XINA

XINA determines which proteins exhibit similar abundance patterns within and across experimental conditions by importing multiple datasets, tagging datasets in silico, and combining the data for subsequent subgrouping into multiple clusters.

Key Features:

• Multiplexed Data Integration: XINA imports multiple datasets, tags datasets in silico, and combines the data into a single output depicting variation across all conditions.
• Co-abundance Profile Extraction: XINA extracts co-abundance profiles within and across experiments to identify proteins with similar patterns.
• Protein-Protein Interaction Integration: XINA incorporates protein-protein interaction databases to infer interactors.
• Functional Annotation: XINA incorporates integrative resources such as KEGG to infer molecular functions.
• Graphical Output Generation: XINA produces graphical outputs.

Scientific Applications:

• Co-abundance Pattern Identification: XINA identifies proteins with similar abundance patterns that may have common molecular functions.
• Network Analysis of Isobaric Mass Tagged-based Kinetics Data: XINA multiplexes isobaric mass tagged-based kinetics data for network analysis.
• Interactor Inference: XINA uses protein-protein interaction databases to infer protein interactors from co-abundance data.
• Molecular Function Prediction: XINA uses KEGG and other integrative resources to infer molecular functions of proteins with co-abundance patterns.

Methodology:

XINA imports multiple datasets, performs in silico tagging of datasets, combines the data, and performs subgrouping into multiple clusters. XINA incorporates protein-protein interaction databases and KEGG to infer interactors and molecular functions.