BIOL337-23S1 (C) Semester One 2023

Bioinformatics

15 points

Details:
Start Date: Monday, 20 February 2023
End Date: Sunday, 25 June 2023
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 5 March 2023
  • Without academic penalty (including no fee refund): Sunday, 14 May 2023

Description

The general aim of this course is to discuss major concepts in the bioinformatic analysis, application, handling and management of large-scale biological data, and apply these bioinformatics methods to real-world issues. The central focus will be on bringing together previously developed skills in programming, computing and data wrangling, and evaluating how these skills apply to biological datasets. This paper will also discuss the cultural, political, social and legal issues regarding data ownership, use and governance. The course will consist of regular lectures and computer labs, where students will be able to explore biological datasets using their knowledge of bioinformatics. The emphasis is on the amalgamation of students’ previous two years of training and experience, providing students with the context and the background required to apply their skills in the real world. Skills learnt will be assessed via short computer lab reports and a final exam. BIOL337 is a required course for enrolment in BIOL338 (Bioinformatics Project).

The focus of this course is on the critical understanding and application of published methods which are transferable to many modern bioinformatic applications.

You will apply computational methods to large-scale biological data, to generate biologically meaningful outputs, in a statistically-informed manner.  

Specific skills acquired in this course will include:
● The ability to reliably reproduce published research
● The ability to adapt and apply existing bioinformatics methods to new data
● Use of advanced programming/coding across multiple programming languages (Linux, Bash,
     R, Shell, Git, Java, Python, Perl) to analyse  and interpret biological datasets
● The development and application of simulated datasets to answer questions in biology.

Through the practice and implementation of the above skillset, students will gain a strong proficiency in, and fundamental understanding of, the use, management, integration and interpretation of biological data.

Learning Outcomes

As a student in this course, I will develop the ability to:
Learning Outcome Number 1
LO1
Demonstrate an understanding of biological data analysis across multiple programming languages, and demonstrate the ability to apply these methods to different types of biological data, to derive biologically meaningful conclusions from the data
(Assessment: written assessments final exam)
Related Graduate Attributes and Kaupapa: Critically competent in the core academic discipline, Biculturally Competent and Confident (kaupapa 1,3,4,5,7), Employable, innovative and enterprising, Globally aware
Learning Outcome Number 2
LO2
Demonstrate an ability to edit and adapt existing bioinformatics analysis methods and pipelines to new datasets
(Assessment: written assessments)
Related Graduate Attributes and Kaupapa: Critically competent in the core academic discipline, Biculturally Competent and Confident (kaupapa 3,4,5,7), Employable, innovative and enterprising, Globally aware
Learning Outcome Number 3
LO3
Show competency in the fitting of appropriate statistical tests to data outputs from LO1
(Assessment: written assessments)
Related Graduate Attributes and Kaupapa: Critically competent in the core academic discipline, Biculturally Competent and Confident (kaupapa 3,4,5,7), Employable, innovative and enterprising
Learning Outcome Number 4
LO4
Demonstrate understanding of the characteristics and limitations of these methods
(Assessment: written assessments final exam)
Related Graduate Attributes and Kaupapa: Critically competent in the core academic discipline, Employable, innovative and enterprising, Globally aware
Learning Outcome Number 5
LO5
Demonstrate an understanding of the importance of biological data ownership and governance, as applied to ethical, social and political issues
(Assessment: written assessments final exam)
 Related Graduate Attributes and Kaupapa: Critically competent in the core academic discipline, Biculturally Competent and Confident (kaupapa 3,4,5,6,7), Employable, innovative and enterprising, Globally aware

Transferable Skills / Pūkenga Ngaio
As a student in this course, I will develop the following skills:
 Synthesising information. In everyday life and in many job situations you will be required to read information from different sources, construct your own understanding and shape your own viewpoint. In lectures and labs we will discuss recent research papers in a group environment and this will develop your abilities to identify the essential elements of research outputs - you will then use in report writing. (Graduate Attribute 2: Employable, Innovative and Enterprising)
 Generating data. Important for research and in governmental and non-governmental organizations. We will conduct research activities to provide both the real-world context for lectures and to develop hands-on skills in data generation, manipulation and interpretation. (Graduate Attribute 2: Employable, Innovative and Enterprising)
 Analysing data. Important for research, as well as in a number of private-sector organizations. This skill will be further developed when you analyse and present the data you generate in the labs. (Graduate Attribute 2: Employable, Innovative and Enterprising)
 Writing a report on findings. Clear written communication is essential for most professional careers. We will provide instruction on the elements of successful reports and help you identify these elements with clear marking rubrics through peer and self-assessment. (Graduate Attribute 2: Employable, Innovative and Enterprising)

Prerequisites

Timetable 2023

Students must attend one activity from each section.

Computer Lab A
Activity Day Time Location Weeks
01 Monday 12:00 - 15:00 Rehua 008 Computer Lab
20 Feb - 2 Apr
24 Apr - 4 Jun

Timetable Note

Lectures/tutorials / Akoranga

There will be a total of 36 contact hours in this course, consisting of lectures and labs.  Due to the practical nature of this course, there will be 12 x 3hr sessions, where the first hour is a lecture and the remaining two hours are a computer lab. Each session will be a mix of instruction, question time, and practical application of the content. This format has been devised to allow for in-depth group discussions on analysis methods, and to facilitate peer-assisted learning. Topics covered in this course will include:

● Biological data sovereignty, ownership and governance, as applied to cultural, ethical, social and political issues
● Whole genome sequencing, assembly, annotation and analysis (de novo/shotgun and reference-aligned, bacterial genome)
● Single nucleotide polymorphisms, genotyping-by-sequencing and microarrays (human, animal)
● Transcriptomics/RNA sequencing/differential gene expression analysis (human, animal or bacterial)
● DNA methylation/post-translational modification analysis (human, animal)
● ChIPseq/HiC (histone modifications and genome structure and conformation, human and animal)


Datasets will focus on integrating information and analyses across multiple environments and platforms.  Datasets used in this course will be from medical/health, ecological, microbiological, conservation, phylogenetic and evolution projects, to highlight breadth of applicability of knowledge.

Our teaching philosophy is that students need to be actively engaged in learning – it is important that you do more than simply turn up to class and receive instruction from us. You will be given clear instructions on what preparation is expected before each class – please come to class prepared to make best use of the time. [Students should note that in the Science Faculty that the average student is responsible for 10 hours of study per credit point – this equates to approximately 3 hours of additional study for each hour of class contact at the 300-level].

Assessment

Assessment Due Date Percentage  Description
Written assessments 60% 6 assessments - 10% each
Final Exam 40% 2 hours

Textbooks / Resources

There is no required text. During the course, you will be directed to various books and to primary scientific papers through LEARN | Ako, including resources used or referred to in lectures. This allows us to include in this course the most current scientific knowledge available, and to provide greater breadth than would be found in a single textbook. To do well in final exam you must show evidence that you have read and understood this material.

Indicative Fees

Domestic fee $951.00

International fee $4,750.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see School of Biological Sciences .

All BIOL337 Occurrences

  • BIOL337-23S1 (C) Semester One 2023