0Learning items
8 WeeksCourse duration
0Active learners
80%Completion rule
COURSE OVERVIEW
About this course
Everything you need to understand the course before you begin.
WEEK 1: BIOINFORMATICS FOUNDATIONS & DATABASES
Day 1: Central dogma; FASTA, FASTQ, GenBank; NCBI search and sequence retrieval
Day 2: UniProt, PDB, EMBL-EBI; reliable database searching and record interpretation
Day 3: Linux basics for bioinformatics: grep, awk, sed; working with sequence files
Day 4: BLAST (blastn, blastp, tblastn); E-value, identity, coverage
Day 5: Global/local alignment, Needleman–Wunsch, MSA with Clustal Omega/MUSCLE; mini-task: retrieve and align disease-related genes
WEEK 2: PYTHON & BIOPYTHON
Day 1: Python essentials for biological data; Biopython Seq and SeqIO
Day 2: NumPy/Pandas; metadata tables, count tables, and data cleaning
Day 3: Sequence analysis in code: GC content, transcription, translation, sequence length
Day 4: NCBI Entrez API; automate sequence and record downloads
Day 5: Basic statistics and visualization: t-test, ANOVA, GC/length plots; mini-task: explore sequence or FASTQ data
WEEK 3: PHYLOGENY & PROTEIN STRUCTURE
Day 1: Phylogenetic trees: Neighbor-Joining, Maximum Likelihood, bootstrap support
Day 2: Build and annotate trees with MEGA/iTOL; interpret tree outputs
Day 3: Protein domains and motifs: Pfam, PROSITE, MEME
Day 4: PDB and PyMOL: protein visualization, residue mapping, mutation inspection
Day 5: AlphaFold DB and structural alignment concepts; mini-task: identify conserved domains and map them onto a structure
WEEK 4: NGS & RNA-SEQ
Day 1: NGS basics; Illumina data and FASTQ quality control with FastQC
Day 2: Read alignment: Bowtie2/STAR; reference genomes and alignment files
Day 3: FeatureCounts and DESeq2; count matrices and differential expression
Day 4: Volcano plots, heatmaps, GO/KEGG enrichment with g:Profiler
Day 5: Guided RNA-seq analysis using a GEO dataset; troubleshoot and reproduce one figure
WEEK 5: GENOMICS, VARIANTS & METAGENOMICS
Day 1: Genome browsers: IGV and UCSC Genome Browser
Day 2: Variant analysis: SAMtools, BCFtools, VCF; variant interpretation basics
Day 3: GWAS fundamentals and Manhattan plots
Day 4: 16S metagenomics with QIIME 2; alpha/beta diversity and PCoA
Day 5: Microbiome diversity mini-project; introduction to proteomics and MaxQuant
WEEK 6: MACHINE LEARNING & SYSTEMS BIOLOGY
Day 1: Sequence features: k-mers, PSSMs, feature engineering
Day 2: Machine learning for biological prediction: train/test split, Random Forest, SVM, evaluation metrics
Day 3: Protein–protein interaction networks using STRING and Cytoscape
Day 4: Pathway analysis with KEGG and Reactome; biological interpretation
Day 5: Introduction to scRNA-seq, Seurat and UMAP; mini-task: build and interpret a PPI network
WEEK 7: DEEP LEARNING & DEPLOYMENT
Day 1: CNNs for DNA motif detection; training data and overfitting basics
Day 2: Protein language models and ESM-2 embeddings
Day 3: Graph neural networks for protein–protein interaction prediction
Day 4: Reproducible experiments with MLflow and Docker
Day 5: Build a simple Streamlit gene-classifier/RNA-seq results app; project review
WEEK 8: RESEARCH & PORTFOLIO
Day 1: Reproduce one figure from a bioinformatics paper
Day 2: Compare two aligners or differential-expression methods; document results
Day 3: Write a concise Methods section and prepare a technical blog
Day 4: GitHub portfolio, README, and optional open-source contribution
Day 5: Mock interview, final presentation, concept map, and 6-month specialization plan
COURSE CURRICULUM
What you will learn
Explore the complete learning path, section by section.
0Sections
0Lessons
Curriculum is being preparedPublished lessons will appear here soon.