MOTIVATION: The advance of mass spectrometry-based technologies enabled the profiling of the phosphoproteomes of a multitude of cell and tissue types. However, current research primarily focused on investigating the phosphorylation dynamics in specific cell types and experimental conditions, whereas the phosphorylation events that are common across cell/tissue types and stable regardless of experimental conditions are, so far, mostly ignored. RESULTS: Here, we developed a statistical framework to identify the stable phosphoproteome across 53 human phosphoproteomics datasets, covering 40 cell/tissue types and 194 conditions/treatments. We demonstrate that the stably phosphorylated sites (SPSs) identified from our statistical framework are evolutionarily conserved, functionally important and enriched in a range of core signaling and gene pathways. Particularly, we show that SPSs are highly enriched in the RNA splicing pathway, an essential cellular process in mammalian cells, and frequently disrupted by cancer mutations, suggesting a link between the dysregulation of RNA splicing and cancer development through mutations on SPSs. AVAILABILITY AND IMPLEMENTATION: The source code for data analysis in this study is available from Github repository https://github.com/PYangLab/SPSs under the open-source license of GPL-3. The data used in this study are publicly available (see Section 2.8). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and cell-surface proteins. Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for doublet detection, modality integration, clustering, differential RNA and protein expression analysis, antibody-derived tag evaluation, ligand-receptor interaction analysis and interactive web-based visualization of CITE-seq data. RESULTS: We demonstrate the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single-modality profiling using both simulations and real-world CITE-seq data. Furthermore, we illustrate a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Finally, we demonstrate CiteFuse for predicting ligand-receptor interactions by using multi-modal CITE-seq data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data. AVAILABILITY AND IMPLEMENTATION: CiteFuse is freely available at http://shiny.maths.usyd.edu.au/CiteFuse/ as an online web service and at https://github.com/SydneyBioX/CiteFuse/ as an R package. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.