BISC 478 : Computational Genome Analysis
Instructors: Fengzhu Sun, Ting Chen, Frank Alber
Time: 9:30 – 10:50 am TTh
With the development of new biotechnologies, enormous amount of
data including molecular sequences, networks, pathways, structures,
genetic polymorphisms, etc. have been accumulated and deposited
into databases. Most biological laboratories and biotechnological
companies use these data sources for research and development. The
molecular data will also significantly improve our understanding of
human health. This course introduces students to the basics of
computational and statistical thinking within the context of biology
and for data analysis. The course content includes introduction to
probability and statistics, molecular sequence database searches,
gene expression analysis, phylogenetic analysis, and disease gene
identification.
The course is essential for biology students interested in using
modern computational and informatics tools to solve biological
problems and students with quantitative skills to apply their skills to
real world problems.
This is a required course for the minor in computational biology and
bioinformatics. Students with computational biology and
bioinformatics backgrounds are in high demand in academia,
industry, and government.
BISC 478 : Computational Genome Analysis
Time: 9:30 – 10:50 am TTh, Room: RRI 301
Discussion 4-4:50 TTh, Room: RRI 301
Instructors
Professor Tim Chen
Professor Fengzhu Sun
Professor Frank Alber
Phone: (213)740-2415 Email: tingchen@usc.edu
Phone: (213)740-2413 Email: fsun@usc.edu
Phone: (213)740-0778 Email:alber@usc.edu
T:1-3pm
TTH:11:00-12:30
TBA
Book: Computational Genome Analysis: An Introduction. (Deonier, Tavare, Waterman, Springer 2005)
Course Content
This course provides an introduction to the computational side of molecular biology, with an emphasis
on genome analysis. With the development of new biotechnologies, enormous amount of data
including molecular sequences, networks, pathways, structures, genetic polymorphisms, e. have been
accumulated and deposited into databases. Most biological laboratories and biotechnological
companies use these data sources for research and development. The molecular data will also
significantly improve our understanding of human health. This course introduces students to the basics
of computational and statistical thinking within the context of biology and for data analysis. The course
content includes introduction to probability and statistics, molecular sequence database searches, gene
expression analysis, phylogenetic analysis, and disease gene identification.
Grading
There are homework assignments (25%), and three examinations (two mid-terms and one final, 25%
pts each). Each examination will cover one-third of the content. All examinations will occur as
scheduled below. Note particularly that university regulations strictly regulate the final examination
date and time. Homework submitted for grading is to be the independent work of each individual
student.
BISC478
Date
Computational Genome Analysis
Topic
Lecture 1
Introduction to Genomes (Ch1, all)
Lecture 2
Words/ An Introduction to Probability, 1 (Ch2; 2.1-2.3.3)
Wk. 2
Lecture 3
Lecture 4
Words/ An Introduction to Probability, 2 (Ch2; 2.3.4-2.5)
Words/ An Introduction to Probability, 3 (Ch2; 2.6-2.9)
Wk. 3
Lecture 5
Lecture 6
Words/ An Introduction to Statistics, 1 (Ch3; 3.1-3.2)
Words/ An Introduction to Statistics, 2 (Ch3; 3.3-3.4.1)
Wk. 4
Lecture 7
Lecture 8
Words/ An Introduction to Statistics, 3 (Ch3; 3.4.2-3.6)
Physical Mapping-1 (Ch4; 4.1-4.4)
Wk. 1
Wk. 5
Lecture 9
Mini-review
Examination I
Wk. 6
Lecture 10 Genome Rearrangements (Ch5;5.1-5.2)
Lecture 11 Genome Rearrangements (Ch5;5.3-5.4)
Wk. 7
Lecture 12 Sequence Alignments (Ch6;6.1-6.4)
Lecture 13 Sequence Alignments (Ch6;6.5-6.8)
Wk. 8
Lecture 14 FASTA and BLAST (Ch7;7.1-7.2,7.5)
Lecture 15 FASTA and BLAST (Ch7;7.3-7.4)
Wk. 9
Lecture 16 Sequence Assembly (Ch8;8.1-8.3)
Lecture 17 Sequence Assembly (Ch8;8.3-8.4)
Spring Break
Spring Break
Wk. 10 Lecture 18 In-Class Discussion and Mini-review
Examination II
Wk. 11 Lecture 19 Clustering (Ch10; 10.1-10.3)
Lecture 20 Clustering (Ch10; 10.4-10.5)
Wk. 12 Lecture 21 Gene Expression (Ch11; 11.1-11.3)
Lecture 22 Gene Expression (Ch11; 11.4)
Wk. 13 Lecture 23 Gene Expression (Ch11; 11.5)
Lecture 24 Phylogenetics (Ch12; 12.1-3)
Wk. 14 Lecture 25 Phylogenetics (Ch12; 12.4-13.5)
Lecture 26 Genetic Variation (Ch13; 13.1-13.3)
Wk. 15 Lecture 27 Genetic Variation (Ch13; 13.4-13.6)
Lecture 28 Review
Final examination (Tue, 8:00-10:00 am, RRI301)