Course Info Instructor: Jing Li 509 Olin Bldg Phone: X0356 - - PDF document

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Introduction to Computational Biology and Bioinformatics

EECS 458 CWRU Fall 2004

Course Info

• Instructor:

Jing Li

509 Olin Bldg Phone: X0356 Email: jingli@eecs.cwru.edu Office hours: MW 10:30-11:30am

• Class Meeting:

MWF 11:30am-12:20pm, SEARS 356

• Course Website:

http://vorlon.cwru.edu/~jxl175/teaching.htm eecs458@eecs.cwru.edu Course number: eecs458 (15091)

Students’ Info

• Name
• Email
• Background (dept, year, enrollment status,

what you know about algorithms, stat, bio and computational bio)

• Interests (expectation, topics, time you want

to spend on this course)

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Areas in computational biology

• Sequencing: to determine the sequence
• f DNA and Proteins
• Sequence analysis:

– Sequence comparison – Gene recognition – Pattern discovery

• Statistical genetics/polymorphisms:

– Gene mapping – Linkage and association study

Areas in computational biology

• Comparative genomics: phylogenetics,

evolution

• Functional genomics: gene expression

array

• System biology

– Pathways – Regulatory networks – Protein interaction networks

Areas in computational biology

• Proteomics:

– Sequencing, folding/structure, functions

• Drug discovery in the post-genomic era

– Biomarkers – High throughput screen – Docking

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Course Overview

• Intro to molecular bio

– Cell, DNA, RNA, Proteins – Central dogma – Human genome – DNA sequencing

• Intro to probability, hidden Markov

models, and applications

Course Overview

• Genomics (sequence analysis)

– Sequence alignments (dynamic programming, hmm) – Gene prediction (hmm) – Motif/Pattern identification (suffix tree, statistical methods)

• Genomic variations

– Gene mapping – Haplotype inference – Population genetics

Course Overview

• Evolution

– Phylogeny reconstruction

• Functional genomics

– Microarray data analysis (mining and learning)

• Proteomics

– Sequencing – Structure/function prediction

• System bio

– Pathways – Protein interaction networks

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Main Purposes

• Basic concepts/techniques in molecular

bio/genetics

• Current topics in comp bio
• Mathematical methods:

– DP, Graph, String algorithms – Learning algorithms (HMM, Clustering/ Classification) – Statistical methods

• Research experience

Course Format / Grade

• HW: 4 assignments, 40%

– Mainly design/analysis of algorithms

• Project/presentation: 60%

– Team of 2-3 – Topics will be provided or selected by your own. – Examples: proposal of new methods, improvement on previous results, applying methods on new formulations. – Presentation in class. – Term paper.

What you will not learn here:

• How to use existing software/databases
• How to collect data experimentally
• Molecular biology
• Genetics/ Epidemiology
• Statistics

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Relevant Courses

• EECS 433: Database Systems
• EECS 435: Data Mining
• EECS 454: Analysis of Algorithms
• EPBI 452: Statistical Methods in Human Genetics
• EPBI 457: Genetic Linkage Analysis
• EPBI 471: Statistical Aspects of Data Mining
• EPBI 491: Epidemiology: Application of Theory and Methods
• EPBI 492: Epidemiology: Statistical Methods and Modeling
• GENE 508: Bioinformatics and Computational Genomics
• GENE 509: Complex Genetic Traits
• STAT 445/446: Theoretical Statistics I/II
• BIOC 618: Biology and Mathematics of Microarray Studies

Reference books

0262161974 0521585198

Reference books

0412993910 0521629713

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Reference books

026202506 0805346333

Reference books

0131439812

http://www.ncbi.nlm.nih.gov/books/

Acknowledgements

• Professor Tao Jiang
• Professor Stefano Lonardi