A Hands-on Introduction to Hidden Markov Models

In this Lesson, we describe a classroom activity that demonstrates how a Hidden Markov Model (HMM) is applied to predict a eukaryotic gene, focusing on predicting one exon-intron boundary. This HMM lesson is part of the BIOL/CS 370 ‘Introduction to Bioinformatics’ course (Truman State University, MO) and of Bio4342 ‘Research Explorations in Genomics’ (Washington University in St. Louis, MO). The original target student audiences include both Biology and Computer Sciences majors in their junior and senior years, although we believe the model activity would be successful with younger students. The class session starts with a brief introductory lecture describing HMMs and the terminology used in defining the parameters for a given model. This lecture is followed by students’ exploration of the HMM using Excel spreadsheets to manage calculations while they alter the key variables; collaborative problem solving and discussion of their strategies and results; and homework to check their understandings. Students have reacted very positively to the HMM curriculum. Students with more computer science experience tended to ask more questions concerning the model itself. Overall, students performed well on the homework assignment, leading us to believe that we are a step closer to our main goal of filling the intellectual gap between computer scientists and biologists. Citation: Weisstein, A.E. , Gracheva, E. , Goodwin, Z. , Qi, Z. , Leung, W. , Shaffer, C.D. and Elgin, S.C. R. 2016. A Hands-on Introduction to Hidden Markov Models. CourseSource. https://doi.org/10.24918/cs.2016. 8 Editor: Mark Pauley, University of Nebraska at Omaha, Omaha, NE Received: 04/05/2016; Accepted: 05/09/2016; Published: 06/13/2016 Copyright: © 2016 Weisstein, Gracheva, Goodwin, Qi, Leung, Shaffer and Elgin. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. Conflict of Interest and Funding Statement: This work was supported by a Freiberg Visiting Fellowship for AW from Washington University; WL and EG are supported by IUSE grant #1431407 from the National Science Foundation to Washington University (for SCRE), with additional support for WL from grant #52007051 from the Howard Hughes Medical Institute (HHMI) Precollege and Undergraduate Science Education Professors Program to Washington University (for SCRE). ZG, ZQ, CDS and SCRE are supported by Washington University in St. Louis. Authors declare no conflict of interest. Supporting Materials: S1.Hidden Markov Models-description, S2.Hidden Markov Models-Lecture Slides version1, S3.Hidden Markov Models-Additional notes for Introductory Lecture, version 1, S4.Hidden Markov Models-Lecture Slides version 2, S5.Hidden Markov Models-Student Manual, S6.Hidden Markov ModelsSpreadsheet for 5’ Splice Site Prediction, S7.Hidden Markov Models-Homework, S8.Hidden Markov Models-Additional references, S9.Hidden Markov ModelsHMM examples and S10.Hidden Markov Models-Suggested manipulations for the Spreadsheet. *Correspondence to: One Brookings Dr., Department of Biology, Campus Box 1137, Washington University in St Louis, St Louis, MO 63130 E-mail: selgin@biology.wustl. edu CourseSource | www.coursesource. org 2016 | Volume 03 1 Lesson