| 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>
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| | | 5 | EN-US;mso-bidi-language:AR-SA">In this bioinformatics adventure, we are going to continue to look amino acid sequences and compute similarity scores using the <i style="mso-bidi-font-style:normal">BLOSUM62</i> matrix, but we will take a glimpse at some of the tools that are available in the world of <i style="mso-bidi-font-style:normal">Bioconductor</i>. This set of tasks are set after Exercise 3 in the <i style="mso-bidi-font-style:normal">Sequence Similarity</i> materials</span></p>
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| 3 | <p>In the module, students first define sequence similarity and then investigate how similarity can be quantitatively compared between two similar length proteins using a Blocks Substitution Matrix (BLOSUM) scoring matrix. Students then look for local regions of similarity between a sequence query and subjects within a large database using Basic Local Alignment Search Tool (BLAST). Lastly, 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>
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| | | 13 | mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA"> is a collection of </span><span class="VerbatimChar"><span style="font-size:11.0pt;
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| | | 17 | EN-US;mso-bidi-language:AR-SA">R</span></span><span style="font-size:12.0pt;
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| | | 21 | mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA"> packages that has been built by several bioinformatics researchers to perform common calculations in their field. In their own words, ``<i style="mso-bidi-font-style:
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| | | 22 | normal">Bioconductor</i> provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, and an active user community.’’ </span></p>
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