Difference between revisions of "BCH394P BCH364C 2022"

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* [https://sites.google.com/utexas.edu/csra-orthogonality-project/results Development of a Model to predict CsrA-RNA binding, by Ryan Buchser & Vinya Bhuvan]
 
* [https://sites.google.com/utexas.edu/csra-orthogonality-project/results Development of a Model to predict CsrA-RNA binding, by Ryan Buchser & Vinya Bhuvan]
 
* [https://sites.google.com/view/bch-364c-final-project/home Extending Cascade Models of Synaptic Plasticity, Argha Bandyopadhyay]
 
* [https://sites.google.com/view/bch-364c-final-project/home Extending Cascade Models of Synaptic Plasticity, Argha Bandyopadhyay]
 +
* [https://sites.google.com/view/ama1-polymorphism/home?authuser=0 Genetic diversity of Plasmodium falciparum apical membrane antigen-1, by Christopher Smith, Jeffrey Marchioni, Jin Eyun Kim]
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* [https://sites.google.com/view/bioinformaticsproject/introduction-and-goals?authuser=0 Identifying putative stabilizing disulfide bond mutations for viral fusion protein vaccine design with machine learning, by Doug Townsend & W. Chase Sanders]
 +
* [https://sites.google.com/view/finalproject-com/title?authuser=0 Investigation of Unique Intron Associated RT, by Jose Alvarado]
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* [https://sites.google.com/utexas.edu/oishika-das-bioinformatics-pro/home Breast Cancer Classification Using Tumor Characteristics: An Analysis through Pandas and Numpy, by Oishika Das]
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* [https://sites.google.com/view/kcgslc30a10 Regulators of Manganese Efflux Transporter SLC30A10, by Kerem Gurol]
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* [https://sites.google.com/view/bioinformaticsprojectjustin/references You discovered an antibody, now what?, by Justin Lerma]
 +
* [https://sites.google.com/view/bch394p-project/home Predicting ISGylation Sites with Machine Learning Models, Xu Zhao]
  
  

Latest revision as of 14:27, 29 April 2022

BCH394P/BCH364C Systems Biology & Bioinformatics

Course unique #: 54540/54450
Lectures: Tues/Thurs 11 – 12:30 PM on Zoom until Jan 27 (log in to Canvas for the link), then in WEL 2.110
Instructor: Edward Marcotte, marcotte @ utexas.edu

  • Office hours: Wed 11 AM – 12 noon on Zoom

TA: Muyoung Lee, ml49649 @ utexas.edu

  • TA Office hours: Mon 1-2/Fri 11-12 on Zoom

Class Slack channel: ut-sp22-bioinfo.slack.com
Class Canvas site: https://utexas.instructure.com/courses/1325179

Lectures & Handouts

Apr 26 - May 3, 2022 - Final Project Presentations

  • Welcome to the end of the course! You made it! The last 3 days will be presentations of your class projects.
  • We'll spend 5 minutes on the Course - Instructor Survey Thursday morning.

Here's a sampling of some of the completed course projects (posted with permission, with more to come):


April 21, 2022 - Synthetic Biology, highly compressed

  • 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 19, 2022 - Phenologs

  • Remember: The final project web page is due by midnight April 25, 2022, 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 14, 2022 - Networks

  • Today's slides
  • Metabolic networks: The wall chart (it's interactive. For example, can you find enolase?), 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 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.
    • HumanNet (older versions for other organisms at netbiolab.org and FunctionalNet), which provides interactive searches of a human functional gene network. The earlier versions helped my own group find genes for a wide variety of biological processes.
    • 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 human protein complex map from Hu.MAP.
    • Clustering algorithms can be applied to networks. For example, we frequently use the Walktrap algorithm developed by Pascal Pons and Matthieu Latapy, which is available in the Python iGraph library. Here's a nice blog demonstration using it.

Reading:


Apr 12, 2022 - Deep learning

  • Guest speaker: Dr. Claire McWhite, who is a Lewis-Sigler Fellow at Princeton where she develops protein language models using deep learning. She previously completed her B.S. at Rice University, interned at the National Cancer Institute, earned her Ph.D. at UT Austin working extensively in computational biology and proteomics, and appeared as a contestant in BahFest.
  • Today's slides
  • Why neural networks aren't neural networks


Apr 7, 2022 - Principal Component Analysis (& the curious case of European genotypes)

A smattering of links on PCA:


Apr 5, 2022 - Classifiers


Mar 31, 2022 - 3D Protein Structure Modeling

  • Guest speaker: Caitie McCafferty, who is finishing up her Ph.D. at UT Austin, prior to which she earned her B.S. in Chemistry at the University of Maryland and worked as a computational researcher for 3 years at the NIH National Eye Institute. She has mentored numerous undergrads in computational structural biology, co-developed the citizen science video game Protein Puzzles, and is now leading local efforts to solve the 3D structures of ciliary proteins by cryoEM, mass spec, and integrative modeling.
  • Today's slides and the EVcouplings method discussed
  • 3D macromolecular structural modeling software: UCSF ChimeraX, the Rosetta software suite, and an overview of what it can do for you, and last but not least: AlphaFold predicted structures and the AlphaFold colab where you can run your own structure predictions.
  • & a few other useful 3D structure tools: The Protein Data Bank, MODELLER, and Pymol


Mar 29, 2022 - Clustering II

  • We'll be continuing the slides from just before the guest lecture
  • I'm also posting the next (last) problem set:

Problem Set 3, due before midnight Apr. 8, 2022. You will need the following software and datasets:

Reading:


Mar 24, 2022 - Proteomics


Mar 22, 2022 - Functional Genomics & Data Mining - Clustering I

  • Science news of the day: The latest tests of HexaPro look very promising! UT has granted a royalty-free license to 80 low and middle income countries around the world for vaccine use of HexaPro. You can read more about the development of HexaPro by the McLellan, Finkelstein, and Maynard labs in this very nice NYT article.
  • Today's slides

Reading:


Mar 15,17, 2022 - SPRING BREAK

  • Don't forget to finish HW3 and turn in the proposal for your course project by "March 21st".


Mar 10, 2022 - Motifs


Mar 8, 2022 - NGS analysis best practices

  • Homework #3 (worth 10% of your final course grade) has been assigned on Rosalind and is due by 11:59PM March 21. In past years, we've run into problems with Rosalind timing out before Meme completes although it usually runs eventually, so be warned you may have to try it a couple of times. Meme also runs faster using the "zero to one" or "one" occurrence per sequence option, rather than the "any number of repeats" option.
  • Guest speaker: Anna Battenhouse from the Center for Biomedical Research Support, where she maintains the Biomedical Research Computing Facility.
  • Today's slides


Mar 3, 2022 - Genomes II

Supporting reading:


Mar 1, 2022 - Genome Assembly


Feb 24, 2022 - Gene finding II

  • Short classes at UT start this week in genome sequencing, proteomics, and bioinformatics
  • Several of you have asked about programming the Viterbi algorithm for the homework, so I wanted to make sure everyone realized that you are not required to program it. The sequence is short enough that you can solve it in a spreadsheet if that's easier for you.
  • We're finishing up the slides from last time.

Reading:


Feb 22, 2022 - Gene finding

Reading (a couple of old classics + a review + better splice site detection):


Feb 17, 2022 - HMMs II Problem Set 2, due before midnight Mar. 3, 2022:


Feb 15, 2022 - Hidden Markov Models

  • Happy Class-After-Valentine's Day!
  • Don't forget: Rosalind Homework #2 (worth 10% of your final course grade) is due by 11:59PM February 16.
  • More stats for comp biologists worth checking out: Modern Statistic for Modern Biology, by Susan Holmes and Wolfgang Huber. It's currently available online and available on dead tree. (FYI, all code is in R.)
  • Today's slides

Reading:


Feb 10, 2022 - Biological databases


Feb 8, 2022 - BLAST


Feb 3, 2022 - ICEPOCALYPSE 2022

  • WEATHER WARNING: Per the President's email, "all classes will be canceled", so no class on Feb. 3. We'll pick up with the scheduled material when campus re-opens!
  • Yes, responding to popular request, we'll extend the Problem Set 1 deadline to midnight Feb. 9, 2022


Feb 1, 2022 - Sequence Alignment II


Jan 27, 2022 - Sequence Alignment I

Problem Set I, due before midnight Feb. 7, 2022:

  • 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.

Extra reading, if you're curious:


Jan 25, 2022 - Intro to Python #2

  • Reminder that today will be part 2 of the "Python boot camp" for those of you with little to no previous Python coding experience. We'll be finishing the slides from last time, plus Rosalind help & programming Q/A.
  • Also, don't forget that the Rosalind assignments are due by 11:59PM January 27. Please do start if you haven't already, or you won't have time to get help if you have any issues installing Python.
  • Another strong recommendation (really) to the Python newbies to download Eric Matthes's GREAT, free Python command cheat sheets that he provides to accompany his Python Crash Course book.


Jan 20, 2022 - Intro to Python

  • STANDARD REMINDER: My email inbox is always fairly backlogged (e.g., my median time between non-spam emails was 11 minutes when I measured it some time ago, and it's gotten much worse since then), so please copy the TA on all emails to help us make sure they get taken care of.
  • Today's slides
  • E. coli genome
  • Python 2 vs 3? Bioinformatics researchers held out for 2 until quite recently, but the shift to 3 is pretty clear now. We'll use Python 3 (the latest version is 3.10, but any recent version will be fine), but Rosalind and some materials are only available in Python 2.7, so we'll generally try to be version agnostic for compatibility. Use whichever you wish, but be aware that support for Python 2.7 has officially been stopped. For beginners, the differences are quite minimal and are summarized in a table here. There's also a great cheat sheet here for writing code compatible with both versions.


Jan 18, 2022 - Introduction

  • Today's slides
  • We'll be conducting homework using the online environment Rosalind. Go ahead and register on the site, and enroll specifically for BCH394P/364C (Spring 2022) Systems Biology/Bioinformatics 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 27.

Here are some online Python resources that you might find useful:

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 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. By the end of the course, students will know the fundamentals of important algorithms in bioinformatics and systems biology, be able to design and implement computational studies in biology, and have performed an element of original computational biology research.

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
Online probability & stats texts: #1, #2 (which has some lovely visualizations)

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), which can be collaborative (1-3 students/project). 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, 2022. The last 3 classes will be spent presenting your projects to each other. (The presentation will account for 5/25 points of the project grade.)

Since we will be in coronavirus lockdown at the start of this semester, this portion of the class will be web-based. We will hold lectures by Zoom during the normally scheduled class time. Log in to the UT Canvas class page for the link, or, if you are auditing, email the TA and he will send the link by return email. Slides will be posted before class on this web site so you can follow along with the material. We'll record the lectures & post the recordings afterward on Canvas so any of you who might be in other timezones or otherwise be unable to make class will have the opportunity to watch them. Note that the recordings will only be available on Canvas and are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings.

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 (except the final collaborative project). 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 and cause for dismissal with a failing grade, possibly expulsion (UT's academic integrity policy). In particular, no materials used in this class, including, but not limited to, lecture hand-outs, videos, assessments (papers, projects, homework assignments), in-class materials, review sheets, and additional problem sets, may be shared online or with anyone outside of the class unless you have the instructor’s explicit, written permission. Any materials found online (e.g. in CourseHero) that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in sanctions, including failure in the course.

The final project web site is due by midnight April 25, 2022.

Finally, between the pandemic and snowpocalypse, the last two years have really pushed our class schedule around a lot, so we’re going to reserve the last class day, May 5, as an emergency flex day. The current plan is for classes to end on May 3 and for there to be NO CLASS on May 5, but if weather, the pandemic, etc, leads to loss of lecture days, we’ll vote as a class to extend class to May 5.