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Introductory course in Bioinformatics

Introductory course in Bioinformatics. 1st year freshman from any discipline of Science & Engineering 3 Credit hours course 12 weeks module. Pre-requisites. Concept of Basic Algebra. Knowledge of writing mathematical expression from word problems and from simple physical aspects.

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Introductory course in Bioinformatics

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  1. Introductory course in Bioinformatics 1st year freshman from any discipline of Science & Engineering 3 Credit hours course 12 weeks module

  2. Pre-requisites • Concept of Basic Algebra. • Knowledge of writing mathematical expression from word problems and from simple physical aspects. • Knowledge of Basic Descriptive Statistics, such as Central Tendency of frequency (data), Probability, Histogram etc. • Basic concept of using computer, such as managing data files, word files, text files etc. • Concepts of DNA, mRNA, nucleic acid. Amino acid etc. • Concepts of Gene. • A little bit of knowledge of the biological system in which the student is interested (can be developed with his own during the course).

  3. Learning Outcomes At the end of this course, students will be able to: - Use freely available bioinformatics tools. - Form a biological hypothesis to guide data analysis and interpretation. - Collect an appropriate data set for evaluating their hypothesis. - Provide a basic outline of the process used for global and local alignment.

  4. Lesson plan for Week-1 • An overview of the field of Bioinformatics – its importance, roles to unfold the mysteries of complex biological systems. • Introduction of different text books and reference books in bioinformatics. • Reviews on computer skills via small biological systems such as population growth, prey-predator relationship using Excel spreadsheet. (Ref: BioQuest Curriculum Consortium, Jungck, Fass and Stanley)

  5. Lesson plan for week-2 • Information of protein structures. • Basic knowledge of protein and amino acid chemistry. • Introduction of PAM : Point Accepted Mutations. Hands on experience on development of Dayhoff’s matrix in order to understand well-tolerated, conserved, single amino acid changes in highly conserved sequences. (Mathematical frame work of Dayhoff’s matrix and its optimization involves dynamic optimization hence out of the scope of this level)

  6. Lesson plan for week-3 • Introduction to MATLAB. • Develop program writing skill for small biological problems that involves both continuous and discrete variables with and without delays. • Introduction to Statistics toolbox, Bioinformatics toolbox and Simbiology toolbox in MATLAB. (It helps students to build their own code for different algorithms – it also bypass the knowledge of calculus if the students are not very much familiar with calculus)

  7. Lesson plan for week-4 Task: Explain scoring function to provide insight to how sequences are aligned Focus: Needleman-Wunsch Algorithm; global alignment of two sequences Needleman, SB and Wunsch, CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48:443-453, 1970. Key Concept: The best alignment mathematically speaking is NOT ALWAYS the best alignment biologically. We must balance our interpretation of an alignment using our knowledge of biology. Activity: Students perform global alignments using ALIGN at Biology Workbench, a global alignment using a dynamic programming strategy developed by Myers & Miller. Students make graphs in Excel to investigate how alignment score is changed by altering gap open, gap extension, and other parameters.

  8. Lesson plan for week-5 Task: Introduce students to Hands-On HIV session at BioQUEST Focus: Working with sequence data; forming a biologically meaningful hypothesis; Key Concept: A biologically meaningful hypothesis gives focus and purpose to bioinformatics analysis. Activity: Students work on BioQUEST HIV exercise. http://bioquest.org/bedrock/problem_spaces/hiv/index.php

  9. Lesson plan for week-6 Task: Students choose an independent project for remainder of semester Focus: Provide examples of possible projects and outline project flow Key Concept: Students will feel ownership of a project they choose and design (within provided guidelines). Students will have opportunity to apply bioinformatics tools to gain information on a system using real data. Activity: Students explore possible projects. CFTR: http://bioquest.org/bioinformatics/module/tutorials/Cystic_Fibrosis/index.html HIV: http://bioquest.org/bedrock/problem_spaces/hiv/index.php OMIM: http://www.ncbi.nlm.nih.gov/Omim/omimhelp.html#SampleSearches MHC: http://www.ncbi.nlm.nih.gov/gv/mhc/main.cgi?cmd=init (dbMHC at NCBI) http://www.ncbi.nlm.nih.gov/gv/mhc/html/tutorial.html Influenza Virus: http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html

  10. Lesson plan for weeks-7-12 Students continue work on their independent project. Student progress is reviewed/supported by weekly meeting with professor, and by peer-sharing of progress. Students are encouraged to provide advice and suggestions to one another.

  11. Lesson plan for weeks-13-14 Group presentation of completed projects. Sharing in a public forum would be advantageous.

  12. This is a work in progress, and we welcome suggestions and comments!

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