Download ICS 20B Course Proposal Form-Bio-CS1

ICS 20X: Python Programming with applications to
Biology
Proposer: Dennis Kibler
Information and Computer Science
1) Level
a) Undergraduate (only first course for CS students)
b) Upper Division ( any level for biology students- even grad students I think would
be ok)
2) Catalog Description
ICS 20X: BIOINFORMATICS (4). The course assumes no background in
computer science or in biology. Fundamental programming concepts are
introduced using the language Python using a problem-oriented
approach. All problems come from molecular biology.
3) Prerequisites
a) none. Does not count as elective CS class.
4) Logistics
a) Final exam and nine homeworks
b) Final grade will be based on
(1) best 8 of 9 homeworks (80%)
(2) 20% final
5) Text Book
Python Programming: An Introduction to Computer Science by John
Zelle
6) Potential course overlaps
i) No overlap with any ICS courses. This is intended as a pre-CS21 class for
students who have no experience with programming.
7) Curriculum
This course is intended as pre-21 class for computer science students who have had
little or no experience in programming. It also allows students to test their interest in
computer science and experience one of the most significant application areas of
computer science. The structure of course is problem-oriented rather than tooloriented.
8) Potential Instructors (list all that are applicable)
a)
b)
c)
d)
Dennis Kibler,
Alex Thornton
Pierre Baldi
any of the new Bioinformatics faculty
9) Expected Frequency of Offering
a) Once a year
b) Which quarter(s)?
i) Fall
10)
Course Overview and Goals
This course is meant as a first course in programming for students in computer
science or biology. The goals of the course are:
(1) provide a problem-oriented approach to learning computer science and
programming
(2) provide a consistent set of examples from a single domain, computational
biology, so students can experience the increased application value of
computer science
(3) empower biologists to write simple programs for the computational
analysis of their data.
(4) provide students with a computational view on understanding biological
data.
(5) enable biology students to communicate more effectively with computer
scientists.
(6) provide all students with an understanding of the limitations and potentials
of computation.
11)
Topic Outline
a) This course outline may vary with individual instructors.
This is an ambitious syllabus that will be adjusted to match the students ability to
learn the material. Examples applications are all drawn from a single domain to
demonstrate the significance of programs that students are learning to write. This also
provides a natural justification for objects and modules. In general the weekly syllabus is
divided into two parts: the biological problem being addressed and the programming
tools sufficient to address the problem. Theoretical computer science questions are also
introduced, but are not discussed in depth.
1. Introduction to Python and the process of program development
2. Computing the GC-Count of a dna file: strings, for-loops, file-io
3. A simple program to identify genes: file-io, while-loops, strings, functions
4. Computing dinucleotides/trinucleotides frequencies: hash tables
5. Creating and analyzing kmer distributions and signatures: hash tables, sorting
6. Finding known patterns in dna and amino acid sequences: regular expressions
7. Discovering patterns (regulatory elements) in dna sequences: modules
8. Retrieving selecting information from GenBANK: modules,
9. Comparing genes: arrays, dynamic programming
10. Clustering sequences and gene-expression data: objects