Difference between revisions of "BCH394P BCH364C 2024"

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'''Feb 20, 2024 - Gene finding'''
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'''Feb 15, 2024 - Gene finding'''
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* Happy Valentine's Day!
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* Happy day-after-Valentine's Day!
 
* [http://www.marcottelab.org/users/BCH394P_364C_2024/BCH394P-364C-GeneFinding-Spring2024.pdf Today's slides on gene finding]  
 
* [http://www.marcottelab.org/users/BCH394P_364C_2024/BCH394P-364C-GeneFinding-Spring2024.pdf Today's slides on gene finding]  
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'''Problem Set 2, due before 10 PM, Feb. 26, 2024''':<br>
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* [http://www.marcottelab.org/users/BCH394P_364C_2024/BCH394P-364C_ProblemSet2_Spring2024.pdf '''Problem Set 2''']. 
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* You'll need these 3 files: [http://www.marcottelab.org/users/BCH394P_364C_2024/state_sequences State sequences], [http://www.marcottelab.org/users/BCH394P_364C_2024/soluble_sequences Soluble sequences], [http://www.marcottelab.org/users/BCH394P_364C_2024/transmembrane_sequences Transmembrane sequences]
 
* A nice commentary on gene finding: [http://www.marcottelab.org/users/BCH394P_364C_2024/2019StateOfGeneAnnotation.pdf Next-generation genome annotation: we still struggle to get it right]
 
* A nice commentary on gene finding: [http://www.marcottelab.org/users/BCH394P_364C_2024/2019StateOfGeneAnnotation.pdf Next-generation genome annotation: we still struggle to get it right]
 
* For a few more examples of HMMs in action, here's a [http://www.marcottelab.org/users/BCH394P_364C_2024/MinionHumanGenome.pdf 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.
 
* For a few more examples of HMMs in action, here's a [http://www.marcottelab.org/users/BCH394P_364C_2024/MinionHumanGenome.pdf 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.
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'''Feb 13, 2024 - HMMs II'''
 
'''Feb 13, 2024 - HMMs II'''
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* Happy day-before-Valentine's Day!
 
* Science news of the day: [https://doi.org/10.1101/2024.01.24.525373 a fun preprint] illustrating the scale of efforts to identify protein families. This one clustered "19 billion sequences in 18 days on 27 high performance computing nodes, using 250,000 CPU hours in total".  In all, they found 544 million sequence families (clusters) capturing ~94% of all known proteins, giving a sense of the overall size of the universe of proteins.
 
* Science news of the day: [https://doi.org/10.1101/2024.01.24.525373 a fun preprint] illustrating the scale of efforts to identify protein families. This one clustered "19 billion sequences in 18 days on 27 high performance computing nodes, using 250,000 CPU hours in total".  In all, they found 544 million sequence families (clusters) capturing ~94% of all known proteins, giving a sense of the overall size of the universe of proteins.
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'''Problem Set 2, due before 10 PM, Feb. 26, 2024''':<br>
 
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* [http://www.marcottelab.org/users/BCH394P_364C_2024/BCH394P-364C_ProblemSet2_Spring2024.pdf '''Problem Set 2''']. 
 
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* You'll need these 3 files: [http://www.marcottelab.org/users/BCH394P_364C_2024/state_sequences State sequences], [http://www.marcottelab.org/users/BCH394P_364C_2024/soluble_sequences Soluble sequences], [http://www.marcottelab.org/users/BCH394P_364C_2024/transmembrane_sequences Transmembrane sequences]
 
 
* Link to [http://setosa.io/blog/2014/07/26/markov-chains/ a great interactive visualization of Markov chains], by Victor Powell & Lewis Lehe. It's worth checking out to build some intuition. They correctly point out that [https://en.wikipedia.org/wiki/PageRank Google's PageRank algorithm] is based on Markov chains. There, the ranking of pages in a web search relates to how random walks across linked web pages spend more time on some pages than on others.
 
* Link to [http://setosa.io/blog/2014/07/26/markov-chains/ a great interactive visualization of Markov chains], by Victor Powell & Lewis Lehe. It's worth checking out to build some intuition. They correctly point out that [https://en.wikipedia.org/wiki/PageRank Google's PageRank algorithm] is based on Markov chains. There, the ranking of pages in a web search relates to how random walks across linked web pages spend more time on some pages than on others.
 
* A non-biological example of using log odds ratios & Bayesian stats [https://priceonomics.com/how-statistics-solved-a-175-year-old-mystery-about/ to learn the authors of the Federalist Papers]. In a related example, [https://arstechnica.com/science/2024/02/lost-and-found-code-breakers-decipher-50-letters-of-mary-queen-of-scots/ researchers just decoded >50 coded letters from a French archive] and discovered they were lost correspondence from Mary, Queen of Scots, before she was executed in 1587 for treason against Elizabeth I.  The researchers used an approach closely related to computing log odds ratios of 5-mer frequencies between putative decoded texts and known free text to figure out the correct ciphers. If you're curious, you can read about it in [https://www.tandfonline.com/doi/full/10.1080/01611194.2022.2160677 Appendix A of their paper]
 
* A non-biological example of using log odds ratios & Bayesian stats [https://priceonomics.com/how-statistics-solved-a-175-year-old-mystery-about/ to learn the authors of the Federalist Papers]. In a related example, [https://arstechnica.com/science/2024/02/lost-and-found-code-breakers-decipher-50-letters-of-mary-queen-of-scots/ researchers just decoded >50 coded letters from a French archive] and discovered they were lost correspondence from Mary, Queen of Scots, before she was executed in 1587 for treason against Elizabeth I.  The researchers used an approach closely related to computing log odds ratios of 5-mer frequencies between putative decoded texts and known free text to figure out the correct ciphers. If you're curious, you can read about it in [https://www.tandfonline.com/doi/full/10.1080/01611194.2022.2160677 Appendix A of their paper]

Revision as of 11:24, 12 February 2024

BCH394P/BCH364C Systems Biology & Bioinformatics

Course unique #: 54430/54305
Lectures: Tues/Thurs 11 – 12:30 PM WEL 2.110
Instructor: Edward Marcotte, marcotte @ utexas.edu

  • Office hours: Mon 4 – 5 PM on the class Zoom channel (available on Canvas)

TA: Vicki Deng, dengv @ utexas.edu

  • TA Office hours: Tues 1 - 2 PM / Fri 12 - 1 PM in MBB 3.204 or by appointment on Zoom

Class Canvas site: https://utexas.instructure.com/courses/1379402

Lectures & Handouts

Feb 8, 2024 - Hidden Markov Models

Reading:


Just a reminder about the mechanics of this class: Lectures will generally be about algorithms and concepts, while the coding help hours (or my office hours) are for you to get individual coding help and feedback. Please plan to go to coding help hours if you need that support!


Feb 6, 2024 - Biological databases

Homework #2 (worth 10% of your final course grade) has been assigned on Rosalind and is due by 10 PM February 14:

  • Besides giving a bit more programming experience, these questions will also give you some more practice with the BioPython Python library (see the "programming shortcuts" at the bottom of several questions). If you have yet to install BioPython on your computer, open an Anaconda prompt window (on a PC) or launch a console window from the Anaconda Navigator & type "pip install biopython". (You can use this approach to install most Python libraries.) There's a very useful tutorial here (also downloadable as a pdf file)
  • NOTE: The problem titled "Complementing a Strand of DNA" uses a now out-of-date call for IUPAC codes in the Programming Shortcut. Just delete the "from Bio.Alphabet import IUPAC" line & delete the ", IUPAC.unambiguous_dna" portion of the Seq() functions and it will work fine. e.g. all you need is something like this: my_seq = Seq("GATCGATGGGCCTATATAGGATCGAAAATCGC")

Extra reading/classes:


Feb 1, 2024 - BLAST


Jan 30, 2024 - Sequence Alignment II


Jan 25, 2024 - Sequence Alignment I

  • Reminder relevant to our discussion of ChatGPT last class: CNET & other news sources used it to write articles; this Gizmodo story found that "the AI-program fabricates information and bungles facts like nobody’s business" and CNET was "forced to issue multiple, major corrections". So, if you do opt to try ChatGPT to help with Python, be sure to check (and then double-check) everything.
  • Today's slides

Problem Set I, due 10PM Feb. 5, 2024:

  • 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. Also, if you prefer a .fasta format file (e.g. for BioPython), just add a first line to the text file starting with a ">" character, e.g. "> Hinfluenzae genome file".
  • 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 23, 2024 - Intro to Python II

  • 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.
  • *** Rosalind assignments are due by 10 PM January 24. ***
  • We'll talk a bit about ChatGPT today for co-programming
  • 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 18, 2024 - Intro to Python

  • Remember that today and the next lecture are dedicated to the Python Boot Camp to start getting those of you with minimal coding skills up to speed on the basics. Advanced programmers can skip class!
  • Today's slides.
  • E. coli genome (formatted as a text file with no extra lines; updated on Jan 23 to be the version matching the slides)
  • E. coli genome (formatted as a fasta file, which only differs here in having a header)
  • Don't forget that the Rosalind assignments are due by 10 PM January 24. Please do start if you haven't already, or you won't have time to get help if you have any issues installing Python.
  • We'll use Python version 3 (any version after 3.0 should be fine; just get the latest version in Anaconda), but Rosalind and some older materials are only available in Python 2.7, so we'll generally try to be version agnostic for compatibility. 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 16, 2024 - 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 2024) 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 10:00PM January 24.
  • We'll be using the free Anaconda distribution of Python and Jupyter (download here). Note that there are many other options out there, such as Google colab. You're welcome to use those, but we'll restrict our teaching and TA help sessions to Jupyter/Anaconda for simplicity.

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

  • First and foremost, and very, very useful if you're a complete Python newbie: Eric Matthes's Python Crash Course book. He made some GREAT, free Python command cheat sheets to support the book.
  • Practical Python, worth checking out!
  • If you have any basic experience at all in other programming languages, Google offered an extremely good, 2-day intro course to Python (albeit version 2) that is now available on Youtube.
  • Khan Academy has archived their older intro videos on Python here (again, version 2)

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, 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, will be able to design and implement computational studies in biology, and will 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. A truly excellent stats book with a free download is An Introduction to Statistical Learning, by James, Witten, Hastie, Tibshirani, and Taylor, and is accompanied by many supporting Python examples and applications.

Two other online probability & stats references: #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 the 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 10 PM, April 17, 2024. 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.)

If at some point, we have to go into coronavirus lockdown, that 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 we will send the link by return email. Slides will be posted before class 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 time zones 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 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 for 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 use of artificial intelligence tools (such as ChatGPT or Github co-pilot) in this class shall be permitted on a limited basis for programming assignments. You are also welcome to seek my prior-approval to use AI writing tools on any assignment. In either instance, AI writing tools should be used with caution and proper citation, as the use of AI should be properly attributed. Using AI writing tools without my permission or authorization, or failing to properly cite AI even where permitted, shall constitute a violation of UT Austin’s Institutional Rules on academic integrity.

The final project website is due by 10 PM April 17, 2024

  • How to make a website for the final project
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