EECS 395495 Algorithmic Techniques for Bioinformatics General Introduction

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

Plan for Today 1. 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 2. Have some technical discussions. 9/27/2012 2

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 9/27/2012 3

Emphasis of This Course Context: Biological problems change, but the computational techniques for solving them may be similar. Emphasis: 1. We will emphasize algorithmic techniques over specific bioinformatics problems. 2. We will emphasize algorithmic techniques – that are applicable to multiple bioinformatics problems, and – that can likely be adapted to solve new bioinformatics problems. 9/27/2012 4

Topics 1. 2. 3. 4. 5. 6. 7. 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) 8. Evolutionary Tree Comparison (2 meetings) 9/27/2012 5

Prerequisites for This Interdisciplinary Course 1. Technical knowledge about biology is useful, but not required. 2. Broad intellectual curiosity about computer science is essential. 3. EECS 336 Algorithms or equivalent mathematical maturity is required. 9/27/2012 6

Learning Strategies for Bioinformatics Research For biology students: 1. Learn CS materials as much as you need to start working on an interdisciplinary research project. 2. Start working on the project as soon as you can. Don’t wait! 3. Continue to learn CS materials while you are working on the project. 9/27/2012 7

Learning Strategies for Bioinformatics Research For CS students: 1. Learn biology materials as much as you need to start working on an interdisciplinary research project. 2. Start working on the project as soon as you can. Don’t wait! 3. Continue to learn biology materials while you are working on the project. 9/27/2012 8

Learning Strategies for Bioinformatics Research If you are a non-biology and non-CS student, 1. Learn biology and CS materials as much as you need to start working on an interdisciplinary research project. 2. Start working on the project as soon as you can. Don’t wait! 3. Continue to learn biology and CS materials while you are working on the project. 9/27/2012 9

Course Work and Grading Policy 1. Active participation in classroom discussions is required. Weekly reading assignments are required. 2. A survey paper is required. Original research is optional but encouraged. 3. One or more presentations may be required. 4. There will be no homework, midterm, or final. 9/27/2012 10

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. 9/27/2012 11

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. 9/27/2012 12

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. 9/27/2012 13

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. 9/27/2012 14

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. 9/27/2012 15

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. 9/27/2012 16

My Coordinates • Office: Technology Institute, Room M 324 • Phone: 847 -230 -9867 • Email: kao@northwestern. edu • Office Hours: 10: 30 --11: 30 on Tuesday and Wednesday or by appointment. 9/27/2012 17

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/eecs 395 -bioinformatics/index. html 9/27/2012 18