Bioinformatics (3 Credits) 生物信息学 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 concepts. 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 assembly. 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, Bootstrap. 6. Structure prediction: protein secondary structure prediction, RNA structure prediction, lattice models. 7. Gene expression analysis: Profiling and clustering methods, k-means. 8. Gene regulatory network inference: Boolean networks. References Grading 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 (2000). Homework Assignments (4) 40% Midterm Exam 20% Final Exam 40% Total 100%