Difference between revisions of "CH391L 2013"

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(Lectures & Handouts)
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* [http://www.marcottelab.org/users/CH391L/Handouts/DLBCL-redux.pdf Diffuse large B cell lymphoma redux]
 
* [http://www.marcottelab.org/users/CH391L/Handouts/DLBCL-redux.pdf Diffuse large B cell lymphoma redux]
  
Mar 29, 2011 - Clustering 2
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* [http://www.marcottelab.org/users/CH391L/Handouts/FuzzyK-Means.pdf Fuzzy k-means]
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Mar 26, 2013 - Clustering 2
* [http://www.marcottelab.org/users/CH391L/Handouts/SOM-geneexpression.pdf SOM gene expression]
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* [http://www.marcottelab.org/users/CH391L_2013/Files/FuzzyK-Means.pdf Fuzzy k-means]
** & links to various applications of SOMs: [http://wn.com/Self_Organizing_Maps_Application 1], [http://www.bentley.edu/csbigs/documents/hua.pdf 2], [http://vizier.u-strasbg.fr/kohonen.htx 3], [http://en.wikipedia.org/wiki/Self-organizing_map 4], and a picture of [http://www.cis.hut.fi/research/som-research/teuvo.html Teuvo] himself
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* [http://www.marcottelab.org/users/CH391L_2013/Files/SOM-geneexpression.pdf SOM gene expression]
** (From TA) You can run SOM clustering with Open Source Clustering package (alternative to Eisen's Cluster) with '-s' option, or GUI option. See http://bonsai.hgc.jp/~mdehoon/software/cluster/manual/SOM.html#SOM for detail. (FYI, it also supports PCA). If you are not happy with Cluster SOM function, statistical package R also provides a package for SOM (http://cran.r-project.org/web/packages/som/index.html).  
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** & links to various applications of SOMs: [http://wn.com/Self_Organizing_Maps_Application 1], [http://www.bentley.edu/centers/sites/www.bentley.edu.centers/files/csbigs/hua.pdf 2], [http://vizier.u-strasbg.fr/kohonen.htx 3], [http://en.wikipedia.org/wiki/Self-organizing_map 4], and a picture of [http://www.cis.hut.fi/research/som-research/teuvo.html Teuvo] himself (and his [http://websom.hut.fi/websom/stt/doc/fin/ analyses of Finnish news feeds and USENET articles])
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** You can also run SOM clustering with the Open Source Clustering package (an alternative to Eisen's Cluster) with '-s' option, or GUI option. See http://bonsai.hgc.jp/~mdehoon/software/cluster/manual/SOM.html#SOM for detail. (FYI, it also supports PCA). If you are not happy with Cluster's SOM function, the statistical package R also provides a package for calculating SOMs (http://cran.r-project.org/web/packages/som/index.html).  
  
An assortment of datasets for [http://www.marcottelab.org/users/CH391L/ProblemSets/ProblemSet4_2011.pdf '''Problem Set 4], due Apr. 12, 2011'''
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An assortment of datasets for [http://www.marcottelab.org/users/CH391L_2013/Files/ProblemSet4_2013.pdf '''Problem Set 4], due Apr. 9, 2013'''
* [http://www.marcottelab.org/users/CH391L/ProblemSets/yeast_aaseqs Yeast protein sequences]
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* [http://www.marcottelab.org/users/CH391L_2013/Files/yeast_aaseqs Yeast protein sequences]
* [http://www.marcottelab.org/users/CH391L/ProblemSets/yeast_phyloprofiles Yeast protein phylogenetic profiles]
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* [http://www.marcottelab.org/users/CH391L_2013/Files/yeast_phyloprofiles Yeast protein phylogenetic profiles]
* [http://www.marcottelab.org/users/CH391L/ProblemSets/yeast_microarraydata Yeast mRNA expression profiles]
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* [http://www.marcottelab.org/users/CH391L_2013/Files/yeast_microarraydata Yeast mRNA expression profiles]
* For simplicity, here are the summary files (edited slightly, showing total read counts, counts of reads where F3 and F5 map to the same organism, counts of F3 and counts of F5 reads) of all of the searches on the 5 environmental samples:
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** [http://www.marcottelab.org/users/CH391L/ProblemSets/V3BC21_read_freq V3BC21]
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** [http://www.marcottelab.org/users/CH391L/ProblemSets/V3BC22_read_freq V3BC22]
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** [http://www.marcottelab.org/users/CH391L/ProblemSets/V3BC23_read_freq V3BC23]
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** [http://www.marcottelab.org/users/CH391L/ProblemSets/V3BC24_read_freq V3BC24]
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** [http://www.marcottelab.org/users/CH391L/ProblemSets/V3BC25_read_freq V3BC25]
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** & A link to the [http://www.marcottelab.org/index.php/CH391L/UTpond raw data]
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** <b>(From TA) I found a bug in my script to generate read_freq files, so if you downloaded these files before Apr. 5th (Tues), discard previous files and use these new ones.</b>
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Mar 21, 2013 - Clustering
 
Mar 21, 2013 - Clustering
 
* [http://www.marcottelab.org/users/CH391L_2013/Files/PNAS_phylogenetic_profiles.pdf Phylogenetic profiles 1] and [http://www.marcottelab.org/paper-pdfs/shailesh-natbt.pdf 2]
 
* [http://www.marcottelab.org/users/CH391L_2013/Files/PNAS_phylogenetic_profiles.pdf Phylogenetic profiles 1] and [http://www.marcottelab.org/paper-pdfs/shailesh-natbt.pdf 2]

Revision as of 10:44, 26 March 2013

Contents

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

Lectures & Handouts

Mar 26, 2013 - Clustering 2

An assortment of datasets for Problem Set 4, due Apr. 9, 2013

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