Immuno-Virology Data Analysis Help
Budget: $250 – $750 USD
I’m running a bioinformatics-heavy project in immuno-virology and need a data-driven mind to handle the analysis. The work centres on parsing viral sequence files and matching them with immune-response read-outs so we can pinpoint variations linked to escape mechanisms.
You will take the raw material—high-throughput sequencing reads and laboratory assay outputs—move it through quality control, alignment, variant calling and basic phylogenetics, then weave the results into clear statistical stories and publication-ready figures. R or Python pipelines (Bioconductor, BioPython, tidyverse, matplotlib, etc.) are ideal, but I’m flexible as long as the workflow is reproducible end-to-end.
Deliverables
• Clean, documented scripts that recreate every step
• Annotated variant tables in CSV/TSV
• Integrated statistical summaries with relevant p-values and confidence metrics
• High-resolution plots ready for manuscript submission
• A brief methods note outlining tools, versions, and parameters
Acceptance criteria
• Scripts run without errors on a fresh machine using only the stated dependencies
• All figures and tables correspond exactly to the data produced by the pipeline
• Workflow and results withstand peer-review level scrutiny for transparency and repeatability
If your background blends bioinformatics with virology and you enjoy turning complex datasets into clear biological insight, I’d love to see examples of similar analyses you have completed and hear how quickly you can dive in.
You will take the raw material—high-throughput sequencing reads and laboratory assay outputs—move it through quality control, alignment, variant calling and basic phylogenetics, then weave the results into clear statistical stories and publication-ready figures. R or Python pipelines (Bioconductor, BioPython, tidyverse, matplotlib, etc.) are ideal, but I’m flexible as long as the workflow is reproducible end-to-end.
Deliverables
• Clean, documented scripts that recreate every step
• Annotated variant tables in CSV/TSV
• Integrated statistical summaries with relevant p-values and confidence metrics
• High-resolution plots ready for manuscript submission
• A brief methods note outlining tools, versions, and parameters
Acceptance criteria
• Scripts run without errors on a fresh machine using only the stated dependencies
• All figures and tables correspond exactly to the data produced by the pipeline
• Workflow and results withstand peer-review level scrutiny for transparency and repeatability
If your background blends bioinformatics with virology and you enjoy turning complex datasets into clear biological insight, I’d love to see examples of similar analyses you have completed and hear how quickly you can dive in.