crisscrosslinkeR

A package for analyzing protein-protein and protein-RNA interactions.

Introduction



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.

Features



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 and filtration

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.

Customized options for export and downstream visualization

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.

Installation



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:

library("devtools")
install_github("egmg726/crisscrosslinker")

Documentation



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.

Workflows



Protein-Protein Interactions

Features

  • Data reduction of XL-MS repeats
  • BLAST integration and interactive menus within R
  • Alignment to UniProt and PDB files
  • Export for xiNET and PyMOL visualizations

    • Tutorials

    Protein-RNA Interactions

    Features

  • Proteolytic fragment detection of RBDmap experiments
  • Frequency analysis of RBD-associated amino acids
  • Heatmaps of frequency analysis within R
  • Export for PyMOL visualizations

    • Tutorials

    Gallery



    References



    Castello, Alfredo, et al. (2016), 'Comprehensive Identification of RNA-Binding Domains in Human Cells', Molecular Cell, 63 (4), 696-710.

    Castello, Alfredo, et al. (2017), 'Identification of RNA-binding domains of RNA-binding proteins in cultured cells on a system-wide scale with RBDmap', Nature Protocols, 12 (12), 2447-64.

    Courcelles, M., et al. (2017), 'CLMSVault: A Software Suite for Protein Cross-Linking Mass-Spectrometry Data Analysis and Visualization', J Proteome Res, 16 (7), 2645-52.

    Cox, Jürgen, et al. (2011), 'Andromeda: A peptide search engine integrated into the MaxQuant environment', Journal of Proteome Research, 10 (4), 1794-805.

    Graham, Martin J., et al. (2019), 'xiView: A common platform for the down-stream analysis of Crosslinking Mass Spectrometry data', bioRxiv, 561829.

    Grant, B. J., et al. (2006), 'Bio3d: an R package for the comparative analysis of protein structures', Bioinformatics, 22 (21), 2695-96.

    Holding, A. N. (2015), 'XL-MS: Protein cross-linking coupled with mass spectrometry', Methods, 89, 54-63.

    Holding, A. N., et al. (2013), 'Hekate: software suite for the mass spectrometric analysis and three-dimensional visualization of cross-linked protein sam-ples', J Proteome Res, 12 (12), 5923-33.

    Kosinski, Jan, et al. (2015), 'Xlink analyzer: Software for analysis and visuali-zation of cross-linking data in the context of three-dimensional structures', Journal of Structural Biology, 189 (3), 177-83.

    Leitner, A., et al. (2016), 'Crosslinking and Mass Spectrometry: An Integrated Technology to Understand the Structure and Function of Molecular Ma-chines', Trends Biochem Sci, 41 (1), 20-32.

    Riffle, Michael, et al. (2016), 'ProXL (Protein Cross-Linking Database): A Platform for Analysis, Visualization, and Sharing of Protein Cross-Linking Mass Spectrometry Data', Journal of proteome research, 15 (8), 2863-70.

    Schmidt, Carla, Kramer, Katharina, and Urlaub, Henning (2012), 'Investigation of protein–RNA interactions by mass spectrometry—Techniques and appli-cations', Journal of Proteomics, 75 (12), 3478-94.

    Schrodinger, LLC (2015), 'The PyMOL Molecular Graphics System, Version 1.8'.

    Tyanova, Stefka, Temu, Tikira, and Cox, Juergen (2016), 'The MaxQuant computational platform for mass spectrometry-based shotgun proteomics', Nature Protocols, 11 (12), 2301-19.

    Yang, Bing, et al. (2012), 'Identification of cross-linked peptides from complex samples', Nature Methods, 9 (9), 904-06.

    Zhang, Qi, et al. (2019), 'RNA exploits an exposed regulatory site to inhibit the enzymatic activity of PRC2', Nature Structural & Molecular Biology.