Advanced Bioinformatics & Biological Data Analysis

Week 1: Bioinformatics Foundations + Setup

1. Day 01: Introduction to bioinformatics, career paths, and industry applications
2. Day 02: Central Dogma – DNA, RNA, proteins; FASTA, FASTQ, and GenBank formats
3. Day 03: Biological databases – NCBI, EMBL-EBI, UniProt, and PDB; search strategies
4. Day 04: Linux for bioinformatics – command line, grep, awk, and sed for FASTA/FASTQ files
5. Day 05: Sequence alignment – global vs. local alignment, BLAST introduction, blastn, blastp, E-values, and coverage

Week 2: Python for Bioinformatics + Statistics

1. Day 06: Python basics – variables, loops, functions, and file handling
2. Day 07: NumPy and Pandas – arrays, biological metadata, DataFrames, and GenBank table filtering
3. Day 08: Biopython – Seq, SeqIO, FASTA parsing, GC content, and sequence translation
4. Day 09: Data visualization – Matplotlib, Seaborn, sequence statistics, and GC distribution plots
5. Day 10: Statistics and SQL – mean, median, t-test, p-values, SQL basics, and FASTQ quality assessment hands-on

Week 3: Sequence Analysis + Phylogenetics

1. Day 11: Pairwise alignment – Needleman-Wunsch and Smith-Waterman algorithms
2. Day 12: Multiple sequence alignment using Clustal Omega and MUSCLE
3. Day 13: Phylogenetic trees – Neighbor Joining, Maximum Likelihood, and tree interpretation
4. Day 14: Hands-on phylogenetic analysis – gene-family tree construction using MEGA and iTOL
5. Day 15: Motifs and conserved domains – MEME, PROSITE, Pfam; protein structure basics and PDB

Week 4: Protein Structure + NGS and RNA-Seq

1. Day 16: PyMOL introduction – protein structure visualization and conserved domain identification
2. Day 17: NGS introduction – RNA-Seq, ChIP-Seq, and sequencing data types
3. Day 18: RNA-Seq workflow – FastQC, sequence alignment, and count matrices
4. Day 19: Differential gene expression – DESeq2 concepts, volcano plots, and gene expression interpretation
5. Day 20: Machine learning for bioinformatics – gene classification using expression data and Scikit-learn

Week 5: Biological Networks + Genomics

1. Day 21: Biological networks – STRING, Cytoscape, and protein-protein interaction analysis
2. Day 22: Genome assembly – contigs, scaffolds, and N50 statistics
3. Day 23: Variant calling – VCF format, SAMtools, and BCFtools introduction
4. Day 24: GWAS concepts and Manhattan plot interpretation
5. Day 25: Hands-on: Explore genomic datasets and perform basic variant analysis

Week 6: Metagenomics + Proteomics + Machine Learning

1. Day 26: Metagenomics – 16S rRNA sequencing and QIIME2 overview
2. Day 27: Microbiome diversity – alpha diversity, beta diversity, and visualization
3. Day 28: Hands-on microbiome data analysis using QIIME2
4. Day 29: Proteomics introduction – mass spectrometry data and MaxQuant concepts
5. Day 30: Sequence feature engineering – k-mers, physicochemical properties, and protein subcellular localization prediction

Week 7: Advanced ML + Research Capstone

1. Day 31: Machine learning model evaluation – cross-validation, confusion matrix, and imbalanced datasets
2. Day 32: Capstone 1 – GEO dataset selection, RNA-Seq data exploration, and quality control
3. Day 33: Capstone 1 – Differential expression analysis and pathway enrichment
4. Day 34: Capstone 2 – Develop an ML classifier for gene-function prediction
5. Day 35: Project visualization, volcano plots, pathway plots, GitHub repository, and research report preparation

Week 8: Portfolio + Research Skills + Career Preparation

1. Day 36: GitHub and documentation best practices for bioinformatics projects
2. Day 37: Resume and LinkedIn profile development for bioinformatics opportunities
3. Day 38: Mock interview – BLAST, Python, statistics, NGS, and bioinformatics concepts
4. Day 39: Scientific paper reading and reproduction of one methods figure
5.