Computational Systems Biology Main Page

Computational Systems Biology

Course Wiki for BCB420 (Computational Systems Biology) and JTB2020 (Applied Bioinformatics).

 

This is our main tool to coordinate information, activities and projects in University of Toronto's computational systems biology course BCB420. If you are not one of our students, this site is unlikely to be useful. If you are here because you are interested in general aspects of bioinformatics or computational biology, you may want to review the Wikipedia article on bioinformatics, or visit Wikiomics. Contact boris.steipe(at)utoronto.ca with any questions you may have.

 

Warning – this page and all associated course pages are currently under intense revision to prepare for the 2018 Winter session. This course will be delivered in part via an "inverted", knowledge-network format, and in part via a project centred format. I will contact students later in December to enrol everyone in the course mailing list.

 

 

 

BCB420 / JTB2020

These are the course pages for BCB420H (Computational Systems Biology). Welcome, you're in the right place.

These are also the course pages for JTB2020H (Applied Bioinformatics). How come? Why is JTB2020 not the graduate equivalent of BCB410 (Applied Bioinformatics)? Let me explain. When this course was conceived as a required part of the (then so called) Collaborative PhD Program in Proteomics and Bioinformatics in 2003, there was an urgent need to bring graduate students to a minimal level of computer skills and programming; prior experience was virtually nonexistent. Fortunately, the field has changed and our current graduate students are usually quite competent at least in some practical aspects of computational biology. In this course we profit from the rich and diverse knowledge of the problem-domain our graduate students have, while bringing everyone up to a level of competence in the practical, computational aspects.

The 2018 course...

In this course we pursue a wholly problem oriented format:

• We start from an interesting challenge in computational systems biology;
• We'll formulate an approach to this challenge as a project, defining the the resources we need – data sources, algorithms, programming- and collaboration support;
• We'll define students' roles in the project according to their skills and experience;
• Then we will implement the project.

Organization

Dates

BCB420/JTB2020 is a Winter Term course.

Lectures: Tuesdays, 16:00 to 18:00. (Classes start at 10 minutes past the hour.)

Final Exam: None for this course.

Location

MS 4171 (Medical Sciences Building).

Departmental information

For BCB420 see the BCB420 Biochemistry Department Course Web page.

For JTB2020 see the JTB2020 Course Web page for general information.

Preparation

Undergraduate students in this course will often have taken my BCH441 course; graduate students come from a wide variety of backgrounds. The course requires (i) a solid understanding of molecular biology, (ii) introductory level knowledge of bioinformatics, (iii) a working knowledge of the R programming language. I am only weakly enforcing prerequisites, they are basically your own responsibility. Knowledge of molecular biology is something you have to acquire on your own and bring to class. If you are comfortable reading the introductory literature listed below, and understanding the biology, you should be good to go.

The preparation material detailed below will be the subject of our first Quiz in the second week of class. Please have a look at the three topics below in order to get a head start.

1 – A working knowledge of R ...

• Work through the R tutorial on this site and complete the tasks and exercises in the tutorial and the associated scripts.

2 – A basic knowledge of Bioinformatics ...

Here is a list of detailed, introductory bioinformatics tutorials. If you have taken BCH441, the material will serve as a review. If you have not taken BCH441, this will help you get up to speed with basic concepts and R code.

• Data
• Sequence

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• Structure
• Phylogeny
• Function

3 – Project specific prereading ...

• Vogelstein et al. (2013) Cancer genome landscapes. Science 339:1546-58. (pmid: 23539594)

  [ PubMed ] [ DOI ] Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.

  • Leiserson et al. (2015) Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat Genet 47:106-14. (pmid: 25501392)

    [ PubMed ] [ DOI ] Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.

    • Leiserson et al. (2016) A weighted exact test for mutually exclusive mutations in cancer. Bioinformatics 32:i736-i745. (pmid: 27587696)

      [ PubMed ] [ DOI ] MOTIVATION: The somatic mutations in the pathways that drive cancer development tend to be mutually exclusive across tumors, providing a signal for distinguishing driver mutations from a larger number of random passenger mutations. This mutual exclusivity signal can be confounded by high and highly variable mutation rates across a cohort of samples. Current statistical tests for exclusivity that incorporate both per-gene and per-sample mutational frequencies are computationally expensive and have limited precision. RESULTS: We formulate a weighted exact test for assessing the significance of mutual exclusivity in an arbitrary number of mutational events. Our test conditions on the number of samples with a mutation as well as per-event, per-sample mutation probabilities. We provide a recursive formula to compute P-values for the weighted test exactly as well as a highly accurate and efficient saddlepoint approximation of the test. We use our test to approximate a commonly used permutation test for exclusivity that conditions on per-event, per-sample mutation frequencies. However, our test is more efficient and it recovers more significant results than the permutation test. We use our Weighted Exclusivity Test (WExT) software to analyze hundreds of colorectal and endometrial samples from The Cancer Genome Atlas, which are two cancer types that often have extremely high mutation rates. On both cancer types, the weighted test identifies sets of mutually exclusive mutations in cancer genes with fewer false positives than earlier approaches. AVAILABILITY AND IMPLEMENTATION: See http://compbio.cs.brown.edu/projects/wext for software. CONTACT: braphael@cs.brown.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Grading and Activities

 

Activity	Weight BCB410 - (Undergraduates)	Weight JTB2020 - (Graduates)
5 Self-evaluation and Feedback sessions("Quiz")	40 marks (5 x 8)	30 marks (5 x 6)
Class project contributions A maximum of 45 marks plus 1/2 of marks lost in quizzes, combined, for tasks from the Project Task List	45 marks	45 marks
Participation	15 marks	15 marks
Contribution to Project Manuscript Draft		10 marks
Total	100 marks	100 marks

A note on marking

I do not adjust marks towards a target mean and variance (i.e. there will be no "belling" of grades). I feel strongly that such "normalization" detracts from a collaborative and mutually supportive learning environment. If your classmate gets a great mark because you helped him with a difficult concept, this should never have the effect that it brings down your mark through class average adjustments. Collaborate as much as possible, it is a great way to learn.

Prerequisites

You must have taken an introductory bioinformatics course as a prerequisite, or otherwise acquired the necessary knowledge. Therefore I expect familiarity with the material of my BCH441 course. If you have not taken BCH441, please update your knowledge and skills before the course starts. I will not make accommodations for lack of prerequisites. Please check the syllabus for this course below to find whether you need to catch up on additional material, and peruse this site to find the information you may need. A (non-exhaustive) overview of topics and useful links is linked here.

Timetable and syllabus

Syllabus and assignments will still be in flux for a few weeks.

 

Week	In class: Tuesday, Jan. 10	Readings	Assignment	In class: Tuesday, Jan. 17
1	Syllabus Projects Important dates Grading Organization Signup to mailing list and Student Wiki. Biological Systems science is based on network analysis	R Tutorial		Quiz 1 (See Task list for contents. )

 

Week	In class: Tuesday, Jan. 17	Readings	Assignment	In class: Tuesday, Jan. 24
2	Data sources and workflows Development principles Writing R packages Collaboration tools Defining the class project	TBD		Quiz 2 Perspectives ... TBD.

 

Week	In class: Tuesday, Jan. 24	Readings	Assignment	In class: Tuesday, Jan. 31
3		TBD		Quiz 3 Perspectives ... TBD.

 

Week	In class: Tuesday, Jan. 31	Readings	Assignment	In class: Tuesday, Feb. 7
4		TBD		Quiz 4 Perspectives ... TBD

 

Week	In class: Tuesday, Feb. 7	Readings	Assignment	(In class): Tuesday, Feb. 14
5		TBD		Quiz 5 Perspectives ... TBD

 

Week	(In class): Tuesday, Feb. 14	Readings	Assignment	In class: Tuesday, Feb. 28
6	Note: No class on the following Tuesday, February 20 (Reading Week).	TBD		– Perspectives ... TBD

 

Week	In class: Tuesday, Feb. 28	Readings	Assignment	In class: Tuesday, March 7
7	Project work	TBD		– Perspectives ... TBD

 

Week	In class: Tuesday, March 7	Readings	Assignment	In class: Tuesday, March 14
8	Note: March. 13 - Drop Date for the course. Project work	TBD		– Perspectives ... TBD

 

Week	In class: Tuesday, March 14	Readings	Assignment	In class: Tuesday, March 21
9	Project work	TBD		– Perspectives ... TBD

 

Week	In class: Tuesday, March 21	Readings	Assignment	In class: Tuesday, March 28
10	Project work	TBD		– Perspectives ... TBD

 

Week	In class: Tuesday, March 28	Readings	Assignment	In class: Tuesday, April 4
11	Project work	TBD		– Perspectives ... TBD

 

Week	In class: Tuesday, April 4	Readings
12	Project work	TBD

 

In depth...

• Glossary
• Mutation Data Matrices
• Amino acids

Resources

Course related

• Student Wiki
• The Course Google Group.
• Netiquette for the Group mailing list

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Notes