PLNT*6500 Applied Bioinformatics

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The following description is for the course offering in Winter 2017 and is subject to change. It is provided for information only. The course outline distributed to the class at the beginning of the semester describes the course content and delivery, and defines the methods and criteria to be used in establishing the final grades for the course.

This course covers current methods for making use of large molecular data sets to identify the genes that control traits, to characterize genes' functions, and to infer genetic relationships among individuals. It focuses on case studies and current research in agriculture and medicine to introduce molecular data analysis methods, including analyzing molecular markers, constructing nucleotide and protein sequence alignments, constructing phylogenies, and finding motifs and genes in biological sequences. Lab sessions include an introduction to Unix and Perl for the biologist and hands-on use of several molecular data analysis programs.


Teaching Assistant:

William (Max) Hargreaves

Credit Weight:


Course Level:

  • Graduate

Academic Department (or campus):

Department of Plant Agriculture



Semester Offering:

  • Winter

Class Schedule and Location:

Please refer to WebAdvisor for class schedule and location.

Learning outcomes:

Specific Learning Outcomes:

(1) Literacy: Students will critique a scientific journal article to help understand specific instances in which bioinformatics has been used to test biological hypotheses. Students will learn skills for text processing using programming languages and a number of software analysis packages.

(2) Numeracy: A major part of this course is for students to gain an understanding of how character information- the nucleotide composition of a gene, for example- can be analyzed quantitatively to draw inferences about the biological attributes of the characters- i.e. their biochemical function and their evolutionary history. Students will also learn to apply statistical tests in the context of bioinformatics and study different approaches for data analysis.

(3) Sense of Historical Development: Students will learn how a number of bioinformatics approaches have their antecedents in the fields of systematics and linguistics and the integration of mathematics and statistics into the analysis of molecular data.

(4) Depth and breadth of understanding. The course provides many examples of scientific discoveries that have integrated statistics and mathematics concepts with biological concepts. By studying these examples and applying components of these examples to new problems, students will utilize concepts from disparate fields to solve problems.

(5) Independence of thought. The course describes major high calibre, peer-reviewed discoveries in bioinformatics. Students will  learn to critique these studies through class participation and in written assignments.

Lecture Content:

The lectures are divided into three topics. In each topic, lectures revolve around a number of research papers with similar themes.

Topic 1: Identifying key genes important for biological variation in agriculture and medicine.

In this topic we discuss key research articles that have identified genes that explain variation among plants and animals and the methods used in these studies. We discuss how genomes are sequenced and how the analysis of data from genotyping technologies, including genotyping by sequencing approaches, provide dense snapshots of genomic variation. We also cover algorithmic approaches for obtaining optimal sequence alignments. We discuss methods of genome annotation and how to collect database information. We note DNA changes that differentiate genomes and describe how associations between individuals' genotypes and phenotypes across a population can be used to identify genes. We will also describe challenges specific to association mapping studies.

Topic 2: The effects of small RNAs on gene expression

In this topic we discuss key research articles that have studied sRNAs and their effects on regulating genes' transcript abundances. We will discuss estimating global RNA abundances using RNA Seq technologies and understanding how to perform tests of significance for gene transcript abundance differences. We will discuss how sRNA encoding loci are identified in genomic DNA and how to identify sRNA target sites within nucleotide sequences. We also cover more generally how to identify conserved motifs across members of a protein-coding gene family. We review the application of probabilistic models for hypothesis testing in nucleotide sequence alignment and analysis. We describe approaches to estimate the statistical significance of a sequence alignment score.

Topic 3: Molecular evolution and its application

In this topic we discuss key research articles that have utilized phylogenetic approaches in evolution and criminal justice cases. We will discuss the terms and concepts of phylogenetics;  how to calculate and interpret evolutionary distances between pairs of sequences. Differences between algorithmic vs. optimization methods for phylogeny estimation are highlighted. Lectures will also cover distance, parsimony, maximum likelihood, and if time allows Bayesian  phylogenetic approaches.

Labs & Seminars:

Labs are computational only. They cover a number of analysis techniques including:

  • genetic map construction and quantitative trait loci mapping in a structured population.
  • methods for pairwise and multiple sequence alignments
  • RNASeq data analysis.
  • software for phylogeny generation and testing.
  • beginning instruction on text processing and searching using bash and PERL in a UNIX environment.

There are no seminars scheduled for this course.

Course Assignments and Tests:

Assignment or Test Contribution to Final Mark (%) Learning Outcomes Assessed
Weekly lab completion 17% 1, 2
Critical review papers that use bioinformatics methods 8% 1, 3, 4, 5
Critical review papers that use bioinformatics methods 15% 1, 3, 4, 5
Quiz 1 15%

1, 2, 4. 5

Quiz 2 15%

1, 2, 4, 5

Final Exam 30%

1, 2, 4, 5

Notes on assignments and tests:

Quiz 1 covers material up to Feb 8. Quiz 2 covers material from Feb. 10- Mar. 18th. For the final exam, about 1/3 of the exam will focus on material after Mar. 20th.

Final examination:

Please refer to Web Advisor for exam schedule and location.

Course Resources:

Required Texts:

Papers discussed in lecture and supporting literature will be available through CourseLink. Course notes will be available.

Recommended Texts:

Bioinformatics and Molecular Evolution by Higgs and Attwood

Lab Manual:

Lab exercises are posted on Courselink.

Other Resources:
  • An Introduction to Molecular Evolution and Phylogenetics by Linda Bromham
  • A Primer of Genome Science  by Greg Gibson and Spencer Muse.
  • Biological Sequence Analysis by Richard Durbin, Sean Eddy, Anders Krogh, and Graeme Mitchison.
  • Elementary Sequence Analysis by Brian Golding and Dick Morton
  • Molecular Evolution: a Phylogenetic Approach by Roderic Page

Class notes, links to discussed research papers, and other information is available on Courselink.

Course Policies:

Grading Policies:

All assignments are due in class on their due date unless we have made prior arrangements. Five points will be deducted from quizzes taken after the scheduled time. I will also deduct one point for every day after the scheduled quiz date. Make-ups are not possible later than five days after the quiz date. For written assignments, I will subtract two points per day of lateness.

Course Policy on Group Work:

Individuals within a group are expected to contribute equally.

Course Policy regarding use of electronic devices and recording of lectures:

Electronic recording of classes is expressly forbidden without consent of the instructor. When recordings are permitted they are solely for the use of the authorized student and may not be reproduced, or transmitted to others, without the express written consent of the instructor.


Other Course Information:

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