Genomics · Research software
Software that meets the science.
Across clinical genomics and pharmaceutical research teams, Mike has contributed to control-sample analysis, sequence annotation, and cloud-based scientific workflows.
- Engagement
- Prior professional experience · Team delivery
- Work
- Implemented workflows
- Tools
- Python · Django · AWS Batch · Sequence analysis
01 / The challenge
Specialized science. Practical tools.
Scientific tools need to connect specialized analysis with the practical work around it: preparing inputs, tracking runs, presenting results, and making a workflow executable by the team that relies on it.
02 / The contribution
Connecting analysis and execution.
- Developed control-sample analysis that compares observed variants with known variants and produces performance metrics across germline and tumor analysis modes.
- Contributed sequence alignment, variant annotation, and cross-species comparisons to researcher-facing RNAi workflows.
- Added cloud execution and integration work using containerized analysis and AWS Batch, working with scientific and engineering colleagues.
03 / The result
Results scientists can work with.
Implemented analysis components and execution workflows spanning quality assessment, research applications, and batch processing. The outputs include structured performance reports and annotated sequence results for further scientific interpretation.
Selected work from Mike's previous employment and consulting engagements. Contributions were delivered within multidisciplinary teams; scientific interpretation belongs to the domain specialists.