Advanced Biological Algorithms
After this course, students will be prepared for an internship or to work as a bioinformatics technician
Prerequisites BIOL 1650, CS 2420
Required Books Bioinformatics Algorithms: An Active
Learning Approach Vol 1 and Vol 2
This advanced course is intended for bioinformatics majors, but is open to anyone meeting the prerequisites. The purpose of this course is to explore bioinformatics algorithms in depth so students can understand the fundamental approaches to common algorithms. We examine the benefits and flaws associated with each algorithm. Students are asked to solve a real biological problem either individually or as a group using the algorithms learned in class. Topics include: DNA sequence alignment, viral origin identification, Hidden Markov Models for changes in expected probability, pattern identification, and DNA comparison techniques.
The purpose of BIOL 3650 is to enable students to feel comfortable as an intern or technician at a bioinformatics company. To assess learning outcomes, students will be asked to write various algorithms and submit them to www.rosalind.info. Throughout the semester, we will discuss possible project ideas, and students will be asked to choose one of these projects, obtain results using at least one of the algorithms learned, write a scientific report of their discovery, and present their findings to the class. By the end of the semester, students should feel comfortable doing bioinformatics research.
Aligning DNA or protein sequences is often performed to begin analyzing a gene. Sequences with more similar alignments (i.e., less mutations) typically share more similar functions than sequences with poor alignments. Using a programming language of your choice, create a global alignment algorithm which penalizes gaps with a gap penalty of 5. Substitution scoring is assessed based on the BLOSUM62 scoring matrix found on Rosalind.
Global alignments are very similar to local alignments. Students will be assessed on their ability to successfully answer the question at Rosalind. In this case, the global alignment takes into account the entire Manhattan graph, instead of just looking for the highest scoring subsequence. For more information on how to build alignments, see chapter 5 in the book, or check out their explanation here.