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An efficient algorithm for the blocked pattern matching problem.

Fei Deng1, Lusheng Wang1, Xiaowen Liu2

  • 1Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

A new algorithm efficiently solves the blocked pattern matching problem for gapped tags, accelerating peptide identification in large-scale proteomics. This advancement enhances speed and sensitivity in mass spectrometry data analysis.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Tandem mass spectrometry (MS) is crucial for protein identification and quantification in big data biology.
  • Searching vast protein databases for peptide identification using MS data is computationally intensive.
  • Existing tag-based methods improve search speed, but gapped tags, while more sensitive, present unsolved challenges in pattern matching.

Purpose of the Study:

  • To address the unsolved blocked pattern matching (BPM) problem in gapped tag-based peptide identification.
  • To develop a fast and memory-efficient algorithm for the BPM problem.
  • To enhance the speed and sensitivity of peptide identification in large-scale MS database searches.

Main Methods:

  • A novel algorithm is proposed to solve the blocked pattern matching problem.
  • The algorithm is designed for speed and memory efficiency.
  • Performance is evaluated using both simulated and real-world mass spectral datasets.

Main Results:

  • The proposed algorithm demonstrates high speed in solving the BPM problem.
  • The algorithm achieves high sensitivity for peptide filtration.
  • Experimental results confirm the algorithm's effectiveness on diverse datasets.

Conclusions:

  • The developed algorithm provides an efficient solution for the BPM problem in gapped tag-based peptide identification.
  • This advancement significantly speeds up database searches in proteomics.
  • The algorithm enhances the overall sensitivity of peptide identification, contributing to more comprehensive proteomic analyses.