XINA

XINA identifies coabundance patterns and infers molecular interactions from multiplexed, time-resolved proteomics and transcriptomics data, including isobaric tandem mass tagging (iTRAQ) experiments.

Key Features:

• Coabundance Pattern Identification: Detects proteins with similar abundance trajectories within and across experimental conditions by converting inter-dataset comparisons into intra-dataset comparisons using in silico tagging and dataset combination.
• In Silico Data Tagging and Combination: Mimics isobaric mass tagging (iTRAQ) computationally to tag multiple datasets and combine them for joint analysis.
• Cluster Subgrouping: Subgroups data into multiple clusters within a single output to depict variations in abundance trajectories across all studied conditions.
• Integration with Biological Databases: Incorporates protein-protein interaction databases and KEGG to associate coabundance patterns with potential interactors and molecular functions.
• Graphical Outputs: Generates visual representations of coabundance clusters and inferred functional relationships to support interpretation of dynamic proteomic or transcriptomic changes.

Scientific Applications:

• Time-resolved proteomics and transcriptomics analysis: Applies to multiplexed, time-course datasets to reveal temporal abundance changes across conditions or stimulations.
• iTRAQ-based quantitative proteomics: Analyzes isobaric tandem mass tagging experiments to measure protein abundance changes across multiple time points.
• Study of macrophage activation kinetics: Enables characterization of kinetics profiles (e.g., >5,600 unique proteins) and identification of key regulators involved in macrophage responses to stimulation.
• Delineation of dynamic protein interaction networks: Infers potential interactors and pathway associations to elucidate molecular underpinnings of cellular responses.

Methodology:

Computational in silico tagging of multiple datasets to mimic iTRAQ, combination of tagged datasets for intra-dataset coabundance analysis, subgroup clustering of abundance trajectories, and integration with protein-protein interaction databases and KEGG for functional inference and graphical output generation.