BRM5012 Techniques in medical biotechnology: Genomics, proteomics and bioinformatics
The Complete Study & Assessment Guide · S2 2026BRM5012 Overview
- Monash University
- Semester 2 2026
- Postgraduate
- Hands-on bioinformatics
BRM5012 is a hands-on Monash University unit in genomics, transcriptomics, proteomics and bioinformatics. It trains you to move from a biomedical research question to a defensible analysis of high-throughput data.
- Assessed by Written work, a bioinformatics presentation and applied exercises
- Core skill Turn software output into a bounded biological claim
- Hardest step Separate biological structure from technical variation
- Best preparation Rebuild workflows and defend every analytical decision
What you get in the full BRM5012 Study & Assessment Guide
- 10 chapters, 43 pages — every assessed topic
- 26 original practice questions, each with a model answer
- 10 worked examples, solved the way BRM5012 marks
- 14 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the 3 - Exercise (55%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How BRM5012 is assessed
| Component | Weight | Format |
|---|---|---|
| 1 - Written | 20% | Written analysis aligned with the published unit learning outcomes. |
| 2 - Presentation | 25% | Final bioinformatics presentation. The captured Moodle page lists Tuesday 10 November 2026 at 9:00 AM. |
| 3 - Exercise | 55% | Applied exercises and workshop performance across the teaching period. |
What other students are asking about BRM5012
- Where can I find lecture notes or study guides for BRM5012 at Monash?
- The BRM5012 study guide in AskSia Library covers Techniques in medical biotechnology: Genomics, proteomics and bioinformatics in 10 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in BRM5012?
- No final exam is listed in the offering this AskSia Library guide is built from; BRM5012 is assessed by 1 - Written 20%, 2 - Presentation 25%, 3 - Exercise 55%.
- What does BRM5012 cover on sequencing libraries and fastq data?
- In AskSia Library's BRM5012 guide, the Sequencing Libraries and FASTQ Data chapter explains library preparation, cluster generation, read layout, FASTQ anatomy, quality encoding and method choice.
- What does BRM5012 cover on rna-seq quality control?
- In AskSia Library's BRM5012 guide, the RNA-seq Quality Control chapter explains normalised count distributions, sample relationships, metadata, outlier investigation and suitability decisions.
- What does BRM5012 cover on differential expression analysis?
- In AskSia Library's BRM5012 guide, the Differential Expression Analysis chapter explains comparison direction, log fold change, adjusted evidence, volcano plots, mean difference plots and bounded claims.
Current BRM5012 dates
| Date | Item | Control |
|---|---|---|
| Tuesday, 10 November 2026, 9:00 AM | Final bioinformatics presentation | The captured Moodle assessment page lists this date and time for the 25% presentation. |
Dates are as published in the Semester 2 2026 Moodle assessment page. Confirm exact deadlines and submission settings in the live LMS.
What BRM5012 covers
Move from experimental design and sequencing data through bulk and single cell analysis to biomarker reasoning, integrated evidence and clear scientific communication.
Experimental Design for Sequencing Studies
Research questions, biological units, replication, controls, batch structure, library choice and defensible scope.Experimental design determines whether sequencing data can answer the biomedical question.
- Research question and measured outcome
- Biological units and replication
- Treatment and control structure
- Randomisation and blocking
- Batch variables and confounding
- Library choice and analysis scope
Sequencing Libraries and FASTQ Data
Library preparation, cluster generation, read layout, FASTQ anatomy, quality encoding and method choice.Sequencing data are shaped by library preparation, template binding, cluster amplification and repeated base-calling cycles.
- Library preparation and adapters
- Flow-cell binding and clusters
- Sequencing cycles and reads
- FASTQ four-line anatomy
- Single-end and paired-end layout
- Short reads and long reads
RNA-seq Quality Control
Normalised count distributions, sample relationships, metadata, outlier investigation and suitability decisions.RNA-seq quality control combines expression distributions, sample relationships and metadata to decide whether a planned comparison is credible.
- Normalised-count distributions
- Sample relationship maps
- Replicate coherence
- Outlier investigation
- Batch metadata overlays
- Suitability decisions
Differential Expression Analysis
Comparison direction, log fold change, adjusted evidence, volcano plots, mean difference plots and bounded claims.Differential expression analysis estimates how gene expression changes between named conditions while accounting for variability and many simultaneous tests.
- Named comparison direction
- Log2 fold change
- Conventional fold change
- Average expression
- Adjusted p-values
- Volcano plots
Functional Enrichment and Ranked Gene Sets
Over-representation analysis, ranked lists, Wald statistics, gene set enrichment, backgrounds and robustness.Functional enrichment organises long differential-expression outputs into testable biological hypotheses. This chapter contrasts over-representation analysis of a thresholded gene list with gene set enrichment analysis of a complete ranking.
- Selected lists for ORA
- Background gene universe
- Directional subsets
- Full rankings for GSEA
- Wald statistic
- Leading genes
Biomarkers, Drivers and Evidence
Association, causality, candidate criteria, replication, detectability, multi-omics support and functional validation.A biomarker can indicate disease presence or state without causing it, while a driver actively contributes to initiation or progression.
- Biomarker and driver roles
- Effect size and adjusted evidence
- Within-group consistency
- Healthy-tissue specificity
- Clinical detectability
- Independent replication
Single-cell Barcodes and UMIs
Cell isolation, molecular tagging, reverse transcription, amplification, demultiplexing and molecule counting.Single-cell RNA sequencing isolates viable cells, attaches a cell barcode to molecules from each cell and assigns a unique molecular identifier to each original RNA molecule.
- Cell isolation and dissociation
- Cell barcodes
- Unique molecular identifiers
- Poly(T) priming
- Reverse transcription
- Amplification and pooling
Single-cell Quality Control
nFeature, nCount, mitochondrial reads, doublets, contextual thresholds, filtering and sensitivity checks.Single-cell quality control interprets nFeature, nCount and mitochondrial-read proportion together.
- nFeature distributions
- nCount distributions
- Mitochondrial reads
- Low-quality cells
- Doublet risk
- Biological context
Clustering and Cell Annotation
Expression neighbourhoods, UMAP, cluster stability, positive and negative markers, references and provisional labels.Clustering groups cells by high-dimensional expression similarity, while UMAP and t-SNE provide lower-dimensional views of those neighbourhoods. A visible group is not automatically a cell type.
- Expression neighbourhoods
- Dimensionality reduction
- UMAP and t-SNE
- Clustering resolution
- Marker combinations
- Reference annotation
Multi-omics Integration and Communication
Transcriptomic and proteomic evidence, metadata alignment, workflow schematics, presentation structure and question defence.The unit closes with proteomics, lead prioritisation, metadata analysis and multi-omics integration.
- Matched biological questions
- Sample and identifier alignment
- Transcript and protein evidence
- Metadata analysis
- Pathway-level integration
- Discordant layers
The unit materials emphasise real datasets, sequencing technologies, genome resources, statistical reasoning, research-specific workflow design and scientific communication. The recurring task is not simply to run software.
You must explain why the samples support the comparison, whether the data are suitable, what each output measures and how far the biological conclusion can travel.
The first part of this guide builds the sequencing evidence chain. Experimental design identifies the biological unit, conditions, controls, replicates, batches and intended library.
Library preparation and sequencing determine what molecules can be observed and how reads are generated. FASTQ records pair sequence with position-specific quality, while RNA-seq quality control combines distributions, sample relationships and metadata. Differential expression then joins comparison direction, effect size and adjusted evidence.
Functional analysis turns long gene lists into pathway hypotheses through over-representation analysis or gene set enrichment.
The single-cell chapters explain how dissociated cells retain computational identity after pooling. Cell barcodes identify origin, unique molecular identifiers distinguish original RNA molecules, and the resulting cell by gene matrix supports quality control, clustering and annotation.
nFeature, nCount and mitochondrial reads are interpreted together because no universal threshold fits every tissue, preparation or treatment. UMAP and t-SNE visualise expression neighbourhoods, but cluster labels still require marker combinations, sample replication and biological context.
The final chapters focus on claim calibration.
A biomarker can indicate a state without causing it, while a driver claim needs mechanistic and functional evidence. Transcriptomic and proteomic layers can agree, refine one another or reveal meaningful discordance. A strong report or presentation preserves those boundaries, shows the workflow clearly and answers questions with a claim, its evidence, its limitation and the next test.
Worked example · free
Original practice: audit a sequencing comparison
- +1State the contrast before interpreting the sign: positive values mean higher expression in treated samples than controls.
- +1Calculate the Wald statistic as 1.5 divided by 0.5, giving 3.0.
- +2Convert the effect as two raised to 1.5, giving approximately 2.83 times higher expression in treated samples.
- +1Investigate the unusual sample through identity, raw-read quality, mapping and metadata checks before any exclusion.
- +1Conclude that the gene is an associated candidate, not a causal driver, until functional evidence is available.
Key terms
- Biological replicate
- An independently sampled biological unit that contributes evidence about variation in the population or system of interest.
- Library preparation
- The molecular process that selects and converts sample material into templates compatible with a sequencing platform.
- FASTQ record
- A four-line read record containing an identifier, nucleotide sequence, separator and position-aligned quality string.
- Batch effect
- Systematic technical variation associated with processing, platform, date or another non-biological factor.
- Log fold change
- A logarithmic expression ratio whose sign depends on the declared comparison direction and reference.
- Adjusted p-value
- A statistical value modified to account for the large family of simultaneous tests in a high-throughput analysis.
- Wald statistic
- The estimated log fold change divided by its standard error, used here to rank genes for enrichment.
- Over-representation analysis
- A method testing whether an annotated function occurs in a selected gene list more often than expected by chance.
- Gene set enrichment
- A method testing whether related genes accumulate toward an end of a complete ranked list.
- Cell barcode
- A sequence tag shared by molecules from one cell that preserves cell of origin after pooling.
- Unique molecular identifier
- A sequence tag distinguishing original RNA molecules so amplified copies can be collapsed during counting.
- nFeature
- The number of different genes detected in a single cell after expression counting.
- nCount
- The total number of counted RNA molecules in a single cell after UMI processing.
- Cell annotation
- The reasoned assignment of a cell type or state using marker combinations, references and biological context.
BRM5012 FAQ
How is the unit assessed?
The official structure has three top-level components: Written at 20%, Presentation at 25%, and Exercise at 55%. The presentation is the final bioinformatics presentation, while the exercise category covers applied work across the teaching period. Use the current Moodle task pages for operational submission details.
What should I prepare before a workshop?
Read the task and preparation material, identify the dataset and expected output, and arrive able to explain the relevant workflow. The workshop rubric rewards readiness, professional communication, task understanding and critical reasoning, so record questions early and be prepared to justify analytical decisions.
How do I know whether a sequencing design is valid?
Identify the biological unit, groups, controls, independent replicates and technical batches. Check whether condition is confounded with processing day, platform or another factor. Then confirm that the library and read design can measure the feature named in the research question.
Which quality-control plot matters most?
No single plot is sufficient. Combine normalised-count distributions, sample relationships and metadata. Describe the visible pattern, connect it to a plausible mechanism, and end with a suitability decision. An unusual sample should be investigated before exclusion because several biological and technical explanations are possible.
How should I interpret a negative log fold change?
Read it from the declared contrast. For case divided by control, a negative value means lower estimated expression in case. Reversing the contrast reverses the sign without changing the data. Report the reference beside every table and plot so up and down language remains consistent.
What is the difference between ORA and GSEA?
Over-representation analysis tests a selected gene list against a background universe. Gene set enrichment analysis uses a complete ranked list and asks whether related genes accumulate toward one end. Their inputs, assumptions and interpretations differ, so state the list, ranking rule, background and database.
How do barcodes and UMIs differ?
A cell barcode identifies which cell a sequenced molecule came from. A unique molecular identifier distinguishes original RNA molecules within that cell. Reads are grouped by barcode first, then repeated barcode, gene and UMI combinations can be collapsed so amplification does not inflate molecule counts.
What makes a strong final presentation?
Give the biological question and dataset context, show a workflow whose arrows name transformations, explain quality and analysis decisions, and return to a bounded conclusion. Prepare to answer with the claim, supporting result, important limitation and the next test that would separate alternative explanations.
Questions students ask in each BRM5012 chapter
- Why does sequencing depth not replace replication?
- Depth creates more observations from the same prepared library. It can improve measurement precision, but it does not sample another independent organism, culture or tissue. Biological replication is required to estimate how the effect varies across the units to which the conclusion will apply. Chapter 01 · Experimental Design for Sequencing Studies
- What are the four lines in a FASTQ record?
- The first line identifies the read, the second stores its nucleotide sequence, the third begins with a plus sign, and the fourth stores one quality character for each sequence position. Sequence and quality lengths must match. Chapter 02 · Sequencing Libraries and FASTQ Data
- What does a centred boxplot tell me?
- Similar median-centred distributions support cross-sample comparability after normalisation. They do not prove that every sample is high quality or that biology is correct, so interpret them with sample relationships, raw-read metrics and metadata. Chapter 03 · RNA-seq Quality Control
- Why does comparison direction matter?
- A positive log fold change means higher expression in the numerator condition relative to the reference. Reverse the contrast and the sign reverses while the data remain unchanged. Keep the contrast visible beside tables, plots and narrative. Chapter 04 · Differential Expression Analysis
- Why does ORA need a background list?
- ORA compares the observed overlap with what would be expected from genes that could have been selected. Using genes that were never measured or tested changes the expected frequency and can distort enrichment. Use the actual tested universe after relevant filters. Chapter 05 · Functional Enrichment and Ranked Gene Sets
- Is the largest fold-change gene the best candidate?
- Not automatically. Candidate quality also depends on adjusted evidence, consistency, specificity, biological relevance, detectability, independent replication and intended use. A network driver may change modestly, while a dramatic signal may be a downstream consequence. Chapter 06 · Biomarkers, Drivers and Evidence
- Why are both barcodes and UMIs needed?
- The cell barcode preserves cell of origin after material is pooled. The UMI distinguishes original RNA molecules within that cell. Without the first, cells cannot be reconstructed; without the second, amplification can inflate abundance. Chapter 07 · Single-cell Barcodes and UMIs
- Why are there no universal filtering thresholds?
- Metric distributions depend on tissue, cell size, cell quality, sample preparation, sequencing method, batch and biological treatment. Choose boundaries after inspecting this dataset and report them as operational decisions rather than biological constants. Chapter 08 · Single-cell Quality Control
- What does each dot on a UMAP represent?
- In this workflow, each dot represents a single cell. Nearby dots have similar expression profiles under the chosen preprocessing and embedding, but plot distance is not physical tissue distance and the axes are not direct biological measurements. Chapter 09 · Clustering and Cell Annotation
- Must transcript and protein results agree?
- No. Translation, degradation, post-translational regulation, time point and detection limits can produce meaningful disagreement. Verify sample matching and coverage, then report what each layer shows rather than forcing a single result. Chapter 10 · Multi-omics Integration and Communication
How to prepare for the assessments
Use three passes. First reconstruct each workflow from biological question to final result, naming where confounding, information loss or measurement bias can enter. Second recompute a small sample of the analysis by hand, including a fold conversion, Wald statistic and UMI collapse.
Third rehearse the interpretation: what the result measures, what direction means, which alternative explanation remains and what evidence would test it. For workshops, review the task before class and preserve parameters and screenshots while you work. For the final presentation, build the workflow schematic early, keep comparison direction visible, and use one purpose per slide.
Spend the most revision time on transitions between stages because that is where plausible outputs become unsupported claims: design into QC, QC into differential analysis, gene lists into enrichment, clusters into labels, and association into biomarker or driver language.
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