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Metagenomic Classification Using an Abstraction Augmented Markov Model.

Xiujun Sylvia Zhu1, Monnie McGee2

  • 11 Sabre Corporation , Southlake, Texas.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 1, 2015
PubMed
Summary
This summary is machine-generated.

The abstraction augmented Markov model (AAMM) analyzes genetic sequences using p-mer frequencies. This method is applied here for improved metagenomic classification accuracy.

Keywords:
DNA sequencingextensible Markov modelquasi-alignmentsecondary structure

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Markov models are foundational for sequence analysis.
  • Existing models may not fully capture complex genetic sequence patterns.
  • Metagenomic classification requires robust analytical tools.

Purpose of the Study:

  • To introduce and review the theory of the abstraction augmented Markov model (AAMM).
  • To demonstrate the application of AAMM in metagenomic classification.
  • To evaluate the effectiveness of AAMM for analyzing genetic sequences.

Main Methods:

  • Developed AAMM based on frequencies of consecutive words (p-mers) of a defined length.
  • Applied AAMM to metagenomic datasets for classification tasks.
  • Theoretical review of AAMM principles.

Main Results:

  • AAMM provides a framework for analyzing genetic sequences.
  • The application of AAMM in metagenomic classification shows promise.
  • P-mer frequency analysis is key to AAMM's functionality.

Conclusions:

  • AAMM is a viable extension of Markov models for genetic sequence analysis.
  • The AAMM approach offers potential for enhanced metagenomic classification.
  • Further research can explore AAMM's capabilities in diverse biological sequence analyses.