Difference between revisions of "CH391L 2013"

From Marcotte Lab
Jump to: navigation, search
(Lectures & Handouts)
(Helpful links)
(4 intermediate revisions by one user not shown)
Line 24: Line 24:
May 2, 2013 - Some of the (few!) publicly-viewable final projects.  Many private ones this time!
* [http://microarray.icmb.utexas.edu/annab_ch391l_project/ An Investigation of the Effects of Sequencing Depth on Binding Region Identification in ChIP-seq Data]
* [https://www.sites.google.com/site/bioinfonsa237/ Extraction and Analysis of microRNA Precursor Sequences from Human RNA-Seq Datasets]
* [https://sites.google.com/site/structureandevolutionofaprd4/home Structure and Evolution of AprD4]
* [https://sites.google.com/site/emilo83/home Identifying differentially expressed genes]
* [https://sites.google.com/site/finalprojectch391l/home Changes in host gene expression from Bordetella infection or toxicity]
* [https://sites.google.com/site/bioinformaticsfinalproject/home Diagnosing breast cancer based on PBMC gene expression profile using Bayesian additive regression trees (BART) method]
* [https://sites.google.com/a/utexas.edu/2013_bioinformatics/home Divergence of FRIGIDA in Arabidopsis thaliana ecotypes]
April 30, 2013 - Synthetic biology
April 30, 2013 - Synthetic biology
* [http://www.marcottelab.org/users/CH391L/Handouts/GenomeTransplantation.pdf Genome Transplantation]
* [http://www.marcottelab.org/users/CH391L/Handouts/GenomeTransplantation.pdf Genome Transplantation]
Line 263: Line 272:
** [http://www.cs.utexas.edu/~bajaj/cs384R2012/ Geometric (Bio-) modeling and visualization]
** [http://www.cs.utexas.edu/~bajaj/cs384R2012/ Geometric (Bio-) modeling and visualization]
** [http://slate.ices.utexas.edu/coursewiki/index.php/Main_Page Statistical and Discrete Methods for Scientific Computing]
** [http://slate.ices.utexas.edu/coursewiki/index.php/Main_Page Statistical and Discrete Methods for Scientific Computing]
** [http://www.bio.utexas.edu/faculty/wallingford/class2013/ BIO 337: Developmental Biology], emphasizing how to how to critically read and dissect papers.
** [http://www.bio.utexas.edu/faculty/wallingford/class2013/ BIO 337: Developmental Biology], emphasizing how to critically read and dissect papers.

Latest revision as of 12:20, 19 December 2013


CH364C/CH391L Bioinformatics

Course unique #: 52716/52990
Lectures: Tuesday/Thursday 2:00 – 3:30 PM in WEL 3.260
Instructor: Edward Marcotte, marcotte@icmb.utexas.edu

  • Office hours: Wednesdays 2:00 – 3:00 PM in MBB 3.148BA Phone: 471-5435

TA: John Woods, john.woods at marcottelab dot org

  • TA Office hours: Tuesday/Friday 10:00 – 11:00 AM in MBB 3.128 Phone: 232-3919
    • NOTE #1: John will be out of town 4/18 through 4/22. If you need to meet with him, email him and schedule a gchat meeting for Friday sometime.
    • NOTE #2: TA office hours will start at 10:30am the week of 4/22 instead of 10am (and will end at 11:30am). If you can't make it during those hours, as always, email John to set up an appointment.

Lectures & Handouts

May 2, 2013 - Some of the (few!) publicly-viewable final projects. Many private ones this time!

April 30, 2013 - Synthetic biology

April 25, 2013 - Synthetic biology

April 18-23, 2013 - Networks

April 16, 2013 - Networks

April 11, 2013 - Motifs

April 9, 2013

  • IMPORTANT REMINDER! Today is one of our scheduled NO CLASS DAYS! Given that, and the trouble that several students have had getting clustering software working on Macs, I'm

extending the homework due date to April 11.

April 2-4, 2013 - Classification & PCA/SVD

Mar 28, 2013 - Clustering & Classification

Mar 27, 2013

  • The seminar by Bob Sauer (2:30-3:30, Welch convocation center) conflicts with office hours today, so office hours will be held after seminar instead (3:30-4:30).

Mar 26, 2013 - Clustering 2

An assortment of datasets for Problem Set 4, due Apr. 9, 2013. Someone in the class noted that the phylogenetic profile file was space, not tab, delimited, and that there were a few lines with errors in the mRNA expression data. I've updated the links to be the correct versions. Also, several people have noted that the Mac/Linux versions of the tree viewing program seem to be a bit buggy; however, the Windows version (TreeView) seems to be fine.

Mar 21, 2013 - Clustering

Mar 19, 2013 - Gene expression Wrapping up sequencing:

& on to RNA expression!

Mar 7, 2013 - Assembly and mapping

Mar 5, 2013 - Assembling genomes + next-gen sequencing

Feb 28, 2013 - Assembling genomes

Feb 26, 2013 - Assembling genomes

Feb 21, 2013 - Gene finding

Feb 19, 2013 - HMMs and gene finding

  • Given that we're running a lecture behind, HW#2 will be due on Feb. 26, rather than the 21st.
  • Fly cell Markov chains

Feb 14, 2013 - HMMs

Feb 12, 2013 - Profiles

Feb 7, 2013 - BLAST

Feb 5, 2013 - Sequence Alignment III

  • A few examples of proteins with internally repetitive sequences: 1, 2, 3
  • Repeats in the human genome, tallied here
  • In the news: The pigeon genome

Jan 31, 2013 - Sequence Alignment II

Jan 24, 2013 - Sequence Alignment I

Jan 22, 2013 - Intro to Python

Jan 17, 2013 - Newsworthy computational biology story of the week!

  • Gymrek et al. (Supplement) show that genomic datasets are not as anonymous as we thought!]
  • There are some associated commentaries, if you're curious: #1 2 #3

Syllabus & course outline

Course syllabus

An introduction to computational biology and bioinformatics. The course covers typical data, data analysis, and algorithms encountered in computational biology. Topics will include introductory probability and statistics, basics of programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, synthetic biology, analysis of gene expression data, data clustering, biological pattern recognition, and biological networks.

Open to graduate students and upper division undergraduates in natural sciences and engineering.
Prerequisites: Basic familiarity with molecular biology, statistics & computing, but realistically, it is expected that students will have extremely varied backgrounds.

Note that this is not a course on practical sequence analysis or using web-based tools. Although we will use a number of these to help illustrate points, the focus of the course will be on learning the underlying algorithms and exploratory data analyses and their applications.

Most of the lectures will be from research articles and handouts, with some material from the...
Recommended text (for sequence analysis): Biological sequence analysis, by R. Durbin, S. Eddy, A. Krogh, G. Mitchison (Cambridge University Press),

For non-molecular biologists, I highly recommend (really!) The Cartoon Guide to Genetics (Gonick/Wheelis)
For biologists rusty on their stats, The Cartoon Guide to Statistics (Gonick/Smith) is also very good.

Some online references:
An online bioinformatics course
Assorted bioinformatics resources on the web: #1, #2
Python coding for beginners
Beginning Python for Bioinformatics
Online probability texts: #1, #2, #3

No exams will be given. Grades will be based on 4 problem sets (given every 2 weeks and counting 15% each towards the final grade) and a course project (40% of final grade), which can be individual or collaborative. If collaborative, cross-discipline collaborations are encouraged. The course project will consist of a research paper or project on a bioinformatics topic chosen by the student (with approval by the instructor) containing an element of independent computational biology research (e.g. calculation, programming, database analysis, etc.). This will be turned in as a link to a web page.

The final project is due on April 30, 2013.

Helpful links