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CBMS793 – Research Topic: Advanced Biomolecular Analysis

2017 – S2 Day

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Unit Convenor
Mark Molloy
Contact via mark.molloy@mq.edu.au
E8C311
By appointment
Lecturer
Martin Ostrowski
Lecturer
Matthew McKay
Credit points Credit points
4
Prerequisites Prerequisites
Admission to MRes
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description
This unit addresses some advanced methods of analysis utilised in the biomolecular sciences. Biomolecular sciences spans the study of individual molecular structures and biochemical reactions to also encompass the 'omics' sciences of genomics, proteomics, metabolomics and glycomics. These sciences all generate large and complex datasets that require specialized software and methods to assemble and analyse. The analyses are challenging, as they not only require a good knowledge of biochemistry, molecular biology, and cell and developmental biology, but also an understanding of limitations of both the software and the data quality. This unit will provided a background to the data acquisition methods, quality control of the datasets, and analysis methods within a number of these areas. Most importantly it will provide hands-on experience in the analysis of real large-scale datasets and the correct use of appropriate analysis tools.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at http://students.mq.edu.au/student_admin/enrolmentguide/academicdates/

Learning Outcomes

  1. Demonstrate knowledge of appropriate techniques used in acquiring large biomolecular datasets and the limitations of the use of these methods
  2. Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  3. Process datasets using specific software, providing a broad overview of data in terms of size, quality and utility for further analysis
  4. Analyze large datasets and compare it with established information about the system under investigation
  5. Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

General Assessment Information

  • All written work must be submitted via Turnitin on the CBMS793 iLearn website, or as otherwise advised by the unit convenor.
  • Late submissions will be penalised with 20% loss of the maximum mark for each day past the deadline.  More than 2 days late will result in zero marks awarded unless Disruption to Studies application has been requested.
  • If there is any medical reason why you cannot submit work on time you should lodge a Disruption of Studies application, otherwise your mark will be penalized for lateness.

Assessment Tasks

Name Weighting Hurdle Due
Take home examination 10% 25th August
Proteomics Data AnalysisReport 25% 6th October
Genomics quiz 10% 13th Oct
Genomics Data Presentation 25% 3rd November
Final Examination 30% Examination Period

Take home examination

Due: 25th August
Weighting: 10%

Exam assessing some of the fundamental principles and concepts important in the application of protein mass spectrometry and proteomics, including peptide fragmentation, targeted mass spectrometry and principles of experimental design Content from Week 1-3 will be assessed in the form of short answer questions. There will be 5 questions of equal weighting.


This Assessment Task relates to the following Learning Outcomes:
  • Demonstrate knowledge of appropriate techniques used in acquiring large biomolecular datasets and the limitations of the use of these methods
  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis

Proteomics Data AnalysisReport

Due: 6th October
Weighting: 25%

Students will be given a dataset containing information from a large scale quantitation LC/MS experiment. The dataset needs to be evaluated using Perseus, and a report containing information about quantitative changes presented. The report should present visual statistical analysis providing an overview of the experiment and illustrating differences in samples (e.g. Hierarchical Clustering, Principle-Component-Analysis), and an evaluation of key proteins and the pathways they are associated with. The report should contain sufficient methodological detail appropriate for publication purposes in quality proteomics journals.

 


This Assessment Task relates to the following Learning Outcomes:
  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  • Process datasets using specific software, providing a broad overview of data in terms of size, quality and utility for further analysis
  • Analyze large datasets and compare it with established information about the system under investigation
  • Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

Genomics quiz

Due: 13th Oct
Weighting: 10%

Students will be given raw paired-end next generation sequencing data equivalent to ~50kb of an assembled genome. Students will be tasked with assembling the data following the necessary pre-processing steps, and then annotating the genes and features on the assembled DNA using tools covered in the preceding sessions. The task will be assessed by 1. producing a fasta file of assembled dna contigs, 2. a file of gene/ORF annotations, 3. filling in the summary table (provided) and answering 3 short questions about their data.

 


This Assessment Task relates to the following Learning Outcomes:
  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  • Process datasets using specific software, providing a broad overview of data in terms of size, quality and utility for further analysis
  • Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

Genomics Data Presentation

Due: 3rd November
Weighting: 25%

Students to be given two genome datasets. These are to be analysed following guidelines presented in the tutorials. The outcomes of these results are to be given as an Oral Presentation of 12min plus 3min question time.


This Assessment Task relates to the following Learning Outcomes:
  • Demonstrate knowledge of appropriate techniques used in acquiring large biomolecular datasets and the limitations of the use of these methods
  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  • Process datasets using specific software, providing a broad overview of data in terms of size, quality and utility for further analysis
  • Analyze large datasets and compare it with established information about the system under investigation
  • Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

Final Examination

Due: Examination Period
Weighting: 30%

This will be a 2h exam consisting of a series of problem solving, data interpretation questions and short essays.


This Assessment Task relates to the following Learning Outcomes:
  • Demonstrate knowledge of appropriate techniques used in acquiring large biomolecular datasets and the limitations of the use of these methods
  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  • Analyze large datasets and compare it with established information about the system under investigation
  • Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

Delivery and Resources

 

This unit uses team-based teaching in the form of 'lectorials' that encompass both lectures and hands-on experiences in using various data analysis software programs and tools. The tutors are actively involved in research activities to bring knowledge from real-world experiences in their respective fields. All class sessions take place on Friday 9am-1pm in F7B433 unless otherwise instructed. Tutorials will NOT be recorded. You must attend these tutorials to gain practical experience with data analysis. As some of the assessment is based on your practical use of specific software it is essential that you attend these classes.

It is expected that each student will bring to class a laptop PC computer to install data analysis software, or prior arrangements must be made with the convenor.

Software Requirements

Software used in this module can be installed in weeks 1 or 2 and will be available for use during the software sessions starting week 3.

All official correspondence with lecturers and tutors is to be made using the CBMS793 iLearn website.

Unit Schedule

Week

Date

Title

Lecturer

Proteomics

1

 4 Aug

Unit overview

 

Introduction to protein mass spectrometry (detecting ions - instruments, scan   types, peptide fragmentation and common proteomic workflows)

- Introduction to quantitative mass spectrometry techniques

MM

 

MMcKay

2

11 Aug

Principles of protein identification: peptide fragmentation and the automation of spectral assignment

- Targeted Mass Spectrometry (From SRM to DIA: Using Area Under the Curve (AUC) approaches for quantitation via SWATH)

MMcKay

3

 18 Aug

Principles of experiment design and statistical analysis in proteomics

Quantitation of Large DIA Proteomic Datasets

 

MM

MMcKay

 

4

 25 Aug

Tools for the quantitation of peptides and proteins

PeakView and Perseus

MM

MMcKay

 

5

 1 Sept

 

Combining experimental design with statistical power in DIA experiments

 

MM

McKay

6

 8 Sept

DIA data analysis

MM

McKay

7

 15 Sept

Pathway mapping and data mining

MM

Mid-Semester Break 18 Sept-29 Sept

Genomics

8

 6 Oct

Genomics Introduction

 

Introduction to the unix/linux command line and useful tools

IP, MO

9

 13 Oct

Introduction to sequencing data quality control, assembly and annotation

 

Genomics Quiz

MO

10

 20 Oct

Metagenomic analysis 1

 

Introduction to R 

MO

11

 27 Oct

Metagenomic analysis 2

 

Transcriptome analysis tools

MO

12

 3 Nov

Genomics - Oral Presentations

MO

13

10 Nov

Revision

MM

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central. Students should be aware of the following policies in particular with regard to Learning and Teaching:

Academic Honesty Policy http://mq.edu.au/policy/docs/academic_honesty/policy.html

Assessment Policy http://mq.edu.au/policy/docs/assessment/policy_2016.html

Grade Appeal Policy http://mq.edu.au/policy/docs/gradeappeal/policy.html

Complaint Management Procedure for Students and Members of the Public http://www.mq.edu.au/policy/docs/complaint_management/procedure.html​

Disruption to Studies Policy (in effect until Dec 4th, 2017): http://www.mq.edu.au/policy/docs/disruption_studies/policy.html

Special Consideration Policy (in effect from Dec 4th, 2017): https://staff.mq.edu.au/work/strategy-planning-and-governance/university-policies-and-procedures/policies/special-consideration

In addition, a number of other policies can be found in the Learning and Teaching Category of Policy Central.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/support/student_conduct/

Results

Results shown in iLearn, or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Learning Skills

Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to improve your marks and take control of your study.

Student Enquiry Service

For all student enquiries, visit Student Connect at ask.mq.edu.au

Equity Support

Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Graduate Capabilities

PG - Research and Problem Solving Capability

Our postgraduates will be capable of systematic enquiry; able to use research skills to create new knowledge that can be applied to real world issues, or contribute to a field of study or practice to enhance society. They will be capable of creative questioning, problem finding and problem solving.

This graduate capability is supported by:

Learning outcomes

  • Process datasets using specific software, providing a broad overview of data in terms of size, quality and utility for further analysis
  • Analyze large datasets and compare it with established information about the system under investigation

Assessment tasks

  • Proteomics Data AnalysisReport
  • Genomics quiz
  • Genomics Data Presentation
  • Final Examination

PG - Capable of Professional and Personal Judgment and Initiative

Our postgraduates will demonstrate a high standard of discernment and common sense in their professional and personal judgment. They will have the ability to make informed choices and decisions that reflect both the nature of their professional work and their personal perspectives.

This graduate capability is supported by:

Learning outcomes

  • Demonstrate knowledge of appropriate techniques used in acquiring large biomolecular datasets and the limitations of the use of these methods
  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  • Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

Assessment tasks

  • Take home examination
  • Proteomics Data AnalysisReport
  • Genomics quiz
  • Genomics Data Presentation
  • Final Examination

PG - Discipline Knowledge and Skills

Our postgraduates will be able to demonstrate a significantly enhanced depth and breadth of knowledge, scholarly understanding, and specific subject content knowledge in their chosen fields.

This graduate capability is supported by:

Learning outcomes

  • Demonstrate knowledge of appropriate techniques used in acquiring large biomolecular datasets and the limitations of the use of these methods
  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  • Process datasets using specific software, providing a broad overview of data in terms of size, quality and utility for further analysis
  • Analyze large datasets and compare it with established information about the system under investigation

Assessment tasks

  • Take home examination
  • Proteomics Data AnalysisReport
  • Genomics quiz
  • Genomics Data Presentation
  • Final Examination

PG - Critical, Analytical and Integrative Thinking

Our postgraduates will be capable of utilising and reflecting on prior knowledge and experience, of applying higher level critical thinking skills, and of integrating and synthesising learning and knowledge from a range of sources and environments. A characteristic of this form of thinking is the generation of new, professionally oriented knowledge through personal or group-based critique of practice and theory.

This graduate capability is supported by:

Learning outcomes

  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  • Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

Assessment tasks

  • Take home examination
  • Proteomics Data AnalysisReport
  • Genomics quiz
  • Genomics Data Presentation
  • Final Examination

PG - Effective Communication

Our postgraduates will be able to communicate effectively and convey their views to different social, cultural, and professional audiences. They will be able to use a variety of technologically supported media to communicate with empathy using a range of written, spoken or visual formats.

This graduate capability is supported by:

Learning outcomes

  • Demonstrate knowledge of appropriate techniques used in acquiring large biomolecular datasets and the limitations of the use of these methods
  • Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

Assessment tasks

  • Take home examination
  • Proteomics Data AnalysisReport
  • Genomics quiz
  • Genomics Data Presentation
  • Final Examination

PG - Engaged and Responsible, Active and Ethical Citizens

Our postgraduates will be ethically aware and capable of confident transformative action in relation to their professional responsibilities and the wider community. They will have a sense of connectedness with others and country and have a sense of mutual obligation. They will be able to appreciate the impact of their professional roles for social justice and inclusion related to national and global issues

This graduate capability is supported by:

Learning outcomes

  • Demonstrate knowledge of appropriate techniques used in acquiring large biomolecular datasets and the limitations of the use of these methods
  • Demonstrate understanding of experiment design and ability to critically assess the quality of large biomolecular datasets prior to in-depth analysis
  • Demonstrate ability to effectively report, communicate and draw new conclusions about a biomolecular system from large analytical datasets.

Assessment tasks

  • Take home examination
  • Proteomics Data AnalysisReport
  • Genomics quiz
  • Genomics Data Presentation
  • Final Examination

Changes from Previous Offering

No changes from the unit offered in S2, 2016