What's New in CRAPome 2.0

We are proud to present the release of CRAPome 2.0. We have redesigned much of the interface and provided additional functionality. Based on user feedback, we have placed emphasis on analyzing data from users, with or without the assistance of the negative controls deposited in the repository. Major changes to the CRAPome include:

  • Redesigned interface with different functions accessible as tabs. “Search”, “Browse” and “Download” options are available strictly for exploring contaminant profiles from the repository while ““Analyze your data”” helps users score their own interactions with or without the support of publicly available negative controls.
  • Processing pipeline migrated to UniProt (from RefSeq) to facilitate mapping through BioMart.
  • Addition of 600 new experiments.
  • Support for two additional organisms (Drosophila melanogaster and Escherichia coli) and re-organization of the data based on Experiment type. Currently, three types of experiments are represented in the database: single step affinity purification, tandem (two-step) affinity purification, and the proximity labeling interaction BioID.This re-organization enables more meaningful browsing of the data.
  • Introduction of the browsing option to view the contaminant profiles of all genes.
  • Support for intensity (MS1) data when both the data and the controls are provided by the user (the controls within the CRAPome repository are all spectral counts-based).
  • Introduction of a fast SAINTexpress scorning option (for well-controlled datasets), as well as two additional specificity scores that can be computed for large datasets (recommended for 10 or more baits): CompPASS-like WD score, and a Specificity (Spe) score. All scores can be concurrently run, and their respective performance compared through ROC curves based on literature (iRefIndex, IntAct) data and against each other.
  • Visualization of the bait-prey relationships (after FC-A and SAINT scoring) as dynamic heatmaps (powered by jheatmap) for rapid exploration of the data.
  • Visualization of the interactions identified after scoring in relation to interactions reported in the literature (through iRefIndex and IntAct) through a network view (powered by Cytoscape js).
  • Exploration of the high confidence interactions through mapping to Gene Ontology or KEGG pathways (mapping is performed through UniProt annotation and enrichment is calculated in relationship to the total number of entries with the given annotation in UniProt).
  • Downloaded results tables (post analysis) are now compatible with visualization and clustering tools for interaction proteomics which can be used for the generation of high quality figures (see Knight et al., Proteomics, 2014)