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POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.

Jiawei Wang1, Bingjiao Yang2, Jerico Revote1

  • 1Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.

Bioinformatics (Oxford, England)
|September 15, 2017
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Summary

POSSUM is a new toolkit and web server that generates 21 types of Position-Specific Scoring Matrix (PSSM)-based feature descriptors for machine learning. This tool addresses the need for universal PSSM descriptor generation in bioinformatics.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Position-Specific Scoring Matrices (PSSMs) are crucial for representing protein sequence evolutionary information.
  • PSSM-based features enhance the performance of protein attribute predictors.
  • A lack of universal tools for generating diverse PSSM descriptors hinders research.

Purpose of the Study:

  • To introduce POSSUM, a versatile toolkit and web server for generating PSSM-based feature descriptors.
  • To provide a comprehensive solution for bioinformaticians and computational biologists needing PSSM feature extraction.
  • To facilitate feature extraction, selection, and benchmarking for machine learning models in bioinformatics.

Main Methods:

  • Development of the POSSUM toolkit and associated online web server.
  • Implementation of algorithms to generate 21 distinct types of PSSM-based feature descriptors.
  • Focus on creating a user-friendly and accessible platform for bioinformatics research.

Main Results:

  • POSSUM offers a universal solution for generating a wide array of PSSM-based feature descriptors.
  • The toolkit and web server address a critical gap in current bioinformatics tools.
  • Facilitates improved analysis and modeling pipelines through comprehensive feature generation.

Conclusions:

  • POSSUM provides a valuable resource for the bioinformatics community by enabling diverse PSSM feature generation.
  • The toolkit is expected to significantly aid in the development and benchmarking of machine learning models for protein analysis.
  • POSSUM contributes to more effective bioinformatics research by streamlining feature extraction processes.