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

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• De novo sequencing of MS/MS spectra generates sequence tags for protein identification.
• Accurate sequence tags enable protein homolog identification using tools like MS-BLAST.
• De novo sequencing frequently yields partially incorrect sequence tags, hindering protein identification.

Purpose of the Study:

• To develop an efficient algorithm for matching error-containing sequence tags to database sequences.
• To create a software package, SPIDER, for improved protein and peptide identification.
• To provide a free, publicly accessible tool for researchers in proteomics and bioinformatics.

Main Methods:

• Developed a novel algorithm to efficiently match sequence tags with common errors (e.g., segment replacement) to protein databases.
• Implemented the algorithm into a user-friendly software package named SPIDER.
• Made the SPIDER software freely available via the internet for public use.

Main Results:

• The SPIDER algorithm effectively matches sequence tags containing errors to database sequences.
• SPIDER facilitates more accurate and comprehensive protein and peptide identification from MS/MS data.
• The software package offers an efficient solution for a common challenge in de novo sequencing.

Conclusions:

• SPIDER provides an efficient and effective method for protein and peptide identification using error-prone sequence tags.
• The developed algorithm and software address a significant limitation in de novo sequencing for proteomics.
• SPIDER is a valuable, freely accessible resource for the scientific community involved in mass spectrometry-based protein identification.