Introduces students to the key biological concepts underlying computational gene prediction. Outcomes include ability to analyze and evaluate output of gene prediction programs, and reflect on their engagement with big data and computational output.
Listed in Teaching Materials | resource by group HHMI Science Education Alliance (SEA) Faculty Group
The field of modern genomics is built upon a foundation of big data and the use of computational resources. The teaching materials in this packet are aimed at helping students apply the key biological concepts underlying gene prediction, understand how gene prediction programs work, and how to analyze and apply the output of gene prediction/analysis programs. We also include an activity that allows students to reflect on their engagement with big data and computational output.
After completing this module, students should be able to:
- Identify open reading frames in a 6-frame translation, in both directions.
- Create a 6-frame translation from a DNA sequence and identify potential starts and stop coordinates.
- Explain the relationship between an ORF and a gene.
- Describe how coding potential helps to identify putative genes.
- Interpret GeneMark output.
- Recognize the role of genomics (Big Data) in biological research.
How is the module structured to promote student development as a scientist?
This module promotes students’ development as scientists by encouraging independence, facilitating peer collaboration and through explicit discussion of how the SEA-PHAGES program contributes to big data. The activities have students work with each other to interpret real data output on their phage. In addition, we have included two readings and discussion prompts that will allow students to reflect upon big data, and how their research within the SEA-PHAGES program relates and contributes to the field.
Intended Teaching Setting
Course level: This resource is adaptable for both major and non-major students
Instructional Setting: The module is designed for in-class teaching, but has suggestions for modification for use in online teaching.
Implementation Time Frame: This would likely be implemented over a few class days. The entire module would likely take between 2.5 - 3.5 hours to complete.
Teaching Notes - Gene Prediction Programs
Learning activity documents:
Slides for Gene Prediction Programs.pptx
Creating and Reading a 6-Frame Translation.docx
Find ORFs in a 6-Frame Translation.docx
Article discussion prompts.docx
Creating and Reading a 6-Frame Translation KEY.docx
Find ORFs in a 6-Frame Translation KEY.docx
Question bank for assessment
Question bank for assessment - KEY
Acknowledgments: HHMI Science Education Alliance, Denise Monti
- Article discussion prompts.docx(DOCX | 10 KB)
- Creating and reading a 6 Frame Translation.docx(DOCX | 35 KB)
- Creating and reading a 6 Frame Translation KEY.docx(JPG | 38 KB)
- Final Project Template_Prediction Programs.docx(DOCX | 8 KB)
- Find ORFs in a 6-Frame Translation KEY.docx(DOCX | 67 KB)
- Find ORFs in a 6-Frame Translation.docx(DOCX | 41 KB)
- Question Bank for assessment - KEY.docx(DOCX | 393 KB)
- Question Bank for assessment.docx(DOCX | 272 KB)
- Slides for Gene Prediction Programs.pptx(PPTX | 2 MB)
- Teaching Notes - Gene Prediction Programs.docx(DOCX | 15 KB)
- License terms
Cite this work
Researchers should cite this work as follows:
- Wisner, E., Bortz, B., Anders, K. R., Davis, W., Hughes, L. (2020). Understanding Gene Prediction Programs. HHMI Science Education Alliance (SEA) Faculty Group, QUBES Educational Resources. doi:10.25334/SAEG-TJ47
HHMI Science Education Alliance (SEA) Faculty Group
This publication belongs to the HHMI Science Education Alliance (SEA) Faculty Group group.
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