Repository with data and complete pipelines to reproduce results from fingeRNAt - a software tool for analysis of nucleic acid-ligand complexes. doi: 10.1371/journal.pcbi.1009783

• Repository contents
• Jupyter Notebooks pipelines
• Quick intro to Jupyter Notebooks
  • Opening a Jupyter Notebook
  • Running a cell
  • Common troubleshooting
  • Saving Jupyter Notebook
  • Closing Jupyter Notebook
  • Further reading

This repository holds data and pipelines to perform the following analyses:

1. Analysis of interactions statistics in RNA-Ligand complexes - directory RNA_ligands_interactions_stats.
2. Analysis of RNA Puzzle 23 data RNA_Puzzle_23.
3. Analysis of interactions of HIV-1 TAR structure in complexes with it's active/inactive ligands - directory HIV-1_TAR_active_vs_inactive_ligands.
4. Test of fingeRNAt interactions detection in various combinations of receptors and ligands: - directory fingeRNAt_test.
5. Auxiliary jupyter notebook SMARTS_checker for testing SMARTS patterns used in yaml plugin.

6. Fingerprint calculation times: plot generation and raw data.
7. Relationship of RMSD and SIFts similarity for docking data for set of 144 RNA-ligand complexes

All pipelines are in form of Jupyter Notebooks. They should be run from within fingeRNAt environment to allow for calculation of Structural Interactions Fingerprints using program fingeRNAt. To learn how to use jupyter notebooks, please look at our quick guide.

Some of them require additional dependencies to be install from within fingeRNAt environment.

These are:

• Jupyter Notebook
• ipywidgets >= 7.5.1
• seaborn >= 0.10.1

Example of installation using conda

conda activate fingernat
conda install -c conda-forge notebook
conda install -c conda-forge ipywidgets
conda install -c anaconda seaborn

==============

• Opening a Jupyter Notebook
• Running a cell
• Common troubleshooting
• Saving Jupyter Notebook
• Closing Jupyter Notebook
• Further reading

Jupyter Notebook is a free, open-source and interactive web application allowing to combine code, calculated plots and explanatory texts into a single document.

For the purpose of reproducibility of all the analyzes from this repository, we advise to run Jupyter Notebooks from within fingernat environment.

e.g. using conda fingernat environment:

conda activate fingernat
jupyter notebook Pipeline.ipynb

A new tab in the Internet browser will be opened with the Jupyter Notebook.

In Jupyter Notebooks code is divided into cells, to run a cell, simply click on cell you want to run (the frame around it should become green) and hit ►| Run icon in the upper panel or hit Shift and Enter simultaneously.

If a cell is running for too long, it may mean that there is some kind of trouble with code or Jupyter Software.

Jupyter Notebook indicates that a cell is running with asterisk (marked with magenta on the picture below). Only one cell may be running at the same time.

You may either interrupt cell execution...

...or restart the Kernel

After restarting kernel, you will have to run all Jupyter Notebooks cells once again.
However, Restart & Run All runs one by one all cells in the Jupyter Notebook.

NOTE

Running fingeRNAt on large dataset may require some time to finish calculations.

To save changes in Jupyter Notebook, simply hit Ctrl+S

To close Jupyter Notebook, close the tab and shut down it's kernel in the terminal window you used to run Jupyter Notebook.

How to shut down the kernel?

1. Hit Ctrl+C in the terminal
2. You will receive the following prompt: Shutdown this notebook server (y/[n])?
3. Type y and hit Enter

More information about Jupyter Notebooks may be found at jupyter.org or in the article Using interactive digital notebooks for bioscience and informatics education.