Bioinformatics (3 Credits) 生物信息学

Bioinformatics (3 Credits)
Instructor Li LIAO ([email protected]), Dept. of Computer and Information Sciences, University of Delaware, USA
Synopsis Bioinformatics, as its name suggests, is to use informatics/computing approaches to solve biological
problems. In its short history, bioinformatics has made great progresses towards answering
important, fundamental questions in biology. This course introduces basic concepts, methodologies,
and tools in bioinformatics. From this course, students will learn some major computational methods
and techniques, including: Dynamic programming; Pairwise and multiple sequence alignment;
Phylogenetic tree reconstruction; Hidden Markov models; K-mean clustering. No prior knowledge of
molecular biology is assumed; the course has a primer on molecular biology, covering some basic
Offering 2015 Julmester
Audience Year 3 & 4 Undergraduate and Graduate Students
Classroom Room xxx, Teaching Bldg. No. XX, Peking University
Schedule Class: 8-11 AM, M-F, July 6–24, 2015; Final Exam: 8-11 AM, July 25, 2015
Objective The goals are 1) to pick up the concepts and vocabularies; 2) to become familiar with various
bioinformatics resources (tools and databases); and most importantly, 3) to master basic algorithms
and models.
Topics 1. A primer to molecular biology: Central dogma, DNA, RNA, Proteins, Clone, PCR, sequencing, DNA
microarray chips, yeast 2 hybrid.
2. Genome sequencing: Various sequencing techniques and strategies, physical mapping, sequence
3. Sequence alignments: pairwise and multiple sequence alignments, dynamic programming,
Needleman-Wunsch algorithm and Smith-Waterman algorithm, BLAST, significance analysis, evalue. Gene identification and functional annotation.
4. Hidden Markov models: Viterbi decoding algorithm, Baum-Welch algorithm for model training,
application to protein families and gene finding.
5. Phylogeny: Evolutionary models, phylogenetic trees and reconstruction methods, Fitch algorithm,
6. Structure prediction: protein secondary structure prediction, RNA structure prediction, lattice
7. Gene expression analysis: Profiling and clustering methods, k-means.
8. Gene regulatory network inference: Boolean networks.
1. M. Zvelebil and J. Baum, Understanding Bioinformatics; Garland Science (2008).
2. R. Durbin, S.R. Eddy, A. Krogh, and G. Mitchison, Biological Sequence Analysis: Probabilistic
Models of Proteins and Nucleic Acids; Cambridge University Press (1998).
3. P. Clote and R. Backofen, Computational Molecular Biology: An Introduction; John Wiley & Sons
Homework Assignments (4)
Midterm Exam
Final Exam