BCH339N 2016

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BCH339N Systems Biology & Bioinformatics

Course unique #: 54040
Lectures: Tues/Thurs 11 – 12:30 PM in BUR 212
Instructor: Edward Marcotte, marcotte@icmb.utexas.edu

  • Office hours: Mon 4 PM – 5 PM in MBB 3.148BA

TA: Claire McWhite, claire.mcwhite@utexas.edu

  • TA Office hours: Wed/Thurs 4 PM - 5 PM in MBB 3.128A Phone: listed on the syllabus

Lectures & Handouts

Apr 28 - May 5, 2016 - Gene Presentations


April 26, 2016 - Synthetic Biology

A collection of further reading, if you're so inclined:

Food for thought:
De-extinction I, II, and III

April 21, 2016 - Phenologs

  • Today's slides
  • Phenologs and the drug discovery story we'll discuss 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.

Tools for finding orthologs:

April 19, 2016 - Networks II

Apr 14, 2016 - Genome Engineering

Apr 12, 2016 - Mass spectrometry proteomics

Apr 7, 2016 - 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).

Reading:

Apr 5, 2016 - Principal Component Analysis (& the curious case of European genotypes)

A smattering of links on PCA:

Mar 31, 2016 - Classifiers I

Mar 29, 2016 - Clustering II

Mar 24, 2016 - Functional Genomics & Data Mining - Clustering I

Problem Set 3, due before midnight Apr. 7, 2016. You will need the following software and datasets:

Mar 22, 2016 - Motifs

Mar 15-17, 2016 - SPRING BREAK

Mar 10, 2016 - Genomes II, Gene Expression

Mar 8, 2016 - Genome Assembly

Mar 3, 2016 - ???????


Mar 1, 2016 - Gene finding II

  • We're finishing up the slides from Feb. 25, then moving on into Genome Assembly

Feb 25, 2015 - Gene finding

Reading:

Feb 23, 2016 - HMMs II

  • We're finishing up the slides from Feb. 18.

Problem Set 2, due before midnight Mar. 8, 2016:

Feb 18, 2015 - Hidden Markov Models

Reading:

Feb 16, 2016 - Next-generation Sequencing (NGS)

Feb 11, 2016 - 3D Protein Structure Modeling

Feb 9, 2016 - Biological databases

  • Homework #2 (worth 10% of your final course grade) has been assigned on Rosalind and is due by 11:59PM February 18.
  • Just a note that we'll be seeing ever more statistics as go on. Here's a good primer from Prof. Lauren Myers to refresh/explain basic concepts.
  • Today's slides

Feb 4, 2016 - Guest lecture: Homologs, orthologs, and evolutionary trees

  • We'll have a guest lecture on evolutionary relationships among genes.

Feb 2, 2016 - BLAST

Jan 28, 2016 - Sequence Alignment II

Jan 26, 2016 - Sequence Alignment I

Problem Set I, due before midnight Feb. 4, 2016:

Reading:

Jan 21, 2016 - Intro to Python

Jan 19, 2016 - 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 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 26.
  • 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 biochemistry majors. Prerequisites: Biochemistry 339F or Chemistry 339K with a grade of at least C-.
Requires basic familiarity with molecular biology & basic statistics, although varied backgrounds are expected.


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

The final project is due by midnight April 27, 2016.