An introduction to computational structural and systems biology. Theory and practice of Protein and RNA structure prediction, RNA-RNA, Protein-RNA, protein-protein interaction prediction, biological network analysis.

GENERAL INFORMATION


Syllabus:
This class extends the material covered in COMP462/561 (i.e. Computational Biology Methods and Research). We introduce fundamental concepts and techniques in computational structural biology and computational systems biology, which have a broad impact on the understanding of gene regulation processes and biological systems mechanisms.

This course covers the theory and practice of computational techniques used in these research areas, such as dynamic programming algorithms for RNA structure analysis, molecular dynamics and machine learning techniques for protein structure prediction, and graphical models for biological networks analysis. We also feature practical sessions introducing how to use state-of-the-art software.

Teaching Staff:

Lectures:

  • Tuesday & Thursday, 8:35am to 9:55pm in Leacock 14.

Office hours:

Prof. Jérôme WaldispühlTuesday after classENGTR 3106
Roman Sarrazin-GendronThursday 2pm-3:30pmENGTR 3110 (if no one knock at 3140)

Course Webpage: http://www.cs.mcgill.ca/~jeromew/comp564.html

Course Material:
All the material needed for this class will be available on the public course web page. There is no required textbook. Although, we recommend the following textbooks to deepen the material presented in class:

  • [CB2000] Peter Clote and Rolf Backofen, Computational Molecular Biology: An Introduction, Wiley, 2000.
  • [DEKM1998] Richard Durbin, Sean R. Eddy, Anders Krogh, and Graeme Mitchison, Biological Sequence Analysis, Cambridge University Press, 1998.
  • [GR2014] Jan Gorodkin and Walter Russo, RNA Sequence, Structure, and Function: Computational and Bioinformatics Methods, Humana Press, 2014.
Lecture slides will be made available in PDF form on the course web page.

Evaluation
Your final grade will be calculated as follows:

  • 30% for 2 assignments (15% each)
  • 35% for the final exam
  • 25% for the project report
  • 10% for the paper presentation
The final exam is closed book and electronic devices are not allowed.

ANNOUNCES


Release DateDue DateAnnounce
Apr 23, 2019Apr 24, 2019The instructor will hold special office hours on Wednesday April 24 from 10h30 to 12h00.
Apr 11, 2019Apr 25, 2019We released a practice exam to prepare the final exam.
Mar 28, 2019Apr 30, 2019You can now access the project descriptions.
Mar 22, 2019Apr 12, 2019The second assignment is now available online.
Note: This version supersedes the draft posted on March 12. Please, download and read it again if you started working with the preliminary version.
Mar 11, 2019At least 24h before your presentationYou will use a pptx template for the oral presentation of your paper review. Instruction on the content of the slidedeck and guideline for your presentation are included in the comments of the slides. You are not allowed to change the number of slides. It is your responsability to identify the important aspects of the paper that have to be highlighted in your presentation.
Feb 25, 2019The paper review assignment is now available online.
Feb 6, 2019Mar 4, 2019The first assignment is now available online.
Note on Q3 (26/02/2019): The threshold used in Q3 is correct. Although the number of samples required to reach this value might be larger than the values suggested for testing in the assignment (i.e. >10,000). In the formula of error, you should consider only base pairs found in the dot.ps file. Be careful when converting the values from the dot.ps file.
Feb 6, 2019Feb 21, 2019A selection of papers to review is now available online. Enter your bid in the online form (at least 6) before Feb 21.
Feb 5, 2019We hold a lab session to practice the algorithms covered in class. The class is moved to TR 3120.
Jan 7, 2019Welcome to COMP564!

SCHEDULE


DateTopicMaterial
Lecture 1Jan 8Syllabus. Introduction to RNA structure and function. Timeline of RNA bioinformatics.[Slides]
Lecture 2Jan 10 & 15RNA minimum free energy secondary structure prediction.
Application: The Vienna RNA package (RNAfold).
[Slides]
Chapter 2 of [GR2014]
Lecture 3Jan 17Ensemble of structures and base pair probabilities.[Slides]
Chapter 4 of [GR2014]
Lecture 4Jan 22Stochastic prediction of RNA Secondary Structures.
Application: The Vienna RNA package (RNAsubopt).
[Slides]
Chapter 4 of [GR2014]
Lecture 5Jan 24Comparative modeling of RNAs.
Application: Infernal & the Rfam database.
[Slides]
Chapter 3 of [GR2014]
Lecture 6Jan 29Simultaneous folding and alignment of structured RNAs.[Slides]
Chapter 5, 8, and 9 of [GR2014]. Chapter 6, 9, and 10 of [DEKM1998].
Lecture 7Jan 31Variations on RNA secondary structure prediction
Application: The Vienna RNA package (kinefold).
[Slides]
Chapter 13 of [GR2014]
Lecture 8Feb 5Practical session I (TR3120) [Instructions]
Lecture 9Feb 7Evolution of RNAs and sequence-structure maps
Application: Jupyter notebook for simulating RNA evolution.
[Slides]
Chapter 16 of [GR2014]
Lecture 10Feb 12RNA 3D Modeling
Application: RNA-MoIP and MC-Sym
[Slides]
Chapter 13 of [GR2014]
Lecture 11Feb 14Pseudo-knots and RNA-RNA interaction predictions
Application: The Vienna RNA package (RNAup).
[Slides]
Chapter 19 of [GR2014]
Lecture 12Feb 19Introduction to Protein structure. Timeline of protein structure prediction.
Application: The protein data bank (PDB)
[Slides]
Lecture 13Feb 21Protein secondary structure prediction using Neural Networks (Part I)
Application: PSIPRED
[Slides]
Lecture 14Feb 26Protein secondary structure prediction using Neural Networks (Part II)(Slides from previous lecture)
Lecture 15Feb 28Protein residue contact prediction
Application: EVcoupling
[Slides]
Lecture 16Mar 12Protein fold recognition and threading
Application: RaptorX
[Slides]
Lecture 17Mar 14Minimalist models: The HP lattice model
Application: The CPSP software suite
[Slides]
Lecture 18Mar 19Molecular dynamics simulation
Application: GROMACS
[Slides]
[Tutorial] [Tutorial 2] [Data]
Lecture 19Mar 21Protein-Protein Interaction network alignment
Application: Struct2net, IsoRank
[Slides]
Lecture 20Mar 26Practical session II (TR3120)[Instructions]
Lecture 22Mar 28Student presentations:
  • Ryszard Kubinski (Adhikari_PNAS_2014)
  • Daria Kiseleva (Marks_PLoSONE_2011)
  • Michael Colalillo (Zhu_ISMB_2018)
  • Yanlin Zhang (Peng_Proteins_2011)
  • Yash Patel (Cowen_JCB_2002)
  • Hua-Ting Yao (Hammer_RECOMB_2018)
Lecture 23Apr 2Student presentations
  • Richard Zhang (Rivas_JMB_1999)
  • Sean Nesdoly (Will_PlosCompBio_2008)
  • Yeeren Low (Sato_Bioinformatics_2011)
  • David Lougheed (Zirbel_NAR_2015)
  • Jake Zhu (Cruz_NatureMethods_2011)
  • Matthew Etchells (Boniecki_NAR_2016)
Lecture 24Apr 4Student presentations
  • Ariane Duverdier (Mann_BMCBioinfo_2008)
  • Aseel Shakra (Glouzon_ISMB_2018)
  • Brock Jensen (Andronescu_RNA_2010)
  • Elliot Layne (Orenstein_Bioinformatics_2016)
  • Nisha Kabir (Nguyen_NatureComms_2016)
Lecture 25Apr 9Student presentations
  • Sequoia Crooks (Al-ANzi_PlosCompBio_2016)
  • Grace Smith (Aceved_Nature_2014)
  • John-Paul Tsai (Kim_Science_2006)
  • Alika Utepova (Kazan_PLoSCompBio_2010)
  • Bojia Qiu (Chindelevitch_Bioinformatics_2013)
  • Tristan Simas (Kuzu_CurrOpiStructBiol_2012)
Lecture 26Apr 11Student presentations
  • Eric Schmit (Remmert_NatureMethods_2012)
  • Liam Blount (Ma_JMLR_2013)
  • Dongjoon Lim (Rousseau_GenomeBiol_20140
  • Vincent Mallet (Hwang_PNAS_2018)
  • Orla Mahon (Chauvot_PLoSCompBio_2016)
  • Dmytro Politov (Reinharz_ISMB_2012)
Note: This schedule is subject to modification.

RULES & POLICIES


Background & Pre-requisites
Good understanding of basic algorithms (equivalent to COMP251), and core molecular biology concepts (i.e. DNA, RNA, Proteins structure and function). A basic progamming in Python. COMP462/561.
Undergraduate students: You can register to this class with the permission of the instructor.

Policy on discussion Board
Please follow common sense rules and etiquette for discussion board postings: be polite, avoid texting shorthand ("ur" instead of "you are", ...), choose a suitable subject line for your posting and use multiple postings for multiple subjects, keep your postings brief, etc.

Policy on collaborations
We greatly encourage you to discuss the assignment problems with each other. However, these discussions should not go so far that you are sharing code or giving away the answer. A rule of thumb is that your discussions should considered public in the sense that anything you share with a friend should be sharable with any student in the class. We ask you to indicate on your assignments the names of the persons with who you collaborated or discussed your assignments (including TA’s and instructors).

Policy on re-grading
If you wish us to re-grade a question on an exam (or assignment), we will do so. However, to avoid grade ratcheting, we reserve us the right to re-grade other questions on your exam as well.

Policy on final grades
I will use the same rules and formula for calculating the final grade for everyone. We understand that your performances may be influenced by many factors, possibly out of your control. However, that is the only way we can be fair. The only exceptions will be medical exceptions. In that case, I will require a medical note, which has to be also reported to McGill, and to be informed as early as possible. Failure to comply to these rules, may results in the impossibility to invoke a medical exception.

Policy on Assignments
Due date/time, location/mode for returning your solutions, and accepted formats will be announced in class and indicated on the course web page.
Failure to return your assignment in time will results in penalties or even absence of grading. Late submission of 24h or less will receive a penalty of 20%. In all other cases, your assignment will be refused and not graded.
Importantly, solutions that do not follow the requested format will receive a penalty. By default, we only accept PDF or TEXT files. Images (if any) must be embedded in a PDF. Do not compress your files. All files must open on LINUX SOCS workstations.
The quality of the presentation of your solution is very important. Unreadable material, cryptic notations, or bad organization of the material will results in penalties, and potentialy even an absence of grading. If you scan your hand-written solutions, it is your responsability to ensure that you submit a high-quality image (i.e. excellent luminosity, contrast, focus and resolution). The clarity of your explanations will also be an integral part of your final grade.

Policy on programming code
Questions in assignments may require you to write a Python program. We will provide, as much as possible, input and output data to test your programs. However, it will be your duty to ensure that your Java files compile on LINUX SOCS workstations. We will not grade programs that do not compile on these machines.
Submission of class files (instead of Java source files) will be considered as an absence of submission. Do not compress your files.

Use of French in assignments and exams
In accordance with McGill University’s Charter of Students’ Rights, students in this course have the right to submit in English or in French any written work that is to be graded.

McGill policies
McGill University values academic integrity. Therefore, all students must understand the meaning and consequences of cheating, plagiarism and other academic offenses under the Code of Student Conduct and Disciplinary Procedures. See this link for more information.

CONTACT


If you have any additional question, you can contact the instructor:

Jérôme Waldispühl
3630 University Street, Room 3106, Montreal QC H3A 0C6
(E-mail) jerome.waldispuhl@mcgill.ca
(Phone) +1 514 398 5018

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