sta5mb models for bioinformatics

MODELS FOR BIOINFORMATICS

STA5MB

2020

Credit points: 15

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.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorAgus Salim

Available to Study Abroad/Exchange StudentsYes

Subject year levelYear Level 5 - Masters

Exchange StudentsNo

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

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

Co-requisitesN/A

Incompatible subjectsN/A

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Learning resources

Statistics in Human Genetics and Molecular Biology

Resource TypeBook

Resource RequirementRecommended

AuthorCavan Reilly.

Year2009

Edition/VolumeN/A

PublisherCRC Press

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

R programming for Bioinformatics

Resource TypeBook

Resource RequirementRecommended

AuthorRobert Gentleman.

Year2009

Edition/VolumeN/A

PublisherCRC Press

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Online learning materials

Resource TypeWeb resource

Resource RequirementPrescribed

AuthorAgus Salim.

Year2017

Edition/VolumeN/A

PublisherLa Trobe Univ

ISBNN/A

Chapter/article titleN/A

Chapter/issueN/A

URLN/A

Other descriptionN/A

Source locationN/A

Career Ready

Career-focusedNo

Work-based learningNo

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

Graduate Capabilities

INQUIRY AND ANALYSIS - Creativity and Innovation

INQUIRY AND ANALYSIS - Critical Thinking and Problem Solving

INQUIRY AND ANALYSIS - Research and Evidence-Based Inquiry

PERSONAL AND PROFESSIONAL - Adaptability and Self-Management

PERSONAL AND PROFESSIONAL - Leadership and Teamwork

Intended Learning Outcomes

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

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

03. Apply established theories relevant to statistical issues in bioinformatics

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

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

Subject options

Select to view your study options…

Melbourne (Bundoora), 2020, Semester 2, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment informationN/A

Subject Instance Co-ordinatorAgus Salim

Class requirements

Computer LaboratoryWeek: 31 - 43
One 2.00 hours computer laboratory every two weeks on weekdays during the day from week 31 to week 43 and delivered via face-to-face.

Unscheduled Online ClassWeek: 31 - 43
One 2.00 hours unscheduled online class per week on any day including weekend during the day from week 31 to week 43 and delivered via online.
Lectures in pre-recorded online video format

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	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.	N/A	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	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	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	N/A	No	55	SILO1, SILO2, SILO3, SILO4, SILO5