Team Members
Ben Busby
Nicholas Johnson
Francesco Tabaro
David Enoma
Jiyao Wang
Guangfeng Song
Yuchen Ge
Jupyter Notebooks have become very popular not only for literate programming, but also for creating tidy pairs of code and figures while creating scientific publications. The edits here allow iCn3D to be embedded directly in Jupyter notebooks fully featured, including all windowing, and also allowing the scripting of the figures and visualizations from the code. Creating this functionality can lead to easily reproducible figures.
The improvements made during this hackathon make this possibile.
- Navigate to the JupyterNotebook folder of this repo and follow the instructions to use iCn3D in Jupyter Notebook.
- To build the full iCn3D software, download from this github repo and follow the instructions.
As iCn3D grows in functionality, it inevitably grows in complexity. It is imperative that we think about issues on usability. For example, we now can generate many windows containing valuable information that can overwhelm non-expert users, or even expert users. Managing these windows efficiently becomes important, as does managing the data they contain for project management, publication, storage, for second level analysis, etc.
In particular, we can generate windows with molecular interactions represented as 2D networks, Tables, 3D depiction, 1D sequence track windows, for one or two mapped structures. All these windows are related, synchronized in data. If 2 structures are mapped they can be seen superimposed in 3D, but also now side by side. At this stage, we need smart window management especially if we need to synchronize visualization of windows of 1D, 2D, 3D, or Tables Representations. By the same token, comparing data across structures will become important.
You can click the arrow icon on the dialog to minimize or expand the window. The test site is here