MOESM2 of Quantitative in vivo phosphoproteomics reveals reversible signaling processes during nitrogen starvation and recovery in the biofuel model organism Chlamydomonas reinhardtii

Additional file 2: Table S1. List of the 1227 quantified phosphopeptides in the whole cell for each samples. Phosphopeptides abundance was quantified based on MS1 intensity in Maxquant (see “Methods”). Data were log2 transformed and a Zero-mean normalization was applied. Percentage of coverage and number of unique peptides used for identification and score are indicated. MapMan bins were manually curated. Table S2. List of the 470 phosphopeptides significantly changed in the one-way ANOVA (p-value < 0.05). The mean abundance ± SD for each sampling time are indicated. Table S3. List of the 125 proteins identified in both proteomics and phosphoproteomics approach. For each protein the corresponding phosphopeptide(s) were added (total of 276 phosphopeptides) and coefficients of correlation were calculated. Proteomics data were zero-mean normalized. Table S4. PCA variable loadings of the phosphopeptides dataset. Table S5. Dataset for the Hierarchical clustering, based on the Mapman bin Table S6. sPLS analysis correlations of the integrated dataset employing phosphopeptides as predictive variables and physiological measurements as responsive variables. Table S7. Extraction of the network characteristics from STRING database ( https://string-db.org/ ). Table S8. sPLS analysis correlations of the integrated dataset employing Eukaryotic phosphorylations sites as predictive variables and proteomics [7] data measurements as responsive variables. Table S9. list of protein sequences used for STRING analysis in Fig. 5a.