SPIDER: software for protein identification from sequence tags with de novo sequencing error

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Mass spectrometry/mass spectrometry (MS/MS) is crucial for identifying novel proteins.
• De novo sequencing generates sequence tags from MS/MS spectra for database matching.
• Existing methods struggle with partially correct sequence tags, often due to mass-equivalent amino acid substitutions.

Purpose of the Study:

• To develop an efficient algorithm for matching error-containing sequence tags to database sequences.
• To introduce SPIDER, a software package for improved protein and peptide identification.
• To provide a publicly accessible tool for the scientific community.

Main Methods:

• Developed a novel algorithm to handle sequence tags with common errors, such as segment-for-segment substitutions.
• Implemented the algorithm into a user-friendly software package named SPIDER.
• Made the SPIDER software freely available online.

Main Results:

• The SPIDER algorithm efficiently matches sequence tags with errors to protein databases.
• SPIDER improves the accuracy of protein and peptide identification compared to methods relying on perfectly correct tags.
• The software package facilitates the identification of homologs even when de novo sequencing yields imperfect data.

Conclusions:

• SPIDER offers an effective solution for protein and peptide identification challenges posed by de novo sequencing errors.
• The developed algorithm and software enhance the reliability of MS/MS-based protein identification.
• SPIDER is a valuable, free resource for researchers in proteomics and bioinformatics.