- I had an intensive learning phase of Python using Python Tutorial, The Hitchhiker's Guide to Python and countless other internet resources. Especially interesting I found following projects: nose for test-driven development, sphinx for generating documentations, and BioPython for bioinformatics tasks.
- I thought about when to use R, python, and C/C++ appropriately and most effectively. I think R is very good at prototyping tools combining statistics and visualization. python is an excellent generic scripting language that has a large code base. C/C++, being quite complex but efficient and powerful, remains my choice when it comes to optimize performance.
- I spent some thought on how to integrate several layers of comics data together. The paper sent by my colleague Klas Hatje may be of interest for those who work in this field: Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens by Chasman, et al.
- My colleague Nikolaus Berntenis let me know about Paintomics, developed by another colleague Fernando Garcia-Alcade and his group. The web tool seems to be able to visualize multi-omics datasets using KEGG graphics.
Samstag, 23. Juli 2016
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