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MRHMMs: multivariate regression hidden Markov models and the variantS.

Yeonok Lee1, Debashis Ghosh1, Ross C Hardison1

  • 1Department of Statistics and Department of Biochemistry and Molecular Biology, Penn State University, University Park, PA 16803, USA.

Bioinformatics (Oxford, England)
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Summary

We developed Multivariate Regression Hidden Markov Models (MRHMMs), a flexible software package for analyzing complex biological data. MRHMMs offers diverse emission structures and efficient computation for large datasets in genomics and genetics.

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Area of Science:

  • Genomics and Genetics
  • Computational Biology
  • Statistical Modeling

Background:

  • Hidden Markov Models (HMMs) are widely used in scientific studies, particularly in genomics and genetics.
  • Distinct regulatory regimes exist within genomes, influencing multivariate feature relationships.
  • Examples include differential gene regulation and transcription factor combinatorial patterns.

Purpose of the Study:

  • To develop a flexible software package, MRHMMs (Multivariate Regression Hidden Markov Models and the variantS), for various HMM applications.
  • To supplement existing HMM software by providing diverse emission probability structures.
  • To offer a computationally efficient solution for analyzing large-scale genomic datasets.

Main Methods:

  • Developed MRHMMs software package implemented in C for computational efficiency.
  • Incorporated a diverse set of emission probability structures, including mixture of multivariate normal distributions and logistic regression models.
  • Designed for flexibility to accommodate various HMMs and user-defined models.

Main Results:

  • MRHMMs accommodates a variety of HMMs applicable to biological studies and beyond.
  • Provides diverse emission probability structures, enhancing analytical capabilities.
  • Demonstrates computational efficiency for analyzing large datasets.

Conclusions:

  • MRHMMs is a flexible and efficient software package for analyzing complex biological data using various HMMs.
  • The software's diverse emission structures and computational speed make it valuable for genomics and genetics research.
  • MRHMMs is amenable to implementing alternative models, meeting diverse user needs.