BIN-MYSPE

YFO (Your Favourite Organism)

 

Keywords:  Scenario: Yeast cell-cycle; Model organism, YFO

 

 

Sorry!

This page is only a stub; it is here as a placeholder to establish the logical framework of the site but there is no significant content as yet. Do not work with this material until it is updated to "live" status.

 

Abstract

This unit discusses a scenario for model-organism based inference in a (possibly) uncharacterized organism and selects a "YFO" (Your Favourite Organism), a genome-sequenced fungus that is specific to you and aspects of which you will investigate throughout the course.

 

This unit ...

Prerequisites

You need the following preparation before beginning this unit. If you are not familiar with this material from courses you took previously, you need to prepare yourself from other information sources:

• Biomolecules: The molecules of life; nucleic acids and amino acids; the genetic code; protein folding; post-translational modifications and protein biochemistry; membrane proteins; biological function.

You need to complete the following units before beginning this one:

• FND-CSC-Data_models

 

Objectives

...

 

Outcomes

...

 

Deliverables

• Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
• Journal: Document your progress in your course journal.
• Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.

 

Evaluation

Evaluation: NA

This unit is not evaluated for course marks.

 

Contents

Scenario

You have learned about the concept of "cargo cult science". The "cargo" in Bioinformatics is to understand biology. This includes understanding how things came to be the way they are, and how they work. Both relate to the concept of function of biomolecules, and the systems they contribute to. But "function" is a rather poorly defined concept and exploring ways to make it rigorous and computable and explore it from the perspective of "collaborating" components is a major objective of this course. For this, we will work with an example:

a transcription factor that plays an important role in the regulation of the cell cycle. This is a relatively well-characterized protein that is part of a relatively well-characterized process. The genetic regulation of budding- and fission yeast cell-cycles has been lucidly described in a highly recommended review by McInerny (2011) (see also the short, recent introduction to cell-cycle regulated tranxcription by McInerny (2016)). One transcription factor, Mbp1 is a key component of the MBF complex (Mbp1/Swi6) in yeast. This complex regulates gene expression at the crucial G1/S-phase transition of the mitotic cell cycle and has been shown to bind to the regulatory regions of more than a hundred target genes. It is therefore a DNA binding protein that acts as a control switch for a key cellular process, it is highly conserved across species, and its human homologue plays a role in human disease.

 

Model organism and YFO

Baker's yeast, Saccharomyces cerevisiae is one of the most important model organisms, a eukaryote that has been studied genetically and biochemically in great detail for many decades, and is easily manipulated with high-throughput experimental methods. But model organisms are studied for their value in inferring biology in other, less-well characterized proteins thorugh computational inference, thus you will adopt a different genome-sequenced fungus in which to make discoveries based on knowledge about yeast.

In this section we will set out on our exploration of the system that regulates the G1/S transition by focussing first on the Mbp1 protein in selected species from the kingdom of fungi, whose genome has been completely sequenced; our quest is thus also an exercise in model-organism reasoning: the transfer of knowledge from one, well-studied organism to others. It's reasonable to hypothesize that such central control machinery is conserved in most if not all fungi. But we don't know. Many of the species that we will be working with have not been characterized in great detail, and some of them are new to our class this year. And while we know a fair bit about Mbp1, we probably don't know very much at all about the related genes in other organisms: whether they exist, whether they have similar functional features and whether they might contribute to the G1/S checkpoint system in a similar way. Thus we might discover things that are new and interesting. This is a quest of discovery.

 

Choosing YFO (Your Favourite Organism)

Since we were trying to find related proteins in a different species, our next task is to find suitable species.

For this purpose we create a lottery to assign species at (pseudo) random to students, so that everyone in the class has a good chance to be working on their own species. The technical details are in the R scripts that implement the species search and distribution. In brief, we define a function that picks one species from a long list at random - but to make sure this process is reproducible, we'll set a seed for the random number generator. Obviously, everyone has to use a different seed, or else everyone would end up getting the same species assigned. Thus we'll use your Student Number as the seed. This is an integer, so it can be used as an argument to R's set.seed() function, and it's unique to each of you. The choice is then random, reproducible and unique.

You may notice that this process does not guarantee that everyone gets a different species, and that all species are chosen at least once. I don't think doing that is possible in a "stateless" way (i.e. I don't want to have to remember who chose what species), given that I don't know all of your student numbers. But if anyone can think of a better solution, that would be neat.

Is it possible that all of you end up working on the same species anyway? Indeed. That's the problem with randomness. But it is not very likely.

What about the "suitable species" though? Where do they come from? For the purposes of the course "quest", we need species

• that actually have transcription factors that are related to Mbp1;
• whose genomes have been sequenced; and
• for which the sequences have been deposited in the RefSeq database, NCBI's unique sequence collection.

 

Task:

 

• Open RStudio and load the ABC-units R project. If you have loaded it before, choose File → Recent projects → ABC-Units. If you have not loaded it before, follow the instructions in the RPR-Introduction unit.
• Choose Tools → Version Control → Pull Branches to fetch the most recent version of the project from its GitHub repository with all changes and bug fixes included.
• Type init() if requested.
• Open the file BIN-YFO and follow the instructions.

 

Note: take care that you understand all of the code in the script. Evaluation in this course is cumulative and you may be asked to explain any part of code.

 

 

 

Further reading, links and resources

• Database normalization

 

Notes

 

Self-evaluation

 

 

---

 

If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.

---

 

About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2017-08-25

Version:

0.2

Version history:

• 0.2 First contents
• 0.1 First stub

---

This copyrighted material is licensed under a Creative Commons Attribution 4.0 International License. Follow the link to learn more.