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#56, v5.0
#2404, v1.0
Title
| Old Version | New Version | ||
|---|---|---|---|
| 1 | Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence alignment in introductory undergraduate biology courses | 1 | Developing a phylogram from computational resources. |
Authors
| Old Version | New Version | ||
|---|---|---|---|
| 1 | Sabrina Robertson (North Carolina State University) | 1 | Melanie Lenahan () |
| 2 | Carlos Christopher Goller (North Carolina State University) | 2 | Shea Cinquemani (Raritan Valley Community College) |
| 3 | Steven Roof (Fairmont State University) | 3 | Melanie Lenahan () |
| 4 | Benita Brink (Adams State University) | ||
| 5 | Adam Kleinschmit (Adams State University) | ||
| 6 | Hayley Orndorf (University of Pittsburgh) | ||
Description
| Old Version | New Version | ||
|---|---|---|---|
| 1 | <p>Introductory bioinformatics exercises often walk students through the use of computational tools, but often provide little understanding of what a computational tool does "under the hood." A solid understanding of how a bioinformatics computational algorithm functions, including its limitations, is key for interpreting the output in a biologically relevant context. This introductory bioinformatics exercise integrates an introduction to web-based sequence alignment algorithms with models to facilitate student reflection and appreciation for how computational tools provide similarity output data. The exercise concludes with a set of inquiry-based questions in which students may apply computational tools to solve a real biological problem.</p> | 1 | <p>Introductory bioinformatics exercises often walk students through the use of computational tools, but often provide little understanding of what a computational tool does "under the hood." A solid understanding of how a bioinformatics computational algorithm functions, including its limitations, is key for interpreting the output in a biologically relevant context. This introductory bioinformatics exercise integrates an introduction to web-based sequence alignment algorithms with models to facilitate student reflection and appreciation for how computational tools provide similarity output data. The exercise concludes with a set of inquiry-based questions in which students may apply computational tools to solve a real biological problem.</p> |
| 2 | 2 | ||
| 3 | <p>In the module, students | 3 | <p>In the module, students access text-based FASTA-formatted sequence information via National Center for Biotechnology Information (NCBI) databases as they collect sequences for a multiple sequence alignment using Clustal Omega to generate a phylogram and evaluate evolutionary relationships. The combination of diverse, inquiry-based questions, paper models, and web-based computational resources provides students with a solid basis for more advanced bioinformatics topics and an appreciation for the importance of bioinformatics tools across the discipline of biology.</p> |
| 4 | 4 | ||
| 5 | <p><strong>CourseSource Citation</strong></p> | 5 | <p><strong>CourseSource Citation</strong></p> |
| 6 | 6 | ||
Quote
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Attachments
| 1 | link — Sequence Similarity: An inquiry based and "under the hood" approach for incorporating molecular sequence alignment in introductory undergraduate biology courses | CourseSource | 1 | file — Sequence Similarity - Exercise 3_Cinquemani_Lenahan.docx |
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| 2 | file — ./sequence_sim.JPG | 2 | file — Sequence Similarity - Exercise 3_Cinquemani_Lenahan.pdf |
| 3 | file — publication_1264_2596/sequence_sim.JPG |