PLIPS, an automatically collected database of protein lists reported by proteomics studies

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Proteomics studies generate extensive protein lists, often disseminated across numerous publications.
• Identifying relevant published protein data for new research is challenging.
• Existing methods lack efficient ways to aggregate and analyze protein lists from diverse studies.

Purpose of the Study:

• To develop a web-mining tool for collecting protein lists from full-text proteomics publications.
• To create a computational tool (PLIPS) for inferring relevant published proteomics studies based on protein list intersections.
• To provide a freely available resource for the proteomics research community.

Main Methods:

• Web mining to extract protein identifier tables from full-text scientific articles.
• Collection of approximately 800 independent proteomics studies and 1000 protein lists.
• Development of the Protein Lists Identified in Proteomics Studies (PLIPS) tool, utilizing statistical analyses for list comparison.

Main Results:

• Successfully collected a substantial dataset of protein lists from recent proteomics literature.
• Developed PLIPS, a functional web-based tool that accepts protein/gene identifiers as input.
• PLIPS can identify published proteomics studies with significant overlap to user-provided protein lists.

Conclusions:

• PLIPS facilitates the discovery of relevant published proteomics data, saving researchers time and effort.
• The tool aids in contextualizing new findings within the broader landscape of proteomics research.
• PLIPS represents a valuable resource for advancing proteomics-based discoveries and clinical applications.