A package for analyzing protein-protein and protein-RNA interactions.
crisscrosslinkeR helps to optimize XL-MS and RBDmap analysis to manage a large number of repeats with easy-to-use functions in R.
In conjunction with visualization tools such as xiNET and PyMOL,
crisscrosslinkeR can help
to streamline your ability to create intuitive, colorful, and informative graphics suitable for publication.
Read more about the data generated by the lab that was used in some of the tutorials here.
crisscrosslinkeR contains a number of features to help ease data analysis of XL-MS and RBDmap datasets for reproducibility and preparation of tables and figures for publication.
Data reduction is done across multiple samples, allowing to either merge replicates or accept data points based on user-defined conditions (e.g. only under the condition the same crosslinked peptide appeared in a given number of replicates or samples). Filtration is based on signifi-cance scores that were assigned during the primary data analysis. crisscrosslinkeR is designed to handle an unlimited number of XL-MS and RBDmap datasets simultaneously for a direct comparison or data reduction, based on user preference.
There are currently no tools to visualize RBDmap data over sequences (2D visualization) or structures (3D visualization) of protein complex-es, albeit these forms of visualization are popular and supported in case of XL-MS (Courcelles, et al., 2017; Holding, et al., 2013; Kosinski, et al., 2015; Riffle, et al., 2016). crisscrosslinkeR takes advantage of the color palettes available in R, as well as custom palettes, to illustrate replicates or different experimental treatments for 2D representation of RBDmap data. For 2D visualization of XL-MS and, optionally, RBDmap data, crisscrosslinkeR exports files compatible with the user-friendly server xiView (Graham, et al., 2019). For 3D visualization of RBDmap and XL-MS data, crisscrosslinkeR uses the RCSB PDB mapping API to identify the most suitable 3D struc-ture(s) in the PDB, aligns it to the sequence of the protein used during the experiment and then use the selected structure for 3D visualization of either RBDmap or XL-MS data. Specifically, crisscrosslinkeR generates a Python script to lunch PyMOL (Schroding-er, 2015) — a popular and stable suite for molecular imaging — which the user can then use to generate publication-quality figures or for structural analysis. crisscrosslinkR can be used to hide or present color-coded crosslinking sites based on the score applied by the primary software that detected them (MaxQuant (Tyanova, et al., 2016) or pLink (Yang, et al., 2012)), the number of times linked-peptides were detected across multiple replicates, differential crosslinked sites (e.g. linked-peptides that appeared only under a certain treatment) and, when applicable, the crosslinking distances; all these are visualized with large flexibility for user-defined color selection.
Collectively, crisscrosslinkeR is a versatile pipeline that streamlines the workflow, starting from multiple raw data files down to publication-quality figures.
The package is currently available for download via GitHub. If you are unfamiliar with installing directly from GitHub, please enter the following commands in your RStudio console:
You can read the full documentation here. However, we recommend going through the tutorials/workflows first. Please note that the documentation is currently under construction and will updated soon.
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