Bioinformatics Analysis Pipeline
8 weeks · 0 milestones
Conduct a real bioinformatics analysis on a named publicly available genomic dataset from NCBI, Ensembl, or equivalent open repository: sequence alignment using BLAST, multiple sequence alignment (MUSCLE or MAFFT), phylogenetic tree construction (PhyML, IQ-TREE, or equivalent), variant annotation (Ensembl VEP, ClinVar), or RNA-seq differential expression analysis (DESeq2, edgeR). Document the complete methodology: dataset accession numbers, software tools with version numbers, parameters with rationale, and biological interpretation of outputs. The proof is the documented methodology, submitted code or pipeline, output files, and written interpretation of the biological significance of the results. All tools named are free and widely used by professional researchers — bioinformatics is among the most accessible postgraduate-level research skills available. The submitted code must be independently runnable. Reviewed by a geneticist or bioinformatician who examines the parameter choices and asks why specific settings were chosen over alternatives — requiring methodological understanding, not just tool operation.
Milestone map
Milestone map
3 milestones
Choose a specific biological question addressable through computational analysis, select an appropriate public dataset from a free repository, and document your rationale. Accessible alternative: all three milestones of this outcome can use public data — no wet-lab access is required. Real-world bioinformatics is largely computational work on existing sequencing data.
Proof required
Submit a written project proposal (1–2 pages) stating your biological question, the public dataset you have selected (include the NCBI/Ensembl/GBIF accession number or URL), your rationale for choosing it, and the analysis tool or pipeline you plan to use. Confirm the dataset has been downloaded and passes initial quality checks.
What gets checked
- Biological question is specific and answerable — 'What differentially expressed genes distinguish cancer type A from B in dataset SRX123?' not 'What is interesting in this dataset?'
- Dataset accession number or stable URL is provided — confirms the data is real and reproducible
- Tool choice is justified — not just 'I will use Python' but which library or workflow (e.g., DESeq2, STAR, BLAST, QIIME2) and why it is appropriate for this question