Thermo proteome discoverer software

For the functional enrichment analysis, the clusterProfiler was used for GO term enrichment, followed by the Biological process analysis using the KEGG pathway database. The data from NGS and proteome were sorted based on fold change in gene/protein expression. We represented the RNASeq and the proteomic data on a log scale using log2 fold change, considering a p-value less than 0.05 as significant. Since this representation is logarithmic and not linear, control values are not normalized to 1.0. Therefore, a value more than 0.0 is considered for increased expression, and a value less than 0.0 is considered for decreased expression. We used Thermo Proteome discoverer software (2.2.0.388) for the label-free quantification of the protein IDs. The contaminants, such as keratin, were removed from the raw data (in the case of the proteome), and the resulting data were normalized against parental cells. The upregulated and downregulated transcripts/proteins from OE and KD were used for analysis using STRING (www:http//string-db.org) and ShinyGo 0.76.2 (www:http//bioinformatics.sdstate.edu). Similarly, we analyzed the proteomics data obtained from the TAP system too. The data also generated from KEGG for different pathways^{38 (link)–40 (link)}.

All tandem mass spectra were analysed using Thermo Proteome Discoverer software (v. 1.2, Thermo Scientific) with the Mascot search engine (v. 2.3.2 Matrix Science, London, UK) and the NCBI database version April 2012. The search parameters were set to bony vertebrates, MS accuracy of 5 ppm, MS/MS accuracy of 0.5 Da (for fragmentation in the linear ion trap with CID), one missed cleavage by trypsin allowed, dynamic carbamidomethyl modification of cysteine (previous tests show that this modification is not 100%), and oxidised methionine. For protein identification, the minimum criteria were two tryptic peptides matched at or above the 99% confidence level, allowing only rank-one peptides and peptides in top-score proteins. No decoy database was used, but the highest confidence level for Mascot significance threshold together with two or several peptides for one protein that can ensure the identification.