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EECS 395/495 Algorithmic Techniques for Bioinformatics

EECS 395/495 Algorithmic Techniques for Bioinformatics. General Introduction 9/27/2012 Ming-Yang Kao. Plan for Today. Go over the syllabus, including the following: Learning strategies for bioinformatics General ideas for survey papers General ideas for presentations

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EECS 395/495 Algorithmic Techniques for Bioinformatics

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  1. EECS 395/495 Algorithmic Techniques for Bioinformatics General Introduction 9/27/2012 Ming-Yang Kao

  2. Plan for Today • Go over the syllabus, including the following: • Learning strategies for bioinformatics • General ideas for survey papers • General ideas for presentations • General ideas for research projects • Have some technical discussions.

  3. Objective of Bioinformatics Objective: Use computation to effectively and efficiently extract information from biological data. Examples of Data: Data involving the following molecules: • DNA • RNA • Protein • Sugar

  4. Emphasis of This Course Context: Biological problems change, but the computational techniques for solving them may be similar. Emphasis: • We will emphasize algorithmic techniques over specific bioinformatics problems. • We will emphasize algorithmic techniques • that are applicable to multiple bioinformatics problems, and • that can likely be adapted to solve new bioinformatics problems.

  5. Topics • Sequence Similarity (2.5 meetings) • Suffix Trees (2 meetings) • Database Search (2 meetings) • DNA Sequencing (3 meetings) • RNA Secondary Structures (2.5 meetings) • Protein Peptide Sequencing (2.5 meetings) • Evolutionary Tree Reconstruction (2 meetings) • Evolutionary Tree Comparison (2 meetings)

  6. Prerequisites for This Interdisciplinary Course • Technical knowledge about biology is useful, but not required. • Broad intellectual curiosity about computer science is essential. • EECS 336 Algorithms or equivalent mathematical maturity is required.

  7. Learning Strategies for Bioinformatics Research For biology students: • Learn CS materials as much as you need to start working on an interdisciplinary research project. • Start working on the project as soon as you can. Don’t wait! • Continue to learn CS materials while you are working on the project.

  8. Learning Strategies for Bioinformatics Research For CS students: • Learn biology materials as much as you need to start working on an interdisciplinary research project. • Start working on the project as soon as you can. Don’t wait! • Continue to learn biology materials while you are working on the project.

  9. Learning Strategies for Bioinformatics Research If you are a non-biology and non-CS student, • Learn biology and CS materials as much as you need to start working on an interdisciplinary research project. • Start working on the project as soon as you can. Don’t wait! • Continue to learn biology and CS materials while you are working on the project.

  10. Course Work and Grading Policy • Active participation in classroom discussions is required. Weekly reading assignments are required. • A survey paper is required. Original research is optional but encouraged. • One or more presentations may be required. • There will be no homework, midterm, or final.

  11. General Ideas for Survey Papers • Step 1: Identify a research topic. • Step 2: Choose some, say, 3, papers on this topic. • Step 3: Describe the key biology problem addressed in these papers. • Step 4: Describe the key algorithmic problems formulated to solve this biology problem. • Step 5: Summarize the key algorithmic results. • Step 6: Summarize the key empirical results. • Step 7: Suggest directions or open problems for further research. • Step 8: Propose a reading list for further study.

  12. General Ideas for In-class Presentations Step 1: Pick a paper. Step 2: Describe the key biology problem addressed in this paper. Step 3: Describe the key algorithmic problems formulated to solve this biology problem. Step 4: Summarize the key algorithmic results and empirical results. Step 5: Suggest directions or open problems for further research.

  13. General Ideas for In-class Presentations Step 1: Pick a software package. Step 2: Demonstrate how to use it. Step 3: Suggest improvements for the package.

  14. General Ideas for In-class Presentations Step 1: Pick a databank of biological data. Step 2: Demonstrate how to use it. Step 3: Suggest improvements for the databank.

  15. General Ideas for Research Projects Step 1: Identify a biology problem. Step 2: Formulate the problem into an algorithmic problem: • Input: ... (specify what empirical data is available) • Output: ... (specify what information you are seeking) Step 3: Come up with some ideas for algorithms for this problem by yourself or in collaboration with others (e.g., fellow students or me). Step 4: Design algorithms for this algorithmic problem. Step 5: • Implement the algorithms and perform empirical studies. • Analyze and prove the correctness and performance of the algorithms. Step 6: Write up a paper. Step 7: Submit the paper to a conference and/or a journal.

  16. Required Textbooks Neil C. Jones and Pavel A. Pevzner • An Introduction to Bioinformatics Algorithms • MIT Press, 2004. Wing-Kin Sung • Algorithms in Bioinformatics: A Practical Introduction • CRC Press, 2009.

  17. My Coordinates • Office: Technology Institute, Room M324 • Phone: 847-230-9867 • Email: kao@northwestern.edu • Office Hours: 10:30--11:30 on Tuesday and Wednesday or by appointment.

  18. My Home Page and Class Home Page My home page: http://www.cs.northwestern.edu/~kao Class home page, including a complete syllabus: http://www.cs.northwestern.edu/~kao/eecs395-bioinformatics/index.html

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