT mass spectrometry experiments.
• Protease cleavage analysis: Identification of protease-generated protein substrates such as caspase targets during apoptosis.
• Variance-aware comparative studies: Comparative quantification across samples with heteroscedastic variance using VSN normalization.
• Workflow benchmarking: Validation and benchmarking of isobaric labeling workflows using 6-plex TMT and IPTL datasets.

Methodology:

Extraction and quantification of reporter-ion intensities (low-mass region) and MS/MS fragment-spectrum intensities for IPTL, followed by Variance Stabilizing Normalization (VSN) implemented in R.