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Lesson with Exercises

Module 5. Translation: The need for an Open Reading Frame

In this module students will learn how mRNA is translated into a string of amino acids. After completing this module students will be able to determine the codons for specific amino acids as well as start and stop codons. They will be able to identify open reading frames for a given gene, define the phases of splice donor and acceptor sites and describe how they impact the maintenance of the open reading frame.

Module 3. Transcription, Part II: What happens to the initial transcript made by RNA pol II?

This module demonstrates how the transcript generated by RNA polymerase II (the pre-mRNA) is processed to become mature mRNA using the sequence signals identified in Module 2. After completing this module students will be able to use the genome browser to explain the relationships among pre-mRNA, 5′ capping, 3′ polyadenylation, splicing, and mRNA.

Module 2. Transcription, Part I: From DNA sequence to transcription unit

This module illustrates how a primary transcript (pre-mRNA) is synthesized using a DNA molecule as the template. After completing this module students will be able to explain the importance of the 5′ and 3′ regions of the gene for initiation and termination of transcription by RNA polymerase II, and identify the beginning and end of a transcript using the capabilities of the genome browser (RNA-Seq, Short Match).

Introduction to Dynamic Programming

Developed by Dr. Anton E. Weisstein (Truman State University) and Mingchao Xie (TA for Bio 4342), this lecture and exercise introduce students to the core algorithm (dynamic programming) used by many sequence alignment tools (e.g., BLAST). The exercise includes a spreadsheet with a dynamic programming matrix that allows students to explore the impact of different types of alignments (i.e. global, semiglobal, and local) and scoring systems on the resulting sequence alignment.

An Introduction to Hidden Markov Models

Developed by Dr. Anton E. Weisstein (Truman State University), Zongtai Qi and Zane Goodwin (TAs for Bio 4342), this curriculum introduces students to the idea of Hidden Markov Models (HMM) that forms the core component of most gene predictors. The lecture by Zongtai Qi uses weather prediction to illustrate the key concepts of the HMM, whereas the lecture by Zane Goodwin focuses on the HMM that models a splice donor site. The exercise developed by Dr. Weisstein includes a spreadsheet with a simple HMM exercise that models a splice donor site. The spreadsheet allows students to examine the impact of different transition and emission probabilities on splice site predictions. A video recording of Dr. Weisstein’s HMM presentation during the 2014 GEP Alumni Workshop is also available online. This HMM curriculum is also available on CourseSource.

Using mRNA and EST Evidence in Annotation

This exercise allows students to learn how to use mRNA and Expressed Sequence Tags (ESTs) evidence to assist their annotation efforts. An answer key is available for instructors.