BCH339N 2018

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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

April 19, 2018 - Synthetic Biology I

  • Reminder: All projects are due by midnight, April 25. Turn them in as a URL to the web site you created, sent by email to the TA AND PROFESSOR.
  • Today's slides

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

Food for thought

April 17, 2018 - Phenologs

  • Remember: The final project web page is due by midnight April 25, 2018, turned in as a URL emailed to the TA+Professor. Please indicate in the email if you are willing to let us post the project to the course web site.
  • Today's slides
  • Phenologs and the drug discovery story we'll discuss in class. This is a fun example of the power of opportunistic data mining aka "research parasitism" in biomedical research.
  • 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 12, 2018 - Networks II

  • We're finishing up the slides from Apr. 5.

Apr 10, 2018 - Cryo-electron microscopy

April 5, 2018 - Networks

  • Today's slides
  • Metabolic networks: The wall chart (it's interactive. For example, can you find enolase?), the current state of the human metabolic reaction network, a review of mapping transcriptional networks by Chip-SEQ (with the current record holder in this regard held by ENCODE), and a recent review of protein interaction mapping in humans and how it is informing disease genetics.
  • Useful gene network resources include:
    • Reactome), which we've seen before, links human genes according to reactions and pathways, and also calculated functional linkages from various high-throughput data.
    • 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). Here's an example file to visualize, the latest version of the human protein complex map.

Reading:

Apr 3, 2018 - Principal Component Analysis (& the curious case of European genotypes)

A smattering of links on PCA:

Mar 29, 2018 - Classifiers I

Mar 27, 2018 - 3D Protein Structure Modeling

Mar 22, 2018 - Clustering II

Problem Set 3, due before midnight Apr. 10, 2018. You will need the following software and datasets:

Mar 20, 2018 - Functional Genomics & Data Mining - Clustering I

Mar 13-15, 2018 - SPRING BREAK

  • Finish HW3 and turn in the proposal for your course project.

Mar 8, 2018 - Motifs

  • Today's slides
  • Due March 19 by email - One to two (full) paragraphs describing your plans for a final project, along with the names of your collaborators. This assignment (planning out your project) 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 11 12 13 14
  • NBT Primer - What are motifs?
  • NBT Primer - How does motif discovery work?
  • The biochemical basis of a particular motif
  • Gibbs Sampling
  • FYI, The nanopore sequencing run we started in class ran for a day and collected >9000 basecalled reads, with average lengths of several thousand nucleotides. Our longest read was almost 70 kb! We'll clean up the data and post it to the course web site so that some of you can use it for projects, if desired. Here are the nanopore reads if you'd like to play with them. There are 3 fastq files, each gzipped and ~10-30GB in size: 0, 1, 2

Mar 6, 2018 - Live Demo: Next-next-...-generation Sequencing (NGS) by nanopore

Mar 1, 2018 - Genomes II

  • We're finishing up the slides from Feb. 27. Note that we give short shrift to read mapping/alignment algorithms, of which there are now a very long list. Here's an interesting discussion by Lior Pachter of the major developments in that field.
  • The BWA paper gives a clear introduction to how the Burrows–Wheeler transform can be used to construct an index.

Feb 27, 2018 - Genome Assembly

Feb 22, 2018 - Gene finding II

  • We're finishing up the slides from Feb. 20, then moving on into Genome Assembly

Feb 20, 2018 - Gene finding

Problem Set 2, due before midnight Mar. 5, 2018:

Reading:

Feb 15, 2018 - HMMs II

Feb 13, 2018 - Hidden Markov Models

  • Don't forget: Homework #2 (worth 10% of your final course grade) is due on Rosalind by 11:59PM February 19.
  • Linking out to UniProt, discussed last time
  • Today's slides

Reading:

Feb 8, 2018 - Biological databases

Feb 6, 2018 - BLAST

Feb 1, 2018 - Guest lecture: Homologs, orthologs, and evolutionary trees

  • We'll have a guest lecture by Ben Liebeskind, a postdoctoral fellow in the Center for Systems and Synthetic Biology, on decoding the evolutionary relationships among genes.

Jan 30, 2018 - Sequence Alignment II

Jan 25, 2018 - Sequence Alignment I

  • For those of you who might be interested, Rosalind is having a Bioinformatics Contest. Sign up runs until Feb. 3, the qualification round is Feb. 3-11, and Feb. 24 is the final round, with 24 hours to solve as many problems as you can. First prize in 2017 was to get your genome (exome) sequenced!
  • Today's slides

Problem Set I, due before midnight Feb. 5, 2018:

  • Problem Set 1
  • H. influenzae genome. Haemophilus influenza was the first free living organism to have its genome sequenced. NOTE: there are some additional characters in this file from ambiguous sequence calls. For simplicity's sake, when calculating your nucleotide and dinucleotide frequencies, you can just ignore anything other than A, C, T, and G.
  • T. aquaticus genome. Thermus aquaticus helped spawn the genomic revolution as the source of heat-stable Taq polymerase for PCR.
  • 3 mystery genes (for Problem 5): MysteryGene1, MysteryGene2, MysteryGene3
  • *** HEADS UP FOR THE PROBLEM SET *** If you try to use the Python string.count function to count dinucleotides, Python counts non-overlapping instances, not overlapping instances. So, AAAA is counted as 2, not 3, dinucleotides. You want overlapping dinucleotides instead, so will have to try something else, such as the python string[counter:counter+2] command, as explained in the Rosalind homework assignment on strings.
  • For those of you who could use more tips on programming, there's a peer-led open coding hour happening on Tuesdays 3-4pm in MBB 2.232 (2nd floor lounge). It's a very informal setting where you can ask questions of more experienced programmers.

Reading:

Jan 23, 2018 - Finishing Python intro, plus Rosalind help & programming Q/A, maybe a glimpse of next lecture

Jan 18, 2018 - Intro to Python

  • Given SNOWPOCALYPSE 2018 and our lost first day of class, we'll pick up mid-stream: Today's lecture will combine the planned short intro from lecture 1 with a plunge right into Python...
  • REMINDER: My email inbox is always fairly backlogged (e.g., my median time between non-spam emails was 11 minutes when I measured last year), so please copy the TA on any emails to me to make sure they get taken care of.
  • Today's slides
  • Python primer
  • E. coli genome
  • 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, 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 differences are quite minimal.

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.