BIO621 - Computational and Systems Biology I - Content and Schedule (Spring 2026)

Instructor: Asscociate Prof. Vasilis J Promponas. promponas.vasileios@ucy.ac.cy

DETAILED TEACHING SCHEDULE (Check regularly for updates)

Weeks 1–4: Python crash course for beginners

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Week 1 – Python fundamentals & biological data

Lecture topics

• What is computational & systems biology?
• Python, Jupyter, Google Colab basics
• Variables, data types, control flow
• Strings & biological sequences

Practical examples

• Reading FASTA files
• Counting nucleotide and amino acid frequencies
• GC content calculation

Exercises

• Write a script to compute GC content for multiple sequences
• Identify longest ORF in a DNA sequence

Tutorial focus

• Debugging, indentation, common Python errors

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Week 2 – Data structures & file handling

Lecture topics

• Lists, tuples, dictionaries, sets
• File I/O (FASTA, TSV)
• Writing simple reusable scripts

Practical examples

• Codon usage analysis
• k-mer counting
• Parsing UniProt FASTA headers

Exercises

• Compute codon usage bias for a bacterial genome
• Compare k-mer profiles between two sequences

Tutorial focus

• Designing clean functions

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Week 3 – Libraries for biological data analysis

Lecture topics

• NumPy basics
• Pandas for tabular biological data
• Introduction to Biopython

Practical examples

• Parsing GenBank files
• Sequence translation & transcription
• Handling expression matrices

Exercises

• Extract CDS features from GenBank and compute protein lengths
• Build a summary table of genes per chromosome

Tutorial focus

• Pandas indexing, filtering, plotting

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Week 4 – Visualization, statistics & reproducibility

Lecture topics

• Matplotlib & Seaborn
• Basic statistics (mean, variance, correlation)
• Reproducible notebooks & documentation

Practical examples

• GC content distribution plots
• Length distributions of proteins

Exercises

• Compare GC content across genomes
• Visualize amino acid composition biases

Tutorial focus

• Notebook hygiene, markdown, commenting

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Weeks 5–6: Protein sequence analysis

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Week 5 – Protein composition & intrinsic disorder

Lecture topics

• Amino acid composition & bias
• Low-complexity regions
• Intrinsically Disordered Regions (IDRs)

Practical examples

• AA composition profiling
• Using IUPred / DISOPRED (web + batch)
• Sliding-window disorder analysis

Exercises

• Compare disorder content between eukaryotic and bacterial proteomes
• Identify disorder-enriched functional classes

Tutorial focus

• Interpreting disorder predictions biologically

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Week 6 – Transmembrane proteins & topology

Lecture topics

• Alpha-helical vs beta-barrel membrane proteins
• Hydropathy scales
• Topology prediction

Practical examples

• Kyte–Doolittle plots
• TMHMM / Phobius predictions
• Consensus TM prediction

Exercises

• Identify membrane proteins in a proteome
• Compare predicted vs annotated TM proteins

Tutorial focus

• Automating web tools & parsing outputs

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Weeks 7–8: Nucleotide sequence analysis

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Week 7 – GC content, isochores & genome organization

Lecture topics

• GC bias & genome evolution
• Isochores
• Sliding window analyses

Practical examples

• GC content along chromosomes
• Detecting GC-rich/poor regions

Exercises

• Identify isochores in a eukaryotic chromosome
• Correlate GC content with gene density

Tutorial focus

• Window-based algorithms

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Week 8 – Gene prediction

Lecture topics

• ORFs, signals, content sensors
• Prokaryotic vs eukaryotic gene prediction

Practical examples

• ORF finding
• Using Prodigal / AUGUSTUS
• Evaluating predictions

Exercises

• Compare gene prediction tools on the same genome
• Analyze false positives/negatives

Tutorial focus

• Evaluation metrics (sensitivity, specificity)

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Weeks 9–10: Protein structure prediction & analysis

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Week 9 – Protein structure basics & prediction

Lecture topics

• Levels of protein structure
• Homology modeling
• AlphaFold overview & limitations

Practical examples

• Using AlphaFold DB
• Structure visualization (PyMOL / NGLView)

Exercises

• Compare predicted vs experimental structures
• Identify disordered regions in structures

Tutorial focus

• Structural interpretation

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Week 10 – Structural analysis

Lecture topics

• Secondary structure assignment
• Structural alignment
• RMSD, TM-score

Practical examples

• DSSP
• Structural comparison using BioPython

Exercises

• Compare folds within a protein family
• Relate structure to function

Tutorial focus

• Automating structure analysis

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Weeks 11–12: Gene expression & functional enrichment

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Week 11 – Gene expression analysis

Lecture topics

• RNA-seq overview
• Normalization concepts
• Differential expression

Practical examples

• Analyzing preprocessed RNA-seq matrices
• Using DESeq2-style logic in Python

Exercises

• Identify differentially expressed genes
• Visualize MA & volcano plots

Tutorial focus

• Interpreting expression results

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Week 12 – Functional enrichment & networks

Lecture topics

• GO, KEGG, Reactome
• Over-representation analysis
• Gene set enrichment analysis

Practical examples

• g:Profiler, Enrichr
• Building functional networks

Exercises

• Interpret enriched pathways
• Compare enrichment methods

Tutorial focus

• Biological storytelling with data

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Week 13: Final projects & synthesis

Final project examples

• Proteome-wide disorder and function analysis
• Membrane protein landscape of a pathogen
• GC bias and gene density in a eukaryotic genome
• Structure–function analysis of a protein family
• Differential expression & pathway analysis in disease vs control

Deliverables

• Colab notebook (reproducible)
• Short written report
• 10–15 min presentation

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Skills students will acquire

• Python-based biological data analysis
• Practical experience with real datasets
• Integration of sequence, structure & expression data
• Reproducible research practices

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Bioinformatics Research Laboratory @UCY 2005-2026.