PLNT*6500 Applied Bioinformatics

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The following description is for the course offering in Fall 2021 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, environmental biology and medicine to introduce molecular data analysis methods, including analyzing genome sequences, constructing nucleotide alignments, constructing phylogenies and finding motifs and genes in biological sequences. Lab sessions include an introduction to Unix and Python/R for the biologist and hands-on use of several molecular data analysis programs. Offered in conjunction with BIOL*3300. Distinct work is required of graduate students.

Instructors:

Lewis Lukens

Teaching Assistant:

Haiyang Chang

Credit Weight:

0.50

Course Level:

  • Graduate

Academic Department (or campus):

Department of Plant Agriculture

Campus:

Guelph

Semester Offering:

  • Fall

Class Schedule and Location:

Please refer to WebAdvisor for class schedule and location.

Learning outcomes:

Specific Learning Outcomes:
By the end of this course, you should be able to:
  1. critique scientific journal articles to help understand specific instances in which bioinformatics has been used to test biological hypotheses. Students will learn skills for data processing using programming languages and a number of software analysis packages.
  2. 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. 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. utilize concepts from disparate fields to solve problems. The course provides many examples of scientific discoveries that have integrated statistics and mathematics concepts with biological concepts.
  5. critique major, peer-reviewed discoveries using bioinformatics. Students will critique studies both in class participation and in written assignments.

Lecture Content:

The lectures are divided into three topics. In each topic, lectures revolve around bioinformatics methods and the research that use them.

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

In this topic we discuss bioinformatics methods for the analysis of genetic diversity. Research articles will describe relationships between molecular variation and trait variation. We will review genomics and genetic variation and learn how data from recent genotyping technologies assay molecular variation. Methods to associate genotypic variation with trait variation will be covered.

Topic 2:  Identifying the molecular basis of cellular responses

In this topic we discuss key research articles that have used genes' mRNA abundances to make biological insights. We will describe how to estimate a sample's RNA abundances using RNA Seq technologies and how to compare gene transcript abundances between samples exposed to different treatments. We will discuss how summarizng the expression data of groups of genes can help elucidate the biological differences between treatments. 

Topic 3: Molecular evolution and its application

In this topic we discuss methods to explore factors driving population and evolutionary change. Approaches to evaluate diversity and relatedness among biological samples using single nucleotide polymorphism data are covered. 

Labs & Seminars:
Labs are computational only and intended to enable you to learn bioinformatics skills. Labs will address techniques including:
  • short read sequence analysis
  • RNASeq data analysis.
  • diversity analyses and phylogeny generation and testing.
Web-based graphical user interfaces can perform a number of bioinformatics teasts, but their utility is limited. Labs will introduce bash, Phython and R command-line programming that are used in real-world anlayses.
 
There are no seminars scheduled for this course.

Course Assignments and Tests:

Assignment or Test Contribution to Final Mark (%)
Weekly lab completion 22%
Three critical reviews 18%
Quiz 1 15%
Quiz 2 15%
Final Exam 30%
 
Notes on assignments and tests:
 
The final exam is cumulative. The test will have approximately 1/4 of the questions drawn from last two weeks of class.

Final examination:

Please refer to Web Advisor for exam schedule and location.

Course Resources:

Required Resources:
CourseLink (Website) https://courselink.uoguelph.ca
 
Papers discussed in lecture and supporting literature will be available through CourseLink. Course notes will be available. I recommend taking notes in lecture and using course notes and readings as references.
 
Lab exercises are posted on CourseLink.

Course Policies:

Grading Policies:

All assignments are due in class on their due date unless we have made prior arrangements. Four 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. For written assignments, I will subtract two points per day of lateness. 

Lectures will be both on-lne and in-person.

All exams and quizzes are in-person only

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:

University Policies

Academic Consideration

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University of Guelph students have the responsibility of abiding by the University's policy on academic misconduct regardless of their location of study; faculty, staff and students have the responsibility of supporting an environment that discourages misconduct. Students need to remain aware that instructors have access to and the right to use electronic and other means of detection. Please note: Whether or not a student intended to commit academic misconduct is not relevant for a finding of guilt. Hurried or careless submission of assignments does not excuse students from responsibility for verifying the academic integrity of their work before submitting it. Students who are in any doubt as to whether an action on their part could be construed as an academic offence should consult with a faculty member or faculty advisor.

The Academic Misconduct Policy is detailed in the University Calenders:

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