Computational Genomics | Python Portfolio

Independent, AI-assisted educational implementations of genomics data processing and algorithm concepts. This portfolio was developed after prior coursework and uses newly authored Python demonstrations plus synthetic example data. It is not a copy of graded assignments or workshop notebooks.

Implemented modules

Module	Contents	Scope
genomics/sequence.py	Reverse complement, GC fraction, k-mer counts	Sequence utilities
genomics/fastq_qc.py	FASTQ parsing, Q20, Phred quality, GC	Simplified read QC
genomics/variants.py	VCF parsing, variant classification	File analysis, not variant calling
genomics/metagenomics.py	Kraken2-style report summarization	Report parsing only
genomics/single_cell_qc.py	Per-cell RNA count and mitochondrial metrics	QC only
genomics/alignment.py	Needleman–Wunsch global DNA alignment	Educational dynamic programming
genomics/read_mapping.py	Exact-match positions on a reference	No approximate mapping
genomics/assembly.py	k-mers and maximal nonbranching de Bruijn paths	Toy assembler; unresolved cycles
genomics/phylogenetics.py	Pairwise aligned DNA p-distance matrix	No inferred phylogenetic tree
genomics/annotation.py	Forward-strand ATG-to-stop ORF enumeration	Not genome annotation software

Workflow notes

docs/workflows.md outlines six broader analysis workflows, including short-read QC/mapping and variant analysis, long-read assembly, phylogenetics, genome annotation, metagenomics and single-cell RNA-seq. These are conceptual notes, not executable workflows. docs/limitations.md documents project scope.

Run

Python 3.10+; standard library only.

python -m genomics.cli
python -m unittest discover -s tests -v

Examples in data/ are fully synthetic. tests/test_examples.py checks the original five demonstrations; tests/test_extended.py checks the added algorithm modules. Run both with the command above.

Provenance and limitations

Created for technical portfolio demonstration after studying general computational-genomics topics. Code, illustrative data, documentation, and tests were prepared with AI assistance. This is not an original submission archive, and the implementation should not be represented as professional production pipelines or verified laboratory methods.

No assessment questions, provided workshop data, instructor solutions, personal identifiers, or third-party materials are redistributed. No clinical interpretations are supported. No claim is made that FastQC, Snippy, Flye, QUAST, Prokka, BLAST, IQ-TREE2, Kraken2 or Seurat were run as part of this repository. See PROJECT_NOTES.md for details.