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Unit outline_

OLET5602: Computational Analysis for omics Data

Intensive July - August, 2021 [Block mode] - Camperdown/Darlington, Sydney

Molecular and systems biology have become data-intensive sciences owing to the fast-growing omics technologies that enable the profiling of genome, epigenome, transcriptome, and proteome at full scale and, increasingly, at the single-cell level. Computational and statistical methodologies are now indispensable for analysing omics data generated from high-throughput technologies. This unit will introduce you to commonly used computational and statistical methods in omics data analysis. You are encouraged to use your own data to construct the models to visualise your research and interpret results. Learning the correct use of computational methods for various omics data analysis applications including your own data, you will develop an essential knowledge of methods and techniques in analysing omics data. This will provide a strong foundation for using computational approaches in omics-based molecular and systems biology research.

Unit details and rules

Academic unit Mathematics and Statistics Academic Operations
Credit points 2
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

Experience with at least one programming language. Basic computational and statistical concepts. Basic knowledge of molecular biology.

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Pengyi Yang, pengyi.yang@sydney.edu.au
Type Description Weight Due Length
Presentation group assignment Group presentation
A group video presentation
30% -
Due date: 30 Aug 2020 at 23:00
10 minute presentation
Outcomes assessed: LO1 LO2 LO3
Tutorial quiz Quiz 1
10 quizzes
6% Week 01
Due date: 26 Jul 2020 at 23:00
30 mins
Outcomes assessed: LO1
Tutorial quiz Quiz 2
10 quizzes
6% Week 02
Due date: 02 Aug 2020 at 23:00
30 mins
Outcomes assessed: LO2
Tutorial quiz Quiz 3
10 quizzes
6% Week 03
Due date: 09 Aug 2020 at 23:00
30 mins
Outcomes assessed: LO2
Tutorial quiz Quiz 4
10 quizzes
6% Week 04
Due date: 16 Aug 2020 at 23:00
30 mins
Outcomes assessed: LO1 LO3 LO2
Tutorial quiz Quiz 5
10 quizzes
6% Week 05
Due date: 23 Aug 2020 at 23:00
30 mins
Outcomes assessed: LO1 LO3 LO2
Assignment Individual report
An individual report assessment
30% Week 06
Due date: 30 Aug 2020 at 23:00
10 pages
Outcomes assessed: LO1 LO2 LO3
Participation Peer-review reports
Peer-review report of assessments
10% Week 07
Due date: 06 Sep 2020 at 23:00
2-3 pages
Outcomes assessed: LO1 LO3 LO2
group assignment = group assignment ?

Assessment summary

For OLEO5601: 3 x online quizzes; For OLET5602: 3 x online quizzes (module 1 quiz is formative and modules 2 and 3 each worth 12.5%); 2 x online quiz (20%); presentation (20%), individual report (25%), peer-review reports (10%).

Assessment criteria

Result name Mark range Description
High distinction 85 - 100 Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction 75 - 84 Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit 65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail 0 - 49 When you don’t meet the learning outcomes of the unit to a satisfactory standard.
Absent fail

0 - 49

When you haven’t completed all assessment tasks or met the attendance requirements.
Cancelled No mark When your enrolment has been cancelled.
Discontinued not to count as failure No mark When you discontinue a unit after the relevant census date but before the DC deadline.

Discontinue – fail

No mark When you discontinue a unit after the DC deadline but before the DF deadline
Failed requirements No mark When you don’t meet the learning outcomes to a satisfactory standard, for units which are marked as either Satisfied requirements or Failed requirements.
Satisfied requirements No mark When you meet the learning outcomes to a satisfactory standard, for units which are marked as either Satisfied requirements or Failed requirements.
Withdrawn No mark

When you discontinue a unit before the relevant census date. WD grades do not appear on your academic transcript.

 

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Key concepts of omics and systems biology Independent study (6 hr) LO1
Week 02 Basics of computational and statistical methods Independent study (6 hr) LO2
Week 03 Exploring omics data using R and R markdown Independent study (6 hr) LO1 LO2
Week 04 Clustering and its application in omics Independent study (6 hr) LO2 LO3
Week 05 Classification and its application in omics Independent study (6 hr) LO2 LO3
Week 06 Presentation Presentation (5 hr) LO1 LO2 LO3
Week 07 Individual report Project (5 hr) LO1 LO2 LO3

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Understand the key concepts in two foundation areas of systems biology – computational data analytics, and systems biology and omics sciences.
  • LO2. Identify and match different computational methods used for various omics data analysis, and apply and evaluate them for omics data analysis.
  • LO3. Find and analyse information and judge its reliability and significance.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

This is the first time this unit has been offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.