Overview
An introduction to practical hands-on learning of computer literacy and programming skills needed in the clinical environment together with the theoretical background.
This paper builds on topics covered in GEHM 702 and is designed to meet the needs of clinical professionals, health scientists and researchers who want to develop knowledge and skills in the analysis and interpretation of Clinical Genomic and Epigenomic datasets.
About this paper
| Paper title | Introduction to Clinical Bioinformatics |
|---|---|
| Subject | Bioinformatics |
| EFTS | 0.1250 |
| Points | 15 points |
| Teaching period | Semester 1 (Distance learning) |
| Delivery mode | The Distance Learning offering of this paper is taught and assessed remotely |
| Domestic Tuition Fees ( NZD ) | $1,743.38 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Eligibility
This is a postgraduate (level 8) paper. Clinical experience/prior bioinformatics experience is not required.
- Contact
- Teaching staff
Convenor: Associate Professor Aniruddha Chatterjee
Gregory Gimenez, Dr Peter Stockwell and Dr Euan Rodger
- Teaching Arrangements
This Distance Learning paper is taught remotely.
- Textbooks
No compulsory text books. The paper will include research informed teaching.
- Graduate Attributes Emphasised
- Global perspective, Lifelong learning, Communication, Critical thinking, Cultural understanding, Ethics, Environmental literacy, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete this paper will,
- Acquire new skills to perform basic genomic and epigenomic data analyses that focus on health and clinical environment
- Gain knowledge and be empowered to understand, interpret and perform analysis on multi-omic clinical data.
- Demonstrate a critical sense relating to analytical tools (R and web-based) used and an awareness of their advantages and limitations for biomedical and clinical application
- Gain critical sense on experimental design and power analysis to conduct robust and reproducible analyses
- Apply critical reflection to understanding and addressing emerging challenges and opportunities that relate to Big Data.
Overview
An introduction to practical hands-on learning of computer literacy and programming skills needed in the clinical environment together with the theoretical background.
This paper builds on topics covered in GEHM 702 and is designed to meet the needs of clinical professionals, health scientists and researchers who want to develop knowledge and skills in the analysis and interpretation of Clinical Genomic and Epigenomic datasets.
About this paper
| Paper title | Introduction to Clinical Bioinformatics |
|---|---|
| Subject | Bioinformatics |
| EFTS | 0.1250 |
| Points | 15 points |
| Teaching period | Not offered in 2027 (Distance learning) |
| Domestic Tuition Fees | Tuition Fees for 2027 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Eligibility
This is a postgraduate (level 8) paper. Clinical experience/prior bioinformatics experience is not required.
- Contact
- Teaching staff
Convener: Associate Professor Aniruddha Chatterjee
Gregory Gimenez, Dr Peter Stockwell and Dr Euan Rodger
- Teaching Arrangements
This distance learning paper is taught remotely.
- Textbooks
No compulsory text books. The paper will include research informed teaching.
- Graduate Attributes Emphasised
- Global perspective, Lifelong learning, Communication, Critical thinking, Cultural understanding, Ethics, Environmental literacy, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
Students who successfully complete this paper will:
- Acquire new skills to perform basic genomic and epigenomic data analyses that focus on health and clinical environment.
- Gain knowledge and be empowered to understand, interpret and perform analysis on multi-omic clinical data.
- Demonstrate a critical sense relating to analytical tools (R and web-based) used and an awareness of their advantages and limitations for biomedical and clinical application.
- Gain critical sense on experimental design and power analysis to conduct robust and reproducible analyses.
- Apply critical reflection to understanding and addressing emerging challenges and opportunities that relate to Big Data.