BMS5021 Introduction to Bioinformatics
The Complete Study & Assessment Guide · S1 2027BMS5021 Overview
- Monash University
- S1 2027
- 11 chapters
- Biology
BMS5021 Introduction to Bioinformatics is Monash University's postgraduate, Master-level introduction to computational biology, taught by Biomedicine Discovery Institute staff into the Master of Data Science and built for students with little or no prior biology.
- Assessed by Topic 1 — Summative MCQ Quizzes 30% · Topic 2 — Data-Analysis Report + Presentation + MCQ 35% + 1 more
- Key terms Central dogma, FASTQ / Phred quality score, Sequence alignment (dynamic programming), RPKM / FPKM
- Assessment move Treat BMS5021 as a set of hands-on pipelines to rehearse weekly, not a reading unit…
- Most asked Does BMS5021 have a hurdle?
What you get in the full BMS5021 Study & Assessment Guide
- 11 chapters, 100 pages — every assessed topic, week by week
- 6 original practice questions
- 11 worked examples, solved the way BMS5021 marks
- 36 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Topic 2 — Data-Analysis Report + Presentation + MCQ (35%) — 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 BMS5021 is assessed
| Component | Weight | Format |
|---|---|---|
| Topic 1 — Summative MCQ Quizzes | 30% | Three in-workshop online quizzes across Weeks 1-4 (~15% + 7.5% + 7.5%); the plagiarism & collusion statement must be completed before each quiz unlocks |
| Topic 2 — Data-Analysis Report + Presentation + MCQ | 35% | An RNA-seq differential-expression pipeline on a colorectal-cancer dataset: written report + in-class presentation + a Topic-2 MCQ (Weeks 5-8) |
| Topic 3 — Proteomics Report | 35% | A quantitative-proteomics / mass-spectrometry data-analysis report (Weeks 9-12) |
What other students are asking about BMS5021
- Where can I find lecture notes or study guides for BMS5021 at Monash?
- The BMS5021 study guide in AskSia Library covers Introduction to Bioinformatics in 11 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 BMS5021?
- No final exam is listed in the offering this AskSia Library guide is built from; BMS5021 is assessed by Topic 1 — Summative MCQ Quizzes 30%, Topic 2 — Data-Analysis Report + Presentation + MCQ 35%, Topic 3 — Proteomics Report 35%.
- What does BMS5021 cover on mutation, evolution & dna sequencing?
- In AskSia Library's BMS5021 guide, the Mutation, Evolution & DNA Sequencing chapter explains mutation types · molecular evolution · Sanger & next-generation sequencing (Topic 1, Week 3).
- What does BMS5021 cover on introduction to proteomics & personalised medicine?
- In AskSia Library's BMS5021 guide, the Introduction to Proteomics & Personalised Medicine chapter explains from genome to proteome · why proteins · personalised-medicine motivation (Topic 1, Week 4).
- What does BMS5021 cover on command-line, fastq & sequence pre-processing?
- In AskSia Library's BMS5021 guide, the Command-Line, FASTQ & Sequence Pre-processing chapter explains unix/CLI · FASTQ format · Phred quality Q = -10 log10(P) · trimming (Topic 2, Week 5).
What BMS5021 covers
BMS5021 has no exam — it is 100% coursework across three topics: Topic 1's three in-workshop MCQ quizzes (30% total), Topic 2's RNA-seq differential-expression report, in-class presentation and MCQ (35%), and Topic 3's quantitative-proteomics data-analysis report (35%).
This eleven-chapter map follows the Semester-1 teaching schedule from DNA and the genetic code through sequence alignment, RNA-seq and mass-spectrometry proteomics, so you can see how each week's method feeds the data-analysis task it is marked on. Use it to keep the whole pipeline — raw reads to interpreted result — straight as you build each report.
DNA, the Genome & the Genetic Code
DNA as the hereditary molecule · genome organisation · the genetic code (Topic 1, Weeks 1-2)Weeks 1-2 of Monash University BMS5021 Introduction to Bioinformatics lay the molecular foundation: DNA as the hereditary molecule, how the genome is organised into chromosomes, genes and non-coding regulatory DNA, and the central dogma that connects sequence to protein.
- DNA structure: four bases Adenine (A), Cytosine (C), Guanine (G), Thymine (T); base pairs A-T and C-G
- The central dogma: DNA -> (transcription) RNA -> (translation) protein, plus replication and reverse transcription (RNA -> DNA)
- Genome hierarchy: genome -> chromosomes -> genes -> non-coding regions; humans have 23 pairs = 46 chromosomes
- Non-coding 'junk' DNA regulates WHEN and WHERE genes are on/off; promoters, enhancers and transcription factors
- Three major RNAs and their proportions: rRNA (most abundant, ~75-80%), tRNA (~10%), mRNA (~5%)
- mRNA gains a poly-A 3' tail for stability and is converted to cDNA before sequencing (most platforms cannot read native RNA)
Mutation, Evolution & DNA Sequencing
Mutation types · molecular evolution · Sanger & next-generation sequencing (Topic 1, Week 3)Week 3 of Monash University BMS5021 covers how sequences change and how we read them.
- Mutation as the raw material of evolution: can be beneficial, neutral or disease-causing (e.g. lactase persistence)
- Mutagens: UV radiation causes thymine-thymine dimers; chemical carcinogens (smoking ~70 carcinogens) raise mutation rate
- Four mutation classes: silent (no protein change), missense (one amino-acid swap), nonsense (premature stop), insertion/deletion (frameshift)
- Frameshift generally causes the biggest protein change because all downstream codons shift
- Sanger sequencing: chain-termination with fluorescent ddNTPs; long reads, very accurate (~99.4%, ~Q20), not scalable
- Illumina (short-read) sequencing-by-synthesis: fragment -> adapters -> bridge amplification -> cyclic labelled dNTPs; ~35-350 bp reads
Introduction to Proteomics & Personalised Medicine
From genome to proteome · why proteins · personalised-medicine motivation (Topic 1, Week 4)Week 4 of Monash University BMS5021 moves from the genome to the proteome and closes Topic 1. It explains why the protein complement matters beyond the gene sequence, how proteome complexity vastly exceeds gene number, why mRNA and protein abundance do not always correlate, and how bioinformatics underpins personalised (precision) medicine.
- Genome (fixed sequence) vs transcriptome (varies with time/conditions) vs proteome (all proteins expressed at a given time)
- Proteome complexity scale-up: ~20-25,000 genes -> ~100,000 transcripts -> >1,000,000 proteins/proteoforms
- Sources of extra proteoforms: alternative promoters, alternative splicing, mRNA editing and post-translational modifications (PTMs)
- mRNA-protein abundance correlation is gene- and condition-specific (sometimes strong, sometimes weak) - a key reason proteomics is needed
- NGS vs proteomics: 4 nucleotides, amplifiable, scalable vs 20 amino acids, >300 PTMs, not amplifiable, harder to quantify
- Mass spectrometry measures the mass-to-charge ratio (m/z) of ions - only charged molecules can be measured
Command-Line, FASTQ & Sequence Pre-processing
Unix/CLI · FASTQ format · Phred quality Q = -10 log10(P) · trimming (Topic 2, Week 5)Week 5 of Monash University BMS5021 opens Topic 2 and the hands-on half of the unit: the Unix command line, the sequence file formats (FASTA, FASTQ, SAM/BAM), Phred quality scores and the quality-control-and-trimming step that every RNA-seq analysis begins with.
- Bioinformatics runs mostly from the command line: Unix/Linux shell navigation, file management, scripts and loops
- FASTA format: '>' header line + sequence lines; FASTQ format: 4 lines per read (identifier, sequence, +, per-base quality)
- Alignment/annotation formats: SAM (text) / BAM (binary) / CRAM; GFF/GTF/BED/WIG; VCF stores variants
- Phred quality score: Q = -10 log10(P), so P = 10^(-Q/10); Q is the base-call quality, P the error probability
- Q-to-accuracy table: Q10 = 90%, Q20 = 99%, Q30 = 99.9% (the NGS benchmark), Q40 = 99.99%
- FastQC assesses per-base quality, duplication, adapter content; RNA-seq shows expected early-read composition bias
Algorithms for Alignment & Read Mapping
Needleman-Wunsch (global) · Smith-Waterman (local) · scoring matrices · BWT / Bowtie2 / HISAT2 (Topic 2, Week 6)Week 6 of Monash University BMS5021 is the algorithmic heart of Topic 2: pairwise sequence alignment by dynamic programming (global Needleman-Wunsch and local Smith-Waterman), scoring matrices and gap penalties, and fast read mapping to a reference using the Burrows-Wheeler transform (Bowtie2/HISAT2), followed by SAM/BAM post-processing with samtools.
- Alignment = find regions of similarity between a query and a reference, maximising matched positions
- Global (Needleman-Wunsch) aligns end-to-end; local (Smith-Waterman) finds the best matching sub-region; both use dynamic programming
- Dynamic programming (slow but OPTIMAL) vs heuristic methods (fast but approximate, e.g. BLAST, short-read aligners)
- DP recurrence: F(i,j) = max( F(i-1,j-1)+s(xi,yj), F(i-1,j)-d, F(i,j-1)-d ); three steps: initialise, fill, traceback
- Identity scoring: Id(x,y) = 1 if x=y, 0 if x!=y; alignment score = sum of aligned-pair scores
- Scoring matrices and gap penalties; affine gap = large opening penalty + smaller extension penalty
Gene Expression & Differential Expression
Counting reads · RPKM/FPKM · edgeR differential expression · volcano plot · GO enrichment (Topic 2, Week 7)Week 7 of Monash University BMS5021 is the analytical climax of Topic 2: quantifying expression by counting reads per gene, normalising for gene length and library depth (RPKM/FPKM, CPM, TMM), and testing for differential expression with edgeR, then visualising with the volcano plot and interpreting with GO enrichment.
- Raw read count for a gene depends on expression x gene length x library size, so normalisation is required
- RPKM/FPKM = (10^9 x C) / (N x L): C = reads for the gene, N = total library reads, L = gene length (bp); normalises for length AND depth
- CPM filter for lowly-expressed genes: keep genes with >0.7 CPM in at least 2 samples
- TMM (Trimmed Mean of M-values) normalisation (edgeR calcNormFactors) corrects library-size differences without skew from highly-expressed genes
- edgeR pipeline: counts -> DGEList -> filter -> calcNormFactors -> design/contrast -> estimateDisp -> glmQLFit -> glmQLFTest
- topTags output columns: logFC (log2, vs reference), logCPM, F, PValue, FDR; null = no difference for a gene
Biological Databases & Annotation
NCBI/Ensembl/UniProt · BLAST & E-value · functional annotation (Topic 2, Week 8)Week 8 of Monash University BMS5021 closes Topic 2 by asking how you find and annotate sequences and, critically, how you EVALUATE the databases you use - the focus of ULO4.
- Databases are central to bioinformatics; you must evaluate their merits and limitations for a given problem (ULO4)
- Major sequence/annotation databases: NCBI, Ensembl, UniProt (protein); primary vs secondary databases
- Multi-omic cancer portals named in the unit: TCGA, ICGC, CPTAC (largest proteomic portal), DepMap, COSMIC, cBioPortal, GDC
- BLAST / PSI-BLAST / FASTA are HEURISTIC homology-search tools - fast, approximate similarity search
- The E-value: the number of hits of at least this score expected by chance in a database of this size; lower E-value = more significant (not derived as a formula here - confirm specifics on Moodle)
- Functional annotation attaches meaning (gene name, function, GO terms) to a matched sequence
Proteomics & Mass Spectrometry
Trypsin digestion · m/z · peptide-mass fingerprinting · PSM · MSFragger (Topic 3, Week 9)Week 9 of Monash University BMS5021 opens Topic 3: identifying proteins by mass spectrometry.
- Proteomics = large-scale study of the proteome: sequence, structure, function, interactions and modifications
- Mass spectrometry measures the mass-to-charge ratio (m/z) of ions: source (ionisation) -> mass analyzer (sorts by m/z) -> detector
- Shotgun / bottom-up workflow: (1) extract protein, (2) trypsin digest to peptides, (3) LC separation, (4) MS/MS spectra, (5) database-search identification
- Trypsin cleaves C-terminally of arginine (R) and lysine (K); a missed cleavage changes the peptide list
- Peptide-mass fingerprinting (PMF): compute theoretical tryptic peptide masses in silico and match experimental masses to identify the protein
- Peptide-spectrum match (PSM): MS1 = precursor mass, MS2 = fragment masses; software (MSFragger) matches to the best-scoring database peptide
Quantitative Proteomics
Label-free vs labelled quantification · normalisation · differential abundance (Topic 3, Week 10)Week 10 of Monash University BMS5021 asks not just which proteins are present but HOW MUCH: relative versus absolute quantification, label-free versus labelled (isobaric) methods, normalisation, and testing for differential protein abundance between conditions.
- Aim of quantitative proteomics: relative (fold-change between conditions) or absolute (copies per cell) protein measurement
- Proteome fractionation reduces complexity before quantification
- Label-free quantification vs labelled (isobaric) quantification approaches
- Normalisation and testing for differential abundance between conditions (the Topic 3 analysis)
- Week-10 case: SARS-CoV-2 host + viral proteome/translatome timecourse of virus infection (MSFragger search output)
- Missing values are common in proteomics; filtering rule: keep proteins present in >=25% of samples (<=75% missing)
Multi-Omics & Systems Biology
Integrating genomics/transcriptomics/proteomics · networks · protein-protein interactions (Topic 3, Week 11)Week 11 of Monash University BMS5021 (a guest lecture by A/Prof Lan Nguyen) puts the layers together: integrating genomic, transcriptomic and proteomic data, biological networks and protein-protein-interaction maps, and a systems view of the cell.
- Cancer can be characterised at multiple omic levels: genome, epigenome, transcriptome, proteome, metabolome - no single layer gives the full picture
- Systems biology (Leroy Hood, 1999): studying complex processes as integrated systems - 'the whole is more than the sum of its parts'
- Detecting each layer: epigenome (ChIP-seq, WGBS), transcriptome (RNA-seq), proteome (MS-based proteomics + PTM/phospho states)
- Benefits of integration: (1) compensate for missing/unreliable single-omic data, (2) converging evidence lowers false positives, (3) reveal the complete model
- Integration timing: early (concatenation) / middle / late integration; methods = matrix factorisation, Bayesian, network-based, correlation
- Integration tools named in the unit: mixOmics (PCA/PLS/DIABLO/MINT), iCluster, MOFA, SNF
The Analysis Workflow & Scientific Reporting
Reproducible pipelines · interpreting results · writing the data-analysis report (assessment method)This cross-cutting chapter of Monash University BMS5021 is the assessment method itself: how a bioinformatics analysis is assembled end-to-end and written up.
- The end-to-end pipeline: raw data -> QC/trim -> align/map -> quantify (count/normalise) -> statistical test -> interpret
- Reproducibility: record every tool, version, parameter and step so the analysis can be repeated
- Reading your own results critically: sanity-check alignment rates, normalisation and significance before drawing conclusions
- Report structure: introduction, methods, results with labelled figures, and justified conclusions
- Interpreting mapping statistics: reads aligned 0x / exactly 1x / >1x and the overall alignment rate
- Every figure labelled; every analytical choice (filter, normalisation, threshold) justified
The Monash Handbook frames it around three topics delivered flipped-classroom style — asynchronous mini-lectures and eModules done before the weekly in-person workshops — moving from the biology of the gene and the genetic code, through the data formats and algorithms of sequence analysis (the command line, FASTQ quality, alignment, RNA-seq differential expression), to mass-spectrometry proteomics and multi-omics.
Crucially, BMS5021 has no exam: it is assessed 100% by continuous coursework across the three topics.
Topic 1 is three in-workshop summative MCQ quizzes worth 30% together; Topic 2 is a bioinformatics data-analysis exercise — an RNA-seq differential-expression pipeline on a colorectal-cancer dataset — submitted as a written report with an in-class presentation and a Topic-2 MCQ, worth 35%; Topic 3 is a quantitative-proteomics / mass-spectrometry data-analysis report worth 35%.
That structure rewards steady, hands-on practice with real data and clear scientific writing rather than a last-minute cram, and the BMS5021 result feeds the Weighted Average Mark (WAM) that later units build on. No hurdle or competency threshold is stated in the unit materials — confirm the current assessment rules on Moodle and in the unit guide.
BMS5021 is offered in both Semester 1 and Semester 2; the schedule shown in this guide follows the Semester-1 run.
Worked example · free
Reading an edgeR topTags row and the multiple-testing problem (Topic 2 data-analysis read)
- +1Read the sign and size of the log fold-change. logFC is measured on a log2 scale, relative to the reference (normal) group. A positive logFC = +7.42 means the gene is UP-regulated in the metastasized group; because it is log2, +7.42 corresponds to roughly 2^7.42 ≈ 171-fold higher expression than normal.
- +1Confirm it is a real call, not noise. The raw PValue = 0.00394 is below 0.05, and the adjusted FDR = 0.049 is still below 0.05, so the gene survives multiple-testing correction — it is a credible differentially-expressed gene, strongly up in metastasis.
- +1Count the expected false positives at the unadjusted threshold. If you test 20,000 genes and accept any gene with P <= 0.05 as a hit, you expect about 0.05 x 20,000 = 1,000 genes to pass by chance alone even if nothing were truly different. That is the multiple-testing problem.
- +1Say why FDR is the right column. The Benjamini-Hochberg FDR controls the expected PROPORTION of false discoveries among the genes you call significant, so it is calibrated for testing thousands of genes at once; the raw PValue is not. (Bonferroni FWER is the stricter, more conservative alternative that guards against ANY false positive.) You therefore rank and threshold on FDR, not the raw PValue.
Key terms
- Central dogma
- The flow of genetic information DNA -> (transcription) RNA -> (translation) protein, with replication of DNA and reverse transcription (RNA -> DNA) as additional routes. Gene expression is the multi-step reading of a gene to build its protein.
- FASTQ / Phred quality score
- FASTQ stores each read as four lines (identifier, sequence, +, per-base quality). The Phred score Q = -10 log10(P) encodes the probability P that a base call is wrong; Q30 = 1-in-1,000 error (99.9%) is the NGS quality benchmark.
- Sequence alignment (dynamic programming)
- Finding the arrangement of two sequences that maximises matching positions. Needleman-Wunsch gives the optimal GLOBAL alignment and Smith-Waterman the optimal LOCAL alignment, both by dynamic programming (initialise, fill, traceback) with a scoring matrix and gap penalties.
- RPKM / FPKM
- Reads (or Fragments) Per Kilobase of transcript per Million mapped reads: RPKM = (10^9 x C) / (N x L), where C = reads for the gene, N = total library reads, L = gene length in bp. Normalises a raw count for BOTH gene length and library depth.
- Differential expression (edgeR)
- Statistically testing, gene by gene, whether expression differs between conditions. edgeR outputs logFC (log2 fold-change vs the reference), logCPM, F, PValue and FDR; the null hypothesis is no difference for a given gene.
- Trypsin digestion / peptide-mass fingerprinting
- In MS proteomics, trypsin cleaves a protein C-terminally of arginine (R) and lysine (K), producing peptides whose masses are matched against an in-silico tryptic digest of a database to identify the protein by its peptide-mass fingerprint.
BMS5021 FAQ
Is BMS5021 hard?
It is broad rather than deep, and the challenge is that it mixes real biology with real computing. BMS5021 is built for students with little or no biology background, so the biology is taught from first principles; the harder adjustment for most is the command line, the R/edgeR and MSFragger workflows, and doing genuine data analysis on messy sequencing and mass-spec data.
Because it is 100% coursework with no exam, success comes from keeping up with the flipped-classroom mini-lectures before each in-person workshop and practising the pipelines hands-on rather than cramming. Students who treat each week's method as something to run themselves, not just read, tend to find it manageable.
How is BMS5021 assessed?
There is no exam — BMS5021 is assessed 100% by continuous coursework across three topics. Topic 1 is three in-workshop summative MCQ quizzes worth 30% together (roughly 15% + 7.5% + 7.5% across Weeks 1-4). Topic 2 is worth 35%: a bioinformatics data-analysis exercise — an RNA-seq differential-expression pipeline on a colorectal-cancer dataset — submitted as a written report with an in-class presentation and a Topic-2 MCQ.
Topic 3 is worth 35%: a quantitative-proteomics / mass-spectrometry data-analysis report. The intra-topic sub-weights come from Moodle rather than the Monash Handbook, so confirm the exact splits, dates and submission rules in the current unit guide on Moodle.
Does BMS5021 have a hurdle?
The Monash Handbook lists all three assessment components with a null hurdle field, and no threshold or competency hurdle is stated in the available unit materials — so on the current information there is no separate hurdle to clear beyond the overall pass mark.
Because hurdle and pass rules can change between offerings, confirm the assessment rules, any threshold requirement and whether the quizzes are open- or closed-book on Moodle and in the unit guide before each task. Your BMS5021 result contributes credit points and feeds your WAM in the usual way.
Can AI help me with BMS5021?
Yes, as a study aid. Sia is an AI tutor trained on how BMS5021 is actually taught and assessed — it can explain the central dogma, walk a Needleman-Wunsch alignment or a Phred-score conversion step by step, unpack why you filter low-CPM genes before differential expression, or talk through interpreting a topTags row or a mass spectrum, and it checks your reasoning as you go.
It does NOT do your graded quizzes, report or presentation for you, and Monash University academic-integrity rules apply. Note that AI-tool use in the written reports is governed per task by the educator, so follow the acknowledgement rules on Moodle.
What does it take to do well in the BMS5021 data-analysis reports?
Markers reward a clean, reproducible pipeline and interpretation you can justify, not just a result. For Topic 2, that means QC and trimming before alignment, sensible read-mapping and counting, correct normalisation (RPKM/FPKM or TMM), and differential expression reported on the adjusted FDR with the direction stated relative to the reference group and shown on a volcano plot.
For Topic 3, it means defensible missing-value handling and quantification choices for the mass-spec data. Across both, structure the report clearly, label every figure, and explain WHY each step was done. Confirm the exact rubric and expectations for your cohort on Moodle.
Questions students ask in each BMS5021 chapter
- How does this chapter show up in BMS5021 assessment?
- The Week 1-2 biology of the gene is assessed by the Topic 1 in-workshop MCQ quizzes (30% across Weeks 1-4). Expect factual multiple-choice items on base-pairing, chromosome number, the RNA species and their proportions, promoters/enhancers and the levels of gene-expression control. There is no exam, so these quizzes are your only formal test of this material — confirm the quiz dates and format on Moodle. Chapter 01 · DNA, the Genome & the Genetic Code
- How is this chapter assessed in BMS5021?
- Week 3 is Topic 1 material, tested by the in-workshop MCQ quizzes (30% across Weeks 1-4). Typical items ask you to classify a mutation from a codon change or to compare sequencing platforms (Sanger vs Illumina vs Nanopore) on read length, accuracy, throughput and error rate. There is no exam — confirm the quiz timing on Moodle. Chapter 02 · Mutation, Evolution & DNA Sequencing
- How does Week 4 appear in the assessment?
- It is the final Topic 1 block, tested by the in-workshop MCQ quizzes (30% across Weeks 1-4). Expect conceptual items on the genome-transcriptome-proteome scale-up, the mechanisms that expand the proteome, why mRNA does not predict protein, and the definition of personalised medicine. It also sets up Topic 3 (mass-spectrometry proteomics), so understanding it well pays off later. Confirm quiz timing on Moodle. Chapter 03 · Introduction to Proteomics & Personalised Medicine
- How does this chapter feed the assessment?
- Week 5 is the first stage of the Topic 2 RNA-seq data-analysis report (35%). Your report begins with quality control (FastQC) and read trimming before any alignment, and you will use the command line and understand FASTQ/Phred to do it. A clean, justified QC-and-trimming step is exactly what markers reward at the start of the pipeline. Confirm submission details on Moodle. Chapter 04 · Command-Line, FASTQ & Sequence Pre-processing
- How does this chapter appear in the assessment?
- Week 6 is the mapping stage of the Topic 2 RNA-seq report (35%): you align trimmed reads to the reference with Bowtie2 or HISAT2, convert/sort/index with samtools, and check the alignment statistics before counting. Understanding the alignment algorithms and being able to interpret mapping stats is exactly what the report tests at this stage. Confirm the tool and dataset details on Moodle. Chapter 05 · Algorithms for Alignment & Read Mapping
- How central is this chapter to the assessment?
- It is the core of the Topic 2 data-analysis report (35%). The whole assessed task is an RNA-seq differential-expression pipeline on a colorectal-cancer dataset, and Week 7 supplies the normalisation, the edgeR differential-expression test, the volcano plot and GO interpretation. Getting the normalisation right and interpreting the topTags output correctly is what the marks turn on. Confirm the dataset and rubric on Moodle. Chapter 06 · Gene Expression & Differential Expression
- How is Week 8 assessed?
- Two ways. First, database evaluation and annotation is examinable Topic 2 content and maps to ULO4 (evaluating the merits and limitations of databases) and ULO5 (using tools and databases). Second, Week 8 is when the Topic 2 report and in-class presentation are delivered - so it is also the assessment hand-in for the 35% RNA-seq task. Confirm the exact submission and presentation timing on Moodle. Chapter 07 · Biological Databases & Annotation
- How does Week 9 feed the assessment?
- It opens Topic 3, whose 35% report is a quantitative-proteomics / mass-spectrometry data analysis. Week 9 gives you the workflow and vocabulary - digestion, m/z, PMF, PSM and MSFragger search output - that the report analysis and interpretation depend on. Understanding how a protein is identified from a spectrum is the foundation for everything you write up in Topic 3. Confirm the report details on Moodle. Chapter 08 · Proteomics & Mass Spectrometry
- How does Week 10 relate to the Topic 3 report?
- Directly - the Topic 3 report (35%) is a quantitative-proteomics / mass-spectrometry data analysis, and Week 10 supplies the quantification and differential-abundance methods, plus the missing-value handling, that the report is built on. The Week-10 SARS-CoV-2 timecourse is the kind of dataset and analysis you are assessed on. Confirm the exact report task and dataset on Moodle. Chapter 09 · Quantitative Proteomics
- How is Week 11 assessed?
- Week 11 is a guest lecture whose reasoning underpins how you interpret and contextualise the Topic 3 proteomics report (35%). You are not usually asked to run a full multi-omics integration, but you should be able to explain WHY integration matters and situate your proteomic findings in a systems-biology frame. Confirm the exact expectations for the Topic 3 report on Moodle. Chapter 10 · Multi-Omics & Systems Biology
- Why does this chapter matter so much for the mark?
- Because BMS5021 is assessed 70% by written data-analysis reports (Topic 2 and Topic 3, 35% each) plus a presentation, and those are marked on HOW you assemble and report the analysis, not only on getting a number. A reproducible pipeline, justified choices and clear figures are what separate a High Distinction report from a merely correct one. Confirm the specific rubric for each report on Moodle. Chapter 11 · The Analysis Workflow & Scientific Reporting
How to prepare for the assessments
Treat BMS5021 as a set of hands-on pipelines to rehearse weekly, not a reading unit, and use the flipped-classroom rhythm as intended: do each week's asynchronous mini-lecture and eModule BEFORE the in-person workshop so you can spend the workshop actually running the analysis.
For Topic 1, the three MCQ quizzes (30% across Weeks 1-4) reward knowing the biology cleanly — the central dogma, genome organisation (46 chromosomes, rRNA/tRNA/mRNA proportions), mutation classes, sequencing technologies and the proteome concept — so build a compact fact sheet and self-quiz.
For Topic 2 (35%), practise the whole RNA-seq pipeline end to end on the colorectal-cancer dataset: QC and trim, align (Bowtie2/HISAT2), count with featureCounts, then edgeR (filter low CPM, TMM-normalise, fit, test) and read the topTags/volcano output critically.
For Topic 3 (35%), rehearse the proteomics workflow — trypsin digestion, m/z and peptide-spectrum matching, quantification and missing-value handling — and be ready to justify every choice. Because everything is written up as a report or a presentation, keep a running note of WHY each step is done so your scientific reasoning is on the page.
When a step won't click, ask Sia to explain that single step a different way and to set you fresh practice; it teaches the method and checks your reasoning, and it never substitutes for your own graded work. Confirm assessment dates, sub-weights and rules on Moodle.
Your AI Biology tutor for BMS5021
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