Difference between revisions of "BCH339N 2016"
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'''Mar 8, 2016 - Genome Assembly''' | '''Mar 8, 2016 - Genome Assembly''' | ||
* [http://www.marcottelab.org/users/BCH339N_2016/BCH339N_GenomeAssembly_Spring2016.pdf Today's slides] | * [http://www.marcottelab.org/users/BCH339N_2016/BCH339N_GenomeAssembly_Spring2016.pdf Today's slides] | ||
− | * A gentle reminder that Problem Set 2 is '''due by 11:59PM tonight''' | + | * A gentle reminder that Problem Set 2 is '''due by 11:59PM tonight'''<br> |
* [http://www.marcottelab.org/users/BCH339N_2016/DeBruijnPrimer.pdf DeBruijn Primer] and [http://www.marcottelab.org/users/BCH339N_2016/DeBruijnSupplement.pdf Supplement] | * [http://www.marcottelab.org/users/BCH339N_2016/DeBruijnPrimer.pdf DeBruijn Primer] and [http://www.marcottelab.org/users/BCH339N_2016/DeBruijnSupplement.pdf Supplement] | ||
* Here are a few explanations of using the BWT for indexing: [http://blog.avadis-ngs.com/2012/04/elegant-exact-string-match-using-bwt-2/ 1] [http://www.di.unipi.it/~ferragin/Libraries/fmindexV2/index.html 2] | * Here are a few explanations of using the BWT for indexing: [http://blog.avadis-ngs.com/2012/04/elegant-exact-string-match-using-bwt-2/ 1] [http://www.di.unipi.it/~ferragin/Libraries/fmindexV2/index.html 2] |
Revision as of 15:24, 18 January 2016
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: on syllabus
Lectures & Handouts
Apr 28 - May 5, 2016 - Gene Presentations
- Note: There are some great short summer courses in computational biology being offered at UT. Of particular note, introductions to core NextGen sequencing tools and protein modeling using Rosetta
April 26, 2016 - Synthetic Biology
A collection of further reading, if you're so inclined:
- Genome Transplantation
- JCVI-1.0
- One step genome assembly in yeast
- New cells from yeast genomic clones
- A new cell from a chemically synthesized genome, SOM
- 1/2 a synthetic yeast chromosome and Build-A-Genome
- & the latest: Entire synthetic yeast chromosome
- The Gillespie algorithm
- iGEM, and an example part (the light sensor)
- Take your own coliroids
- The infamous repressilator
- Bacterial photography, and UT's 2012 iGEM entry
- Edge detector
- A more recent example of digital logic
- An example of metabolic engineering: yeast making anti-malarial drugs
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:
- 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. InParanoid tends towards higher recall, lower precision for finding orthologs. Approaches with higher precision include OMA (introduced in this paper), PhylomeDB, and just released, MOSAIC
- All your ortholog definition questions answered!
April 19, 2016 - Networks II
- We're finishing up the slides from Apr. 7.
- Worth noting: UT Summer school for big data in biology
Apr 14, 2016 - Genome Engineering
- Guest speaker: Dr. Chris Yellman
Apr 12, 2016 - Mass spectrometry proteomics
- Guest speaker: Dr. Daniel Boutz
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:
- Functional networks
- Review of predicting gene function and phenotype from protein networks
- Primer on visualizing networks
Apr 5, 2016 - Principal Component Analysis (& the curious case of European genotypes)
- Today's slides
- European men, their genomes, and their geography
- Relevant to today's discussion for his eponymous distance measure: Mahalanobis
A smattering of links on PCA:
- NBT Primer on PCA
- A PCA overview (.docx format) & the original post
- Science Signaling (more specifically, Neil R. Clark and Avi Ma’ayan!) had a nice introduction to PCA that I've reposted here (with slides)
- Python code for performing PCA yourself
Mar 31, 2016 - Classifiers I
- Today's slides
- 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: Weka
Mar 29, 2016 - Clustering II
- We're finishing up the slides from Mar. 24.
- Fuzzy k-means
- SOM gene expression
- Links to various applications of SOMs: 1, 2, 3, 4. You can run SOMs on the following web site. 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).
Mar 24, 2016 - Functional Genomics & Data Mining - Clustering I
- Today's slides
- Clustering
- Review of phylogenetic profiles
- B cell lymphomas
- Primer on clustering
- K-means example (.ppt)
Problem Set 3, due before midnight Apr. 7, 2016. You will need the following software and datasets:
- The clustering and treeview software is available here. Previous students have said the Mac/Linux versions of the tree viewing program can be a bit buggy; however, the Windows version (TreeView) seems to be fine.
- Yeast protein sequences
- Yeast protein phylogenetic profiles
- Yeast mRNA expression profiles
Mar 22, 2016 - Motifs
- Today's slides
- NBT Primer - What are motifs?
- NBT Primer - How does motif discovery work?
- The biochemical basis of a particular motif
- Gibbs Sampling
- AlignAce
Mar 15-17, 2016 - SPRING BREAK
Mar 10, 2016 - Genomes II, Gene Expression
- For those of you interested in doing your homework/research with more experienced coders in the room, there will be a weekly Open Coding Hour in the CCBB conference room / collaboratorium (GDC 7.514) each Tuesday ?from 5-7 PM. Scott Hunicke-Smith will begin each Open Coding Hour with 10 minutes of computing tricks. Accompanying google group in-house programming question and answer forum.
- Homework #3 (worth 10% of your final course grade) has been assigned on Rosalind and is due by 11:59PM March 24.
- We're finishing up the slides from Mar. 8, then on to RNA expression. Note: we'll increasingly be discussing primary papers in the lectures. Here are a few classics and reviews that will come up.
- Gene expression by ESTs
- Gene expression by SAGE
- Affy microarrays 1 & Affy microarrays 2
- cDNA microarrays
- RNA-Seq
- Clustering by gene expression
- Cell cycle data
Mar 8, 2016 - Genome Assembly
- Today's slides
- A gentle reminder that Problem Set 2 is due by 11:59PM tonight
- DeBruijn Primer and Supplement
- Here are a few explanations of using the BWT for indexing: 1 2
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:
- Problem Set 2.
- You'll need these 3 files: State sequences, Soluble sequences, Transmembrane sequences
Feb 18, 2015 - Hidden Markov Models
- Another view of the remarkable growth of data, e.g. UniProt
- Today's slides
Reading:
- HMM primer and Bayesian statistics primer, Wiki Bayes
- Care to practice your regular expressions? (In python?)
Feb 16, 2016 - Next-generation Sequencing (NGS)
- Guest speaker: Dr. Scott Hunicke-Smith, former director of the Genome Sequencing and Analysis Facility, and current director of NextGen sequencing diagnostics at [].
- Illumina/Solexa Sequencing (Youtube Video)
- Genome Analyzer (Youtube Video)
Feb 11, 2016 - 3D Protein Structure Modeling
- Guest speaker: Dr. Kevin Drew, formerly of New York University and now at the UT Center for Systems and Synthetic Biology
- Today's slides
- The Rosetta software suite for 3D protein modeling, and what it can do for you
- The Protein Data Bank, HHPRED, MODELLER, and Pymol
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
- Our slides today are modified from a paper on Teaching BLAST by Cheryl Kerfeld & Kathleen Scott.
- The original BLAST paper
- The protein homology graph paper. Just for fun, here's a link to a stylized version we exhibited in the engaging Design and the Elastic Mind show at New York's Museum of Modern Art.
Jan 28, 2016 - Sequence Alignment II
- We're finishing up the slides from Jan. 26.
- Dynamic programming primer
- An example of dynamic programming using Excel, created by Michael Hoffman (a former UT undergraduate who took the prior incarnation of this class)
- A few examples of proteins with internally repetitive sequences: 1, 2, 3
Jan 26, 2016 - Sequence Alignment I
- One of my favorite news items of the last few last years: China cloning on an 'industrial scale'. Favorite quote: "If it tastes good you should sequence it..." BGI is one of the biggest (the biggest?) genome sequencing centers in the world and employs >2,000 bioinformatics researchers.
- Today's slides
Problem Set I, due before midnight Feb. 4, 2016:
- Problem Set 1
- T. volcanium genome
- 3 mystery genes (for Problem 5): Mgene1, Mgene2, Mgene3
Reading:
- BLOSUM primer
- The original BLOSUM paper (hot off the presses from 1992!)
- BLOSUM miscalculations improve performance
- There is a good discussion of the alignment algorithms and different scoring schemes here
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
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 an independent course project (25% of final grade). The course project will be focused on a specific gene & will involve bioinformatics research (e.g. calculation, programming, database analysis, etc.) developed over the semester in 5 mini-assignments, which will be turned in as a link to a web page that you will continue to expand over the semester. The completed final web site is due by midnight, April 27, 2016, and will be presented to the rest of the class on the last 3 class days. Each mini-assignment is 4% of the final grade; the presentation will be worth 5%.
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.
The final project web site is due by midnight April 27, 2016.
- How to make a web site for the final project
- Google Site: https://support.google.com/sites/answer/153197?hl=en