Difference between revisions of "BCH339N 2018"

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(Lectures & Handouts)
(Lectures & Handouts)
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* [http://www.marcottelab.org/users/BCH339N_2018/AMLALLclassification.pdf Classifying leukemias]
 
* [http://www.marcottelab.org/users/BCH339N_2018/AMLALLclassification.pdf Classifying leukemias]
 
* For those of you interesting in trying out classifiers on your own, here's the best open software for do-it-yourself classifiers and data mining: [http://www.cs.waikato.ac.nz/ml/weka/ Weka].  There is a great introduction to using Weka in this book chapter [http://link.springer.com/protocol/10.1007/978-1-4939-3578-9_17 Introducing Machine Learning Concepts with WEKA], as well as the very accessible Weka-produced book [http://www.cs.waikato.ac.nz/ml/weka/book.html Data Mining: Practical Machine Learning Tools and Techniques].
 
* For those of you interesting in trying out classifiers on your own, here's the best open software for do-it-yourself classifiers and data mining: [http://www.cs.waikato.ac.nz/ml/weka/ Weka].  There is a great introduction to using Weka in this book chapter [http://link.springer.com/protocol/10.1007/978-1-4939-3578-9_17 Introducing Machine Learning Concepts with WEKA], as well as the very accessible Weka-produced book [http://www.cs.waikato.ac.nz/ml/weka/book.html Data Mining: Practical Machine Learning Tools and Techniques].
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'''Mar 27, 2018 - 3D Protein Structure Modeling'''
 
'''Mar 27, 2018 - 3D Protein Structure Modeling'''
 
* Guest speaker: [https://scholar.google.com/citations?hl=en&user=zJ8L0GcAAAAJ&view_op=list_works Dr. Kevin Drew], formerly of New York University and now at the UT Center for Systems and Synthetic Biology
 
* Guest speaker: [https://scholar.google.com/citations?hl=en&user=zJ8L0GcAAAAJ&view_op=list_works Dr. Kevin Drew], formerly of New York University and now at the UT Center for Systems and Synthetic Biology
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* [http://www.marcottelab.org/users/BCH339N_2018/structbio_lecture_BCH339N_2016.pptx Today's slides]<br>
 
* [http://www.marcottelab.org/users/BCH339N_2018/structbio_lecture_BCH339N_2016.pptx Today's slides]<br>
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* The [https://www.rosettacommons.org/software Rosetta] software suite for 3D protein modeling, and [http://www.marcottelab.org/users/BCH339N_2018/RosettaOverview.pdf what it can do for you]
 
* The [https://www.rosettacommons.org/software Rosetta] software suite for 3D protein modeling, and [http://www.marcottelab.org/users/BCH339N_2018/RosettaOverview.pdf what it can do for you]
 
* The [http://www.rcsb.org/pdb/ Protein Data Bank], [http://toolkit.tuebingen.mpg.de/hhpred HHPRED], [https://salilab.org/modeller/ MODELLER], and [http://www.pymol.org/ Pymol]
 
* The [http://www.rcsb.org/pdb/ Protein Data Bank], [http://toolkit.tuebingen.mpg.de/hhpred HHPRED], [https://salilab.org/modeller/ MODELLER], and [http://www.pymol.org/ Pymol]
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* [http://www.rcsb.org/pdb/explore/explore.do?structureId=1L1M The biochemical basis of a particular motif]
 
* [http://www.rcsb.org/pdb/explore/explore.do?structureId=1L1M The biochemical basis of a particular motif]
 
* [http://www.marcottelab.org/users/BCH339N_2018/GibbsSampling.pdf Gibbs Sampling]
 
* [http://www.marcottelab.org/users/BCH339N_2018/GibbsSampling.pdf Gibbs Sampling]
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* [http://www.marcottelab.org/users/BCH339N_2018/AlignAce.pdf AlignAce]  -->
 
  
 
'''Mar 6, 2018 - Genomes II'''<br>
 
'''Mar 6, 2018 - Genomes II'''<br>
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* [http://www.marcottelab.org/users/BCH339N_2018/Ecoli_genome.txt E. coli genome]
 
* [http://www.marcottelab.org/users/BCH339N_2018/Ecoli_genome.txt E. coli genome]
 
* [http://astrofrog.github.io/blog/2015/05/09/2015-survey-results/ Python 2 vs 3?]. For compatibility with Rosalind and other materials, we'll use version 2.7. The current plan is for Python 2.7 support to be halted in 2020, but there is some hope (wishful thinking?) that Python 4 will be backwards compatible, [http://astrofrog.github.io/blog/2016/01/12/stop-writing-python-4-incompatible-code/ unlike Python 3]. Regardless, you're welcome to use whichever version you prefer, but we'll use 2.7 for all class explanations in the interests of simplicity and consistency. For beginners, the [http://www.practicepython.org/blog/2017/02/09/python2-and-3.html differences are quite minimal].
 
* [http://astrofrog.github.io/blog/2015/05/09/2015-survey-results/ Python 2 vs 3?]. For compatibility with Rosalind and other materials, we'll use version 2.7. The current plan is for Python 2.7 support to be halted in 2020, but there is some hope (wishful thinking?) that Python 4 will be backwards compatible, [http://astrofrog.github.io/blog/2016/01/12/stop-writing-python-4-incompatible-code/ unlike Python 3]. Regardless, you're welcome to use whichever version you prefer, but we'll use 2.7 for all class explanations in the interests of simplicity and consistency. For beginners, the [http://www.practicepython.org/blog/2017/02/09/python2-and-3.html differences are quite minimal].
 
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'''Jan 16, 2018 - Introduction'''
 
'''Jan 16, 2018 - Introduction'''
 
* [http://www.marcottelab.org/users/BCH339N_2018/BCH339N-IntroAndRosalind-Spring2018.pdf Today's slides]<br>
 
* [http://www.marcottelab.org/users/BCH339N_2018/BCH339N-IntroAndRosalind-Spring2018.pdf Today's slides]<br>

Revision as of 15:39, 15 January 2018

BCH339N Systems Biology & Bioinformatics

Course unique #: 54510
Lectures: Tues/Thurs 11 – 12:30 PM in BUR 212
Instructor: Edward Marcotte, marcotte @ icmb.utexas.edu

  • Office hours: Wed 9 AM – 10 AM in MBB 3.148BA

TA: Riddhiman Garge, riddhimankg @ utexas.edu

  • TA Office hours: Mon/Tue 3 PM - 4 PM in MBB 2.204 (or 3.128AA) Phone: 512-232-3919

Lectures & Handouts

Jan 16, 2018 - Introduction

  • Today's slides
  • Some warm-up videos to get you started on Python: Code Academy's Python coding for beginners
  • We'll be conducting homework using the online environment Rosalind. Go ahead and register on the site, and enroll specifically for BCH339N-Spring2018 using this link. Homework #1 (worth 10% of your final course grade) has already been assigned on Rosalind and is due by 11:59PM January 25.
  • A useful online resource if you get bogged down: Python for Biologists. (& just a heads-up that some of their instructions for running code relate to a command line environment that's a bit different from the default one you install following the Rosalind instructions. It won't affect the programs, just the way they are run or how you specific where files are located.) However, if you've never programmed Python before, definitely check this out!!!
  • An oldie (by recent bioinformatics standards) but goodie: Computers are from Mars, Organisms are from Venus

Syllabus & course outline

Course syllabus

An introduction to systems biology and bioinformatics, emphasizing quantitative analysis of high-throughput biological data, and covering typical data, data analysis, and computer algorithms. Topics will include introductory probability and statistics, basics of Python programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, proteomics, synthetic biology, analysis of large-scale gene expression data, data clustering, biological pattern recognition, and gene and protein networks.

Open to upper division undergrads in natural sciences and engineering. Prerequisites: Biochemistry 339F, Computer Science 303E, and Statistics and Data Sciences 328M (or Statistics and Scientific Computation 318M, 328M) with a grade of at least C-

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, esp. in high-throughput biology.

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

For biologists rusty on their stats, The Cartoon Guide to Statistics (Gonick/Smith) is very good. A reasonable online resource for beginners is Statistics Done Wrong.

Some online references:
An online bioinformatics course
Assorted bioinformatics resources on the web: Assorted links
Online probability texts: #1, #2, #3

No exams will be given. Grades will be based on online homework (counting 30% of the grade), 3 problem sets (given every 2-3 weeks and counting 15% each towards the final grade) and an independent course project (25% of final grade). The course project will consist of a research 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 by midnight, April 25, 2018. The last three classes will be spent presenting your projects to each other. (The presentation will account for 5% of the project.)

Online homework will be assigned and evaluated using the free bioinformatics web resource Rosalind.

All projects and homework will be turned in electronically and time-stamped. No makeup work will be given. Instead, all students have 5 days of free “late time” (for the entire semester, NOT per project, and counting weekends/holidays). For projects turned in late, days will be deducted from the 5 day total (or what remains of it) by the number of days late (in 1 day increments, rounding up, i.e. 10 minutes late = 1 day deducted). Once the full 5 days have been used up, assignments will be penalized 10 percent per day late (rounding up), i.e., a 50 point assignment turned in 1.5 days late would be penalized 20%, or 10 points.

Homework, problem sets, and the project total to a possible 100 points. There will be no curving of grades, nor will grades be rounded up. We’ll use the plus/minus grading system, so: A= 92 and above, A-=90 to 91.99, etc. Just for clarity's sake, here are the cutoffs for the grades: 92% = A, 90% = A- < 92%, 88% = B+ < 90%, 82% = B < 88%, 80% = B- < 82%, 78% = C+ < 80%, 72% = C < 78%, 70% = C- < 72%, 68% = D+ < 70%, 62% = D < 68%, 60% = D- < 62%, F < 60%.

Students are welcome to discuss ideas and problems with each other, but all programs, Rosalind homework, problem sets, and written solutions should be performed independently . Students are expected to follow the UT honor code. Cheating, plagiarism, copying, & reuse of prior homework, projects, or programs from CourseHero, Github, or any other sources are all strictly forbidden and constitute breaches of academic integrity (UT academic integrity policy) and cause for dismissal with a failing grade.

The final project web site is due by midnight April 25, 2018.