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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Published on: December 7, 2021

Specialized hidden markov model databases for microbial genomics.

Martin Gollery1

  • 1University of Nevada, Reno 1664 N. Virginia Street Reno NV 89557-0014 USA.

Comparative and Functional Genomics
|July 17, 2008
PubMed
Summary
This summary is machine-generated.

Hidden Markov models (HMMs) are crucial for biological sequence analysis. This paper explores HMM algorithms and databases, offering methods for creating custom HMM targets for large-scale genomic studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Hidden Markov models (HMMs) are increasingly vital for analyzing biological sequences.
  • The scale of HMM databases and their application in genomics has rapidly expanded.
  • Current trends involve analyzing entire microbial genomes and large collections of genomes.

Purpose of the Study:

  • To review available HMM algorithms and databases.
  • To provide guidance on developing custom HMM targets.
  • To address the growing need for efficient HMM analysis in large-scale genomic research.

Main Methods:

  • Exploration of readily available HMM and HMM-like algorithms.
  • Review of publicly accessible HMM databases.
  • Outline of methodologies for custom HMM target development.

Main Results:

  • Identification of key HMM algorithms and databases relevant to modern biological sequence analysis.
  • Provision of practical strategies for users to create their own HMM targets.
  • Demonstration of the feasibility of applying HMMs to large-scale genomic datasets.

Conclusions:

  • The increasing importance of HMMs necessitates accessible tools and databases.
  • Developing custom HMM targets empowers researchers to tailor analyses to specific biological questions.
  • This work facilitates the efficient use of HMMs in the era of large-scale genome analysis.