BIO337 2014

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BIO337 Systems Biology/Bioinformatics

Course unique #: 50524
Lectures: Tues/Thurs 11 – 12:30 PM in BUR 212
Instructor: Edward Marcotte,

  • Office hours: Wed 11 AM – 12 noon in MBB 3.148BA

TA: Dakota Derryberry,

  • TA Office hours: Mon 4 - 5 PM/Fri 9 – 10 AM in MBB 3.304 Phone: 512-232-2459

Lectures & Handouts

April 29 - May 1, 2014 - Final Projects

April 24, 2014 - Synthetic Biology II

Food for thought:
De-extinction I and II

April 22, 2014 - Synthetic Biology I


April 17, 2014 - Networks II & Phenologs

  • Today's slides
  • Phenologs and the drug discovery story we discussed in class
  • Search for phenologs here. You can get started by rediscovering the plant model of Waardenburg syndrome. Search among the known diseases for "Waardenburg", or enter the human genes linked to Waardenburg (Entrez gene IDs 4286, 5077, 6591, 7299) to get a feel for how this works. Also, here's Carl Zimmer's NYT article about phenologs and the scientific process.
  • One good tool for discovering orthologs is InParanoid. Note: InParanoid annotation lags a bit, so you'll need to find the Ensembl protein id, or try a text search for the common name.

April 10-15, 2014 - Networks

  • Today's slides
  • Metabolic networks: The wall chart (it's interactive, e.g. here's enolase), the current state of the human metabolic reaction network, and older but still relevant review of transcriptional networks (with the current record holder in this regard held by ENCODE), and an early review of protein interaction extent and quality whose lessons still hold.
  • Useful gene network resources include:
    • FunctionalNet, which links to human, worm, Arabidopsis, mouse and yeast gene networks. Not the prettiest web site, but useful, and helped my own group find genes for a wide variety of biological processes. Try searching HumanNet for the myelin regulatory factor MYRF (Entrez gene ID 745) and predicting its function, which is now known but wasn't when the network was made.
    • STRING is available for many organisms, including large numbers of prokaryotes. Try searching on the E. coli enolase (Eno) as an example.
    • GeneMania, which aggregates many individual gene networks.
    • MouseFunc, a collection of network and classifier-based predictions of gene function from an open contest to predict gene function in the mouse.
    • The best interactive tool for network visualization is Cytoscape. You can download and install it locally on your computer, then visualize and annotated any gene network, such as are output by the network tools linked above. There is also a web-based network viewer that can be incorporated into your own pages (e.g., as used in YeastNet).


Apr 8, 2014 - Motifs

Apr 3, 2014 - Mapping protein complexes

Apr 1, 2014 - Mass spectrometry proteomics

Mar 27, 2014 - Principal Component Analysis (& the curious case of European genotypes)

A smattering of links on PCA:

Mar 25, 2014 - Classifiers I

Mar 20, 2014 - Clustering II

Problem Set 3, due before midnight Apr. 3, 2014. You will need the following software and datasets:

Mar 18, 2014 - Functional Genomics & Data Mining - Clustering I

Mar 6, 2014 - Genomes II, Gene Expression

  • Science news of the day: Genome engineering vs. HIV
  • We're finishing up the slides from Mar. 4. I added new slides to the end of the deck for today's lecture.

Note: we'll increasingly be discussing primary papers in the lectures

& on to RNA expression!

Mar 4, 2014 - Genome Assembly

Feb 27, 2014 - Gene finding II

  • We're finishing up the slides from Feb. 25. I added new slides to the end of the deck for today's lecture.

News of the day, regarding the application of next gen sequencing to fetal diagnostics:

Feb 25, 2014 - Gene finding

  • Due March 6 by email - One to two (full) paragraphs describing your plans for a final project, along with the names of your collaborators. This assignment will account for 5 points out of your 25 total points for your course project.
  • Here are a few examples of final projects from previous years: 1, 2, 3, 4, 5 6 7 8 9 10
  • Today's slides
  • The UCSC genome browser


Feb 20, 2014 - Genome Engineering

Feb 18, 2014 - Next-generation Sequencing (NGS)

Feb 13, 2014 - HMMs II

  • We're finishing up the slides from Feb. 11. Note that I added a few new slides to the end of the deck for today's lecture.
  • There's a nice systems biology/bioinformatics research talk today by Steve Horvath (UCLA), Epigenetic clock, genomic biomarkers, and systems biology (2-3PM, NHB 1.720). Read more about the clock.

Feb 11, 2014 - Hidden Markov Models

Problem Set 2, due before midnight Feb. 23, 2014:


Feb 6, 2014 - Biological databases

Feb 4, 2014 - Guest lecture: Intro to Appsoma

  • We'll have a guest lecture by Zack Simpson, a Fellow of the UT Center for Systems and Synthetic Biology. For the curious, Science magazine wrote a nice feature on Zack several years ago (posted here). Zack co-founded the bioinformatics startup company Traitwise (full disclosure: I'm on their scientific advisory board) and is the lead developer of Appsoma, a web-based scientific cloud computing platform that has a number of enhancements (and dedicated computer clusters) specifically for UT students.

Jan 30, 2014 - BLAST

Jan 28, 2014 - ICEPOCALYPSE 2014!!!

Jan 23, 2014 - Sequence Alignment II

Jan 21, 2014 - Sequence Alignment I

Problem Set I, due before midnight Jan. 28, 2014:


Jan 16, 2014 - Intro to Python

Jan 14, 2014 - 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 BIO337 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 21.
  • 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 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 undergraduates in natural sciences and engineering.
Prerequisites: Biochem I or equivalent (e.g. CH339J or CH339K/L), basic familiarity with molecular biology.

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
Beginning Python for Bioinformatics
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 a course project (25% of final grade), which will be collaborative. Cross-discipline collaborations will be encouraged. The course project will consist of a research project on a bioinformatics topic chosen by the students (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.

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 percentage points 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, and written solutions should be performed independently (except the final collaborative project).

The final project is due by midnight April 28, 2014.