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 scripting for the biologist and hands-on use of several molecular data analysis programs.
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Specitic Learning Outcomes
- 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.
- 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.
- 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.
- 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.
- critique major, peer-reviewed discoveries using bioinformatics. Students will critique studies both in class participation and in written assignments.
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: 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 understanding how to perform tests of significance for gene transcript abundance differences. We will discuss how summarizing the expression data of groups of genes can help elucidate biological processes.
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 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. 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 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 a scripting language in a UNIX environment.
There are no seminars scheduled for this course.
Course Assignments and Tests:
|Assignment or Test||Contribution to Final Grade|
Weekly lab completion
|Student presentation of bioinformatics method use in 3+ research papers||
|Student written critical review of 3+ papers that use bioinformatics methods||
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. The instructor 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, 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:
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:
- For Graduate Students: https://www.uoguelph.ca/registrar/calendars/graduate/2018-2019/genreg/sec_d0e2182.shtml
- For Undergraduate Students: https://www.uoguelph.ca/registrar/calendars/undergraduate/current/c08/c08-ac.shtml
- For Diploma Students: https://www.uoguelph.ca/registrar/calendars/diploma/current/c08/c08-ac.shtml
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:
- For Graduate Students: https://www.uoguelph.ca/registrar/calendars/graduate/2018-2019/genreg/sec_d0e2632.shtml
- For Undergraduate Students: https://www.uoguelph.ca/registrar/calendars/undergraduate/current/c08/c08-amisconduct.shtml
- For Diploma Students: https://www.uoguelph.ca/registrar/calendars/diploma/current/c08/c08-amisconduct.shtml
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