Wrap-up and Outlook
Overview
Break: 30 min
Getting help:
- Stackoverflow: dominant Q&A site for everything programming related (typically would end up here after a Google search)
- Biostars: Q&A site for bioinformatics related questions
- If you really get stuck, ask questions in the course’s MS Team channel!
Further course materials (in the same style of the course):
- Software carpentry: collection of official and community contributed course materials on learning programming languages (also R and Matlab) and related computational tools
- Data carpentry: huge collection of official and community contributed course materials on data science & engineering (also covering Biology a bit)
Good coding practices and advise:
- Ten simple rules for biologists learning to program: excellent advise for newcomers
- Good Computational Biology practices: (somewhat opinionated) advised on good practices in computational biology (work in progress …)
Python libraries relevant for biotech (not covered in the course):
- Biopython: work with biological sequences
- Pydna: molecular cloning, PCR simulation, primer design, …
- scikit-image: image processing and computer vision (counting colonies, cells, …)
- auto-sklearn: auto-ML of scikit-learn
- opentrons: program opentrons liquid handlers using Python
- cobrapy: work with metabolic models
- cameo: use metabolic models for cell factory design
Popular visualization and dashboard libraries (not covered in the course):
- plotly: plotting tool with interfaces for many different programming languages (including Python, R, Matlab, …) producing interactive visualizations
- dash: create dashboards similar to Tableau, Spotfire, Microsoft Power BI etc.using Python code (similar to R shiny; uses plotly for the charts)
- Bokeh: interactive visualizations with sliders and buttons etc.
- Seaborn: good statistical visualization with error bars, violin plots etc. (written on top of matplotlib)
Finding more packages: