PaperBLAST: Text Mining Papers for Information about Homologs.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Millions of protein-coding genes lack functional annotation despite large-scale genome sequencing.
• Functional information for similar proteins is often absent from databases and buried in scientific literature.

Purpose of the Study:

• To develop a tool that makes hidden functional information about uncharacterized proteins accessible to biologists.
• To bridge the gap between genomic data and functional insights by leveraging scientific literature.

Main Methods:

• PaperBLAST utilizes EuropePMC to search the full text of scientific articles for gene and protein references.
• It integrates curated resources like Swiss-Prot, GeneRIF, and EcoCyc to link protein sequences with literature.
• The system employs BLAST software to identify similar proteins within its database of over 700,000 articles.

Main Results:

• PaperBLAST's database contains information on over 400,000 different proteins mentioned in scientific articles.
• The tool efficiently retrieves and presents relevant text snippets from articles or curators for similar proteins.
• It provides a rapid method for scientists to find literature discussing proteins with sequence similarity.

Conclusions:

• PaperBLAST significantly enhances the accessibility of functional information for uncharacterized proteins.
• The tool aids biologists in predicting protein functions by connecting sequence similarity to published research.
• It facilitates a more efficient exploration of scientific literature for protein function discovery.