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#protein folding Open access

Bioinformatics Explained: Data, Genomics & Computational Biology Systems

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This academic curriculum module delivers an analytical, biophysical, and computational exposition of bioinformatics, computational genomics, sequence alignment algorithms, structural biology, and high-throughput multi-omics data integration. Key Technical Topics & Curricular Areas Covered:1. Genomic Data Acquisition & Curation: High-throughput data pipelines for Next-Generation Sequencing (NGS), FastQ quality control (Phred scores), variant calling, and international public biological repositories (NCBI GenBank, Ensembl, Protein Data Bank).2. Algorithmic Sequence Alignment & Information Theory: Dynamic programming principles (Needleman-Wunsch global alignment, Smith-Waterman local alignment), heuristic indexing (BLAST), BLOSUM/PAM log-odds substitution scoring matrices, and Shannon entropy formulations (H = -sum(p_i * log2(p_i))) for alignment column conservation.3. Evolutionary Distance & Statistical Significance: Jukes-Cantor one-parameter nucleotide substitution correction models (d = -3/4 * ln(1 - 4/3 * p)) and Karlin-Altschul BLAST Expectation Value statistics (E = K * m * n * exp(-lambda * S)).4. Transcriptomics & High-Throughput Quantitative Metrics: RNA-seq differential expression log2 fold-change computations and Lander-Waterman whole-genome sequencing depth of coverage calculations (N = (C * G) / L).5. Modern AI-Era Computational Paradigms: Transformer-based protein language models (AlphaFold / ESMFold) predicting atomistic 3D structures from evolutionary co-variance; Physics-Informed Neural Networks (PINNs) for non-stationary Metabolic Flux Analysis (MFA); and Spatial Transcriptomics Graph Neural Networks (GNNs).6. Computational Systems Trade-offs: Dynamic programming sensitivity versus heuristic BLAST search speed, short-read sequencing fidelity versus long-read repeat resolution (PacBio / Nanopore), and full all-atom molecular dynamics versus coarse-grained virtual ligand docking.7. Pedagogical Features: An interactive web-based sequence alignment identity and mutation density simulator, multi-tier conceptual and scenario review questions, and step-by-step worked quantitative engineering calculations with explicit physical units. Format: Open Educational Resource (OER) prepared for persistent archiving on Zenodo and indexing in MERLOT. Permanent Webpage URL: https://prep4uni.online/stem/physical-technologies/biomedical-engineering/bioinformatics/

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