BIOL*3300 Applied Bioinformatics

course node page

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 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 Python for the biologist and hands-on use of several molecular data analysis programs. 


Teaching Assistant:

Haiyang Chang

Credit Weight:


Course Level:

  • Undergraduate

Academic Department (or campus):

Department of Plant Agriculture



Semester Offering:

  • Fall

Class Schedule and Location:

Please refer to Web Advisor for class schedule and location.

Learning outcomes:

Specitic 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 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 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 response

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 summarizing the expression data of groups of genes can help elucidate 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 tasks, but their utility is limited. Labs will introduce bash, Phython and R command-line programming that are used in real-world analyses.

There are no seminars scheduled for this course.

Course Assignments and Tests:

Assignment or Test Contribution to Final Grade

Weekly lab completion


Student written critical review of 3+ papers that use bioinformatics methods


Quiz 1


Quiz 2


Final Exam



Final examination:

Please refer to Web Advisor for exam schedule and location.

Course Resources:

Required Resource:
CourseLink (Website)
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. The instructor will also deduct one point for every day after the scheduled quiz date.  For written assignments, the instructor 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:

University Policies

Academic Consideration

When you find yourself unable to meet an in-course requirement because of illness or compassionate reasons, please advise the course instructor in writing, with your name, id#, and e-mail contact. See the academic calendar for information on regulations and procedures for Academic Consideration:

Academic Misconduct

The University of Guelph is committed to upholding the highest standards of academic integrity and it is the responsibility of all members of the University community, faculty, staff, and students  to be aware of what constitutes academic misconduct and to do as much as possible to prevent academic offences from occurring.

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:


The University of Guelph is committed to creating a barrier-free environment. Providing services for students is a shared responsibility among students, faculty and administrators. This relationship is based on respect of individual rights, the dignity of the individual and the University community's shared commitment to an open and supportive learning environment. Students requiring service or accommodation, whether due to an identified, ongoing disability or a short-term disability should contact the Student Accessibility Services (SAS), formerly Centre for Students with Disabilities (CSD), as soon as possible.

For more information, contact CSD at 519-824-4120 ext. 56208 or email or visit the Student Accessibility Services website:

Course Evaluation Information

Your ratings and comments are important.  Course evaluation data are used to assess and enhance the quality of teaching and student learning at the University of Guelph.  Student course ratings and comments are used as an important component in the Faculty Tenure & Promotion process, and as valuable feedback to help instructors improve their teaching effectiveness and to improve the delivery of the course.

Your responses will not affect your grade.  Course evaluation data are distributed to individual instructors after final grades have been submitted to the Registrar, following the completion of each academic semester.

Please be honest, respectful, constructive and thorough.  Instructors and review committees place great value on student course ratings and read all comments provided in course evaluations. It is helpful to provide comments on the strengths of the course, in addition to the areas for improvement.  Please refrain from personal comments unless they relate to teaching and learning.

Click here for the University of Guelph Course Evaluation System