Hidden Markov Modeling with HMMTeacher

Camilo Fuentes-Beals; Alejandro Valdés-Jiménez; Gonzalo Riadi

doi:10.1371/journal.pcbi.1009703

Hidden Markov Modeling with HMMTeacher

PLoS Comput Biol. 2022 Feb 10;18(2):e1009703. doi: 10.1371/journal.pcbi.1009703. eCollection 2022 Feb.

Authors

Camilo Fuentes-Beals¹, Alejandro Valdés-Jiménez², Gonzalo Riadi³

Affiliations

¹ PhD Program in Sciences Mention Modeling of Chemical and Biological Systems, School of Bioinformatics Engineering, Center for Bioinformatics, Simulation and Modeling, CBSM, Department of Bioinformatics, Faculty of Engineering, University of Talca, Campus Talca, Talca, Chile.
² Center for Bioinformatics, Simulation and Modeling, CBSM, Department of Bioinformatics, Faculty of Engineering, University of Talca, Campus Talca, Talca, Chile.
³ ANID-Millennium Science Initiative Program Millennium Nucleus of Ion Channels-Associated Diseases (MiNICAD), Center for Bioinformatics, Simulation and Modeling, CBSM, Department of Bioinformatics, Faculty of Engineering, University of Talca, Talca, Chile.

Abstract

Is it possible to learn and create a first Hidden Markov Model (HMM) without programming skills or understanding the algorithms in detail? In this concise tutorial, we present the HMM through the 2 general questions it was initially developed to answer and describe its elements. The HMM elements include variables, hidden and observed parameters, the vector of initial probabilities, and the transition and emission probability matrices. Then, we suggest a set of ordered steps, for modeling the variables and illustrate them with a simple exercise of modeling and predicting transmembrane segments in a protein sequence. Finally, we show how to interpret the results of the algorithms for this particular problem. To guide the process of information input and explicit solution of the basic HMM algorithms that answer the HMM questions posed, we developed an educational webserver called HMMTeacher. Additional solved HMM modeling exercises can be found in the user's manual and answers to frequently asked questions. HMMTeacher is available at https://hmmteacher.mobilomics.org, mirrored at https://hmmteacher1.mobilomics.org. A repository with the code of the tool and the webpage is available at https://gitlab.com/kmilo.f/hmmteacher.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Markov Chains*
Probability
Software

Grants and funding

This work was funded by Agencia Nacional de Investigación y Desarrollo (ANID) – Millennium Science Initiative Program [NCN19_168] to GR, Fondecyt [grant number 11140869] to GR, Fondequip [EQM160063] to GR, and ANID Doctoral Fellowship [number 21200775] to CFB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.