MODELS FOR BIOINFORMATICS
Credit points: 15
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.
SchoolEngineering and Mathematical Sciences
Subject Co-ordinatorAgus Salim
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 5 - Masters
Available as ElectiveNo
Prerequisites Must be admitted in the Master of Data Science (SMDS) and have passed STM4PSD or both STA4SS and STM4PM Other students require Coordinators Approval
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Minimum credit point requirementN/A
Online learning materials
PublisherLa Trobe Univ
R programming for Bioinformatics
Statistics in Human Genetics and Molecular Biology
Self sourced or Uni sourcedN/A
Entire subject or partial subjectN/A
Total hours/days requiredN/A
Location of WBL activity (region)N/A
WBL addtional requirementsN/A
Graduate capabilities & intended learning outcomes
Intended Learning Outcomes
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Melbourne (Bundoora), 2021, Semester 2, Blended
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorAgus Salim
Computer LaboratoryWeek: 30 - 42
One 2.00 h computer laboratory every two weeks on weekdays during the day from week 30 to week 42 and delivered via face-to-face.
Unscheduled Online ClassWeek: 30 - 42
One 2.00 h unscheduled online class per week on any day including weekend during the day from week 30 to week 42 and delivered via online.
Lectures in pre-recorded online video format
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.
|N/A||N/A||No||10||SILO1, SILO2, SILO3|
Assignment (Equivalent to 1000 words)1 assignment, submitted online. Assignment will involve significant use of real-life data.
|N/A||N/A||No||10||SILO1, SILO2, SILO3, SILO4|
Written project (Equivalent to 2500 words)The project will require significant use of R programming skills and may require novel approach to problem-solving.
|N/A||N/A||No||25||SILO1, SILO2, SILO3, SILO4, SILO5|
2 Hour Exam(1 hour theory and 1 hour practical). The practical part will require students to solve exam questions using R.
|N/A||N/A||No||55||SILO1, SILO2, SILO3, SILO4, SILO5|