MASCOT HTML and XML parser: an implementation of a novel object model for protein identification data

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry (MS) is crucial for protein identification and generates extensive proteomics data.
• Analyzing and storing this data efficiently presents a significant challenge in proteomics research.

Purpose of the Study:

• To design a data object model (PDOM) for protein identification data.
• To develop a parser that utilizes the PDOM to facilitate data analysis and storage.
• To create a flexible tool for managing MASCOT search results.

Main Methods:

• Developed a Protein Identification Data Object Model (PDOM).
• Created a parser compatible with HTML/XML files from MASCOT MS/MS and PMF searches.
• Implemented features for redundancy elimination and relational database output.
• Designed the system using free and open-source Java libraries.

Main Results:

• The PDOM and parser effectively process and structure protein identification data.
• Redundant information is eliminated, and data can be stored in relational databases.
• The system facilitates enhanced analysis of MASCOT search results.
• The parser is extensible for other search engines and usable as a standalone or integrated application.

Conclusions:

• The PDOM and parser offer an efficient solution for managing proteomics data from MASCOT.
• This tool aids in the storage and analysis of protein identification information.
• The extensible design serves as a template for future parser development in proteomics.
• Freely available source code promotes wider adoption and integration in bioinformatics workflows.