BCH394P BCH364C 2019
BCH394P/BCH364C Systems Biology & Bioinformatics
Course unique #: 54044/53945
Lectures: Tues/Thurs 11 – 12:30 PM in JGB 2.202
Instructor: Edward Marcotte, marcotte @ icmb.utexas.edu
- Office hours: Wed 11 AM – 12 noon in MBB 3.148BA
TA: Caitie McCaffery, clmccafferty @ utexas.edu
- TA Office hours: Mon 11-12/Fri 2-3 in NHB 3.400B atrium (or MBB 3.128B) Phone: 512-232-3919
Lectures & Handouts
Apr 30 - May 7, 2019 - Final Project 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 RNA-seq, also machine learning & proteomics.
- Here's an assortment of final class projects (with permission to be posted):
- Genomic and transcriptomic dynamics of outer membrane proteins in bacterial pathogens, by Nicholas Xerri, Rui Silva
- IMPACT OF CDPK PHOSPHORYLATION OF EIFISO4G1 ON PREDICTED 3D STRUCTURE, by Phillip Woolley
- Corals, Cancer, and Colonial Fusion, by Brandon Burgman
- Directed evolution of codon ramps using a stochastic model, by Alexandra Lukasiewicz
- Plasmid Annotation Tool, by Matt McGuffie
April 23-25, 2019 - Synthetic Biology I
- Reminder: All projects are due by midnight, April 29. 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:
- Minimal Mycoplasma
- Genome Transplantation
- 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
- Entire synthetic yeast chromosome
- Sc 2.0, as of 2017, with the computational genome design
- 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 nice example of digital logic
- An example of metabolic engineering: yeast making anti-malarial drugs
April 18, 2019 - Phenologs
- More on ethics, CRISPR babies & Stanford's response
- Remember: The final project web page is due by midnight April 29, 2019, 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. Also, note that late days can't be used for the final project
- 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.
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. Or, just link there from Uniprot. InParanoid tends towards higher recall, lower precision for finding orthologs. Approaches with higher precision include OMA (introduced in this paper), PhylomeDB, and EggNOG. The various algorithms basically have different trade-offs with regard to precision vs recall, and ease of use. For example, we use EggNOG in the lab for annotating genes in new genomes/transcriptomes because the EggNOG HMM ortholog models are easily downloadable/re-run on any set of genes you happen to be interested in.
- All (well, at least some) of your ortholog definition questions answered!
Apr 16, 2019 - 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.
- 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.
- The Yeast SGA map
- Functional networks
- Review of predicting gene function and phenotype from protein networks
- Primer on visualizing networks
Apr 11, 2019 - Principal Component Analysis (& the curious case of European genotypes)
- Today's slides
- European men, their genomes, and their geography
- The tSNE interactive visualization tool also performs PCA
- Relevant to today's lecture 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. This example gives a great intro to several important numerical/statistical/data mining packages in Python, including pandas and numpy.
Apr 9, 2019 - Classifiers
- Today's slides
- A nice recent review explaining Support Vector Machines and k-NN classifiers
- Classifying leukemias
- For those of you interesting in trying out classifiers on your own, here's the best stand alone open software for do-it-yourself classifiers and data mining: Weka. There is a great introduction to using Weka in this book chapter Introducing Machine Learning Concepts with WEKA, as well as the very accessible Weka-produced book Data Mining: Practical Machine Learning Tools and Techniques.
- There's a particularly nice Python library of simple, easy-to-use, classification, regression, machine learning and data mining tools called scikit-learn
Apr 4, 2019 - Clustering II
- Fun article: All biology is computational biology
- We're finishing up the slides from April 2.
- Fuzzy k-means
- SOM gene expression
- Links to various applications of SOMs: 1, 2, 3. You can run SOMs on the following web site. You can also run SOM clustering with the Open Source Clustering package with the '-s' option, or GUI option (here's the manual). (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).
Apr 2, 2019 - Functional Genomics & Data Mining - Clustering I
- Today's slides
- Primer on clustering
- K-means example (.ppt)
- B cell lymphomas
- Review of phylogenetic profiles
Problem Set 3, due before midnight Apr. 11, 2019. You will need the following software and datasets:
- The clustering software is available here. There is an alternative package here that you can download and install on your local computer if you prefer.
- Yeast protein sequences
- Yeast protein phylogenetic profiles
- Yeast protein fractionation/mass spectrometry profiles. These additionally have common gene names (LocusID_commonname_location) which may help with the interpretation. These data come from this paper.
Mar 28, 2019 - 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
Mar 26, 2019 - Proteomics
- Guest speaker: Dr. Daniel Boutz, formerly of UCLA and now at the UT Center for Systems and Synthetic Biology
- Reminder: the Center for Biomedical Research Support offers one-day courses in bioinformatics and biocomputing in April/May. Topics include Unix, bash, and single cell data analysis. There is also an Annual (4 day long!) Summer School for Big Data in Biology offered May 28-31, 2019. Topics there include genome/RNA seq, python, proteomics, machine learning, R, etc.
Mar 19-21, 2019 - SPRING BREAK
- Don't forget to finish HW3 and turn in the proposal for your course project by March 25th.
Mar 14, 2019 - Motifs
- Today's slides
- Due March 25 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, we stopped last class's nanopore sequencing run pretty soon after class ended. Even in that relatively short time (<30 min), our longest read (sequencing randomly shorter DNA fragments from a 40kb phage genome) was >20 kb.
Mar 12, 2019 - Live Demo: Next-next-...-generation Sequencing (NGS) by nanopore
- Homework #3 (worth 10% of your final course grade) has been assigned on Rosalind and is due by 11:59PM March 25.
- We're going to be live-demoing a nanopore single molecule DNA sequencer in class, assuming all goes well. Specifically, we'll be using an Oxford Nanopore MinION sequencer, which differs substantially from these major alternatives:
- Youtube video of Illumina/Solexa Sequencing by Synthesis
- Youtube video of Pacific Biosciences single molecule sequencing by synthesis in zero mode waveguides
- Here's Oxford nanopore's own videos explaining the tech. DNA sequences are collected first as electrical traces. A big breakthrough was learning to convert these traces to DNA nucleotide sequences using hidden Markov model based algorithms very similar in spirit to those we've already talked about in class (e.g., as in this open source HMM-based nanopore base-caller). The latest base-callers are moving towards neural network algorithms.
Mar 7, 2019 - Genomes II
- We're finishing up the slides from Feb. 28. 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.
- Here is an excellent explanation of how the BWT relates to a suffix tree and enables fast read mapping to a genome
- If you want a more detailed explanation, the BWA paper more formally describes how the Burrows–Wheeler transform can be used to construct an index.
- & here are two more examples of using the BWT for indexing: 1 2
- Plus: "I have some troubling news...the human reference genome is incomplete", breaking prepublication results on the telomere-to-telomere assembly of a complete human X chromosome from Karen Miga and Adam Phillippy, presented at the AGBT 2019 conference last week.
Mar 5, 2019 - Guest Lecture: Anna Battenhouse, NGS Analysis Best Practices
- Practical advice and best practices for NGS mapping and analysis
- Today's slides
Feb 28, 2019 - Genome Assembly
- Today's slides
- A gentle reminder that Problem Set 2 is due by 11:59PM March 6
- Also, next Tuesday will be the first of our guest lecturers, Anna Battenhouse, on practical aspects of genome sequencing/assembly
- DeBruijn Primer and Supplement
- If you would like a few examples of proteins annotated with their transmembrane and soluble regions (according to UniProt) to help troubleshoot your homework, here are some example yeast protein sequences.
- From last time, some definitions of sensitivity/specificity & precision/recall. Note that the gene finding community settled early on to a different definition of specificity that corresponds to the precision or PPV in other fields. Other fields define specificity as the true negative rate.
Feb 26, 2019 - Gene finding II
- We're finishing up the slides from Feb. 21
- Peer-led open coding hour has been resurrected! Wed 12:30-1:30 in the MBB 2nd floor student lounge (the newly revamped eating space)
Feb 21, 2019 - Gene finding
- We'll finish HMM slides from Feb 19, then start today's slides on gene finding
- Some science news of the day: "In the past 12 months Americans have searched for Python on Google more often than for Kim Kardashian"
- For a few more examples of HMMs in action, here's the recent paper on sequencing the human genome by nanopore, which used HMMs in 3-4 different ways for polishing, contig inspection, repeat analysis and 5-methylcytosine detection.
- The UCSC genome browser
Problem Set 2, due before midnight Mar. 6, 2019:
- Problem Set 2.
- You'll need these 3 files: State sequences, Soluble sequences, Transmembrane sequences
Reading (a couple of old classics + a review):
Feb 19, 2019 - HMMs II
- We're finishing up the slides from Feb. 14.
- I was just pointed to this great interactive visualization of Markov Chains, by Victor Powell & Lewis Lehe. It's worth checking out to build some intuition.
- A non-biological example of using log odds ratios & Bayesian stats to learn the authors of the Federalist Papers
Feb 14, 2019 - Hidden Markov Models
- Happy Valentine's Day!
- Don't forget: Rosalind Homework #2 (worth 10% of your final course grade) is due by 11:59PM February 20.
- Modern Statistic for Modern Biology, by Susan Holmes and Wolfgang Huber, discussed last time. It's currently available online and due to be released on dead tree in the US in April. (FYI, all code is in R.)
- Today's slides
- HMM primer and Bayesian statistics primer #1, Bayesian statistics primer #2, Wiki Bayes
- Care to practice your regular expressions? (In python?)
Feb 12, 2019 - Biological databases
- Homework #2 (worth 10% of your final course grade) has been assigned on Rosalind and is due by 11:59PM February 20.
- Science news of the day: Does gum disease cause Alzheimer's?
- 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 7, 2019 - BLAST
- Science news of the day: Yet more CRISPR baby news!
- 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.
Feb 5, 2019 - Sequence Alignment II
- We're finishing up the slides from Jan. 31.
- Science-ish news of the day: The Dance Your Ph.D. contest is on!
- Fact and Fiction in Sequence Alignments
- Dynamic programming primer
- An example of dynamic programming using Excel, created by Michael Hoffman (a former U Texas undergraduate, now U Toronto professor, who took a prior incarnation of this class)
- A few examples of proteins with internally repetitive sequences: 1, 2, 3
Jan 31, 2019 - Sequence Alignment I
- For those of you who might be interested, Rosalind is having a Bioinformatics Contest. Sign up runs until Feb. 2, the qualification round is Feb. 2-10, and Feb. 23 is the final round, with 24 hours to solve as many problems as you can. First prize in 2019 is to get your genome (exome) sequenced or get your own nanopore sequencer!
- Today's slides
Problem Set I, due before midnight Feb. 11, 2019:
- 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.
- 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 29, 2019 - Intro to Python #2
- We'll be finishing Python slides from last time, plus Rosalind help & programming Q/A, maybe a glimpse of next lecture.
- Science news of the day: A 500 year experiment, with a nice commentary in The Atlantic
- Statistics in Python
Jan 24, 2019 - Intro to 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 22, 2019 - Introduction
- Science news of the day (& a reminder of the importance of ethics in science!): Chinese authorities say world's first gene-edited babies were illegal
- Today's slides
- Some warm-up videos to get you started on Python (2 not 3, unless you pay for an upgrade): Code Academy's Python coding for beginners
- Khan Academy has archived their videos on Python here
- We'll be conducting homework using the online environment Rosalind. Go ahead and register on the site, and enroll specifically for BCH394P-BCH364C-Spring2019 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 31.
- 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!!!
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 graduate students and upper division undergrads (with permission) in natural sciences and engineering.
Prerequisites: Basic familiarity with molecular biology, statistics & computing, but realistically, it is expected that students will have extremely varied backgrounds. Undergraduates have additional prerequisites, as listed in the catalog.
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),
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 29, 2019. The last 2.5 classes will be spent presenting your projects to each other. (The presentation will account for 5/25 points for 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 29, 2019.
- How to make a web site for the final project
- Google Site: https://support.google.com/sites/answer/153197?hl=en