Identifying phosphorylation sites and how they change in abundance under different environmental conditions are important for elucidating the role of signal regulations in cellular processes. Phosphorylation sites are often found in close vicinity and they interact coordinately to encode biological signals. There is currently a lack of tools to appropriately quantify the abundance of mono- and multi-phosphorylated peptides and analyse their differential abundance under different environmental conditions. The ProteomeRiver is a novel pipeline that incorporates the removal of unwanted variation (Molania et al. 2019 Nucleic Acids Research, 47:6073 - 6083), linear models (limma R/Bioconductor, Ritchie et al. 2015 Nucleic Acids Research, 43(7), e47) to enable differential abundance analysis of mono- and multi-phosphorylated peptides, kinase-substrate enrichment (KinSwingR R/Bioconductor), and pathways analysis (ReactomeGSA R/Bioconductor). After filtering for phosphosites with site localization probability of 0.75 in at least one phosphopeptide, high-confidence mono- or multi-phosphorylation sites present in different unique phosphopeptides were identified, their abundance values were summed and the samples were analysed with linear models. Comparing the effect high- to low-concentration KCl treatment on the synaptic phosphoproteome (Engholm-Keller et al. 2019, PLoS Biology, 17(3), e3000170), 11,315 unique differentially abundant phosphosites from 3,255 proteins were identified, of which 3,642 multi-phosphorylation sites were found in multi-phosphorylated peptides. Interestingly, we identified 94 phosphosites where the corresponding mono- and multi-phosphorylation sites had opposing directions of significant log fold-changes, thus suggesting co-regulation of proximal phosphosites. The ProteomeRiver pipeline will be available as a R package via https://bitbucket.org/cmri-bioinformatics/proteomeriver.