obaDIA

obaDIA performs automated quantitative proteomics analysis by processing protein sequences in FASTA and abundance matrices (fragment-level, peptide-level, or protein-level) from DIA and by accepting protein-level abundance data from other quantitative proteomic techniques to identify differential protein expression and enable functional and pathway-level interpretation.

Key Features:

• Data Compatibility: Accepts protein sequence files in FASTA format and abundance matrices at fragment-level, peptide-level, or protein-level derived from DIA experiments and supports protein-level abundance data from other quantitative proteomic techniques.
• Automated analysis pipeline: Provides a fully automated workflow that integrates data quality evaluation, data mining, and differential protein expression analysis.
• Data quality evaluation and mining: Integrates quality assessment and data-mining procedures to evaluate the reliability of input proteomic data.
• Differential protein expression analysis: Identifies proteins with significant changes in expression between experimental conditions.
• Functional annotation and enrichment analysis: Performs comprehensive functional annotation and enrichment analysis using both total and expressed protein backgrounds.
• KEGG pathway mapping and visualization: Maps differentially expressed proteins onto KEGG pathways and generates pathway-level visual outputs.

Scientific Applications:

• High-throughput quantitative proteomics: Enables processing and analysis of large-scale proteomic datasets derived from DIA and other quantitative techniques.
• Differential expression studies: Supports identification of condition-specific changes in protein abundance.
• Functional and pathway analysis: Links proteins to biological functions and KEGG pathways to interpret molecular mechanisms.
• Exploratory and hypothesis-driven research: Produces annotation, enrichment, and pathway-mapping outputs to support exploratory analyses and hypothesis generation.

Methodology:

Accepts FASTA protein sequences and abundance matrices (fragment-, peptide-, or protein-level) from DIA and protein-level abundance from other quantitative proteomic techniques; performs data quality evaluation and mining, differential protein expression analysis, functional annotation, enrichment analysis using total and expressed protein backgrounds, and maps differentially expressed proteins onto KEGG pathways.