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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
Speeding up tandem mass spectrometry-based database searching by longest common prefix
Chen Zhou1, Hao Chi, Le-Heng Wang
1Key Lab of Intelligent Information Processing, Chinese Academy of Sciences, Beijing 100190, China.
BMC Bioinformatics
|November 27, 2010
Summary
We developed an algorithm based on the longest common prefix (ABLCP) to improve protein identification efficiency in tandem mass spectrometry. This method reduces computational demand and identification time compared to traditional peptide indexing.
Area of Science:
- Proteomics
- Bioinformatics
- Computational Biology
Background:
- Tandem mass spectrometry is crucial for peptide and protein identification.
- Computational demands increase with larger databases, complex digestion, and modifications.
- Current peptide indexing methods are time-consuming, space-intensive, and inflexible.
Purpose of the Study:
- To develop a novel algorithm for efficient protein sequence database organization.
- To reduce computational burden and accelerate peptide identification in mass spectrometry.
Main Methods:
- Developed an algorithm based on the longest common prefix (ABLCP) coupled with suffix arrays.
- Implemented adjustments to handle enzymatic digestion and ensure no candidate peptides are omitted.
- Compared ABLCP performance against traditional peptide indexing.
Main Results:
- ABLCP efficiently organizes protein sequence databases, eliminating redundant peptides.
- Reduced peptide-spectrum matching times and overall identification time.
- ABLCP requires significantly less construction time and space than peptide indexing, with fewer restrictions.
Conclusions:
- The ABLCP algorithm enhances data analysis efficiency in proteomics.
- A software tool implementing ABLCP is publicly available for broader use.
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