MODELS FOR BIOINFORMATICS

STA5MB

2018

Credit points: 15

This subject addresses La Trobe's Innovation and Entrepreneurship Essential. Innovation and Entrepreneurship is about using your creativity to generate new ideas, understand and solve complex problems and thrive in a fast-changing world.

Subject outline

The advance in omics technology have seen an exponential increase in the volume of biological data in the last ten years. Statistical models play important roles in drawing conclusions from and making sense of the complex and often noisy omics data. This subject will introduce students to statistical issues and potential solutions to problems commonly encountered at various stage of omics data analysis, from data acquisition, alignment, quality controls, data analysis, visualization and interpretation. Topics covered will include introduction to next-generation sequencing and microarray technologies, batch effects and other unwanted variations, multiple hypothesis testing problems, statistical tests and models for high-dimensional data, data visualization and utilizing biological database via pathway-based analysis.  Students will also be introduced to intermediate level of R programming language, including writing customized scripts and functions, developing R packages and working with 'pipe' operator. Bioconductor packages (www.bioconductor.org) and other freely-available Bioinformatics software will be used for all Lab sessions.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorAgus Salim

Available to Study Abroad StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsYes

Subject particulars

Subject rules

Prerequisites Must be enrolled in the Master of Data Science (SMDS) and have passed STM4PM. Other students require Coordinators Approval.

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Special conditionsN/A

Readings

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsStatistics in Human Genetics and Molecular BiologyRecommendedCavan Reilly. 2009CRC Press
ReadingsR programming for BioinformaticsRecommendedRobert Gentleman. 2009CRC Press
ReadingsOnline learning materialsPrescribedAgus Salim. 2017La Trobe Univ

Graduate capabilities & intended learning outcomes

01. Demonstrate specialised theoretical and technical skills in solving statistical issues in bioinformatics

Activities:
Modeled via concepts, illustrations and examples in online lectures and reading materials. Problems in computer labs will require problem-solving skills based on realistic datasets
Related graduate capabilities and elements:
Literacies and Communication Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Personal and Professional Skills
Personal and Professional Skills
Discipline -Specific Knowledge and Skills

02. Use specialised cognitive and technical skills to critically analyse, reflect on and synthesise complex information, problems, concepts and theories relevant to solving statistical issues in bioinformatics

Activities:
Modeled via concepts, illustrations and examples in online lectures and reading materials. Problems in computer labs will require problem-solving skills based on realistic datasets
Related graduate capabilities and elements:
Literacies and Communication Skills
Literacies and Communication Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Personal and Professional Skills
Discipline -Specific Knowledge and Skills

03. Apply established theories relevant to statistical issues in bioinformatics

Activities:
Modeled via concepts, illustrations and examples in online lectures and reading materials. Some questions in computer labs will require students apply their statistical theory knowledge to solve real-life problems in bioinformatics
Related graduate capabilities and elements:
Discipline -Specific Knowledge and Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Literacies and Communication Skills

04. Use advanced communication skills to transmit knowledge and ideas of the role of statistics in bioinformatics to others

Activities:
Modeled via illustrations and examples in online lectures and reading materials. Examples on effective communications of statistical concepts to non-statisticians will be provided
Related graduate capabilities and elements:
Literacies and Communication Skills
Literacies and Communication Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Discipline -Specific Knowledge and Skills

05. Demonstrate autonomy, expert judgement, adaptability and responsibility as an applied statistician

Activities:
Students solving real-life problems in computer labs independently with inputs from tutor/lecturer. The problems will require students to exercise their statistical judgement and adapt existing methods independently
Related graduate capabilities and elements:
Literacies and Communication Skills
Literacies and Communication Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Inquiry and Analytical Skills
Personal and Professional Skills
Personal and Professional Skills
Discipline -Specific Knowledge and Skills

Subject options

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Start date between: and    Key dates

Melbourne, 2018, Semester 2, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorAgus Salim

Class requirements

Unscheduled Online Class Week: 31 - 43
One 2.0 hours unscheduled online class per week on any day including weekend from week 31 to week 43 and delivered via online.
"Lectures in pre-recorded online video format"

Computer Laboratory Week: 31 - 43
One 2.0 hours computer laboratory every two weeks on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Assessments

Assessment elementComments% ILO*
4 Online quizzes (Each quiz equivalent 250 words)There are 4 short quizzes throughout the semester. Students can attempt each quiz a maximum of three times and the best mark for that quiz taken. Randomly assigned questions for each quiz instance.10 01, 02, 03
Assignment (Equivalent to 1000 words)1 assignment, submitted online. Assignment will involve significant use of real-life data.10 01, 02, 03, 04
Written project (Equivalent to 2500 words)The project will require significant use of R programming skills and may require novel approach to problem-solving.25 01, 02, 03, 04, 05
2 Hour Exam(1 hour theory and 1 hour practical). The practical part will require students to solve exam questions using R.55 01, 02, 03, 04, 05