为了指示蛋白表达的显著变化，我们将 1.2或 0.8的折叠变化设置为c

To indicate a significant change in protein expression, we set a 1.2 or 0.8 fold change to cut-off. <br>The two-tailed Fisher's exact test was adopted, and the standard false discovery rate control method was used to correct the multiple hypothesis test. After the correction, the p value of 0.05 was significant. <br>3.2 Bioinformatics analysis <br>3.2.1 bioassay using software <br>analysis <br>software / Method <br>version / URL <br>Mass data analyzing <br>Motif analysis <br>GO annotation <br>Domain Remarks <br>KEGG Note <br>subcellular localization <br>enrichment analysis <br>cluster heatmap <br>protein interaction <br>3.2.2 Protein annotation method <br>Gene Ontology analysis <br>Gene Ontology (GO) annotations on the proteomics level come from the UniProt-GOA database (http://www.ebi.ac.uk/GOA/). <br>First, the system will convert the protein ID to UniProt ID, and then use the UniProt ID to match the GO ID, and retrieve the corresponding information from the UniProt-GOA database according to the GO ID. <br>If there is no protein information in the UniProt-GOA database, an algorithm software based on protein sequence, InterProScan, will be used to predict the GO function of the protein. <br>The protein is then classified according to cellular composition, molecular function or physiological process. <br>KEGG pathway notes<br>We use the KEGG pathway database to annotate protein pathways: first, use the KEGG online service tool KAAS to annotate the submitted proteins, and then use the KEGG mapper to match the annotated proteins into the corresponding pathways in the database. <br>Subcellular localization We use the software wolfpsort, which predicts subcellular localization, to annotate the submitted proteins. <br>For prokaryotes, we use CELLO software to predict and analyze the subcellular structure of its proteins. <br>3.2.3 Enrichment of protein functions <br>GO enrichment analysis <br>The GO annotations of proteins are divided into 3 major categories: biological processes, cell composition, and molecular functions. <br>Fisher's exact test was used to test differentially expressed proteins. With the identified protein as the background, the GO enrichment test with a P-value of less than 0.05 was considered significant. <br>Path Enrichment Analysis The <br>Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used for path enrichment analysis. <br>Finally, these channels are classified according to the KEGG website channel level classification method. <br>3.2.4 Cluster analysis <br>based on protein functional enrichment Cluster analysis based on functional enrichment of differentially expressed proteins (or differentially expressed proteins with different folds) of different groups is used to study their specific functions (GO, KEGG pathway, protein Structural domains, etc.). <br>We first collect the functional classification information and corresponding enriched P-value values ​​enriched by the protein groups used, and then screen out at least one protein group for significant enrichment (P-value

To indicate a significant change in protein expression, we set the folding change of 1.2 or 0.8 to cut-off.<br>Using the two-tail fisher's precision test, the multi-hypothesis test is corrected by standard error detection rate control method, and the p-value of 0.05 after correction is significant.<br>3.2 Bioinformatics Analysis Methods<br>3.2.1 Bioanalysis Use Software<br>Analysis<br>Software/Methods<br>Version/URL<br>Mass spectrometry analysis<br>Motif Analysis<br>GO Notes<br>Domain Notes<br>KEGG Notes<br>Sub-cell positioning<br>Enrichment Analysis<br>Cluster heat map<br>Protein interaction<br>3.2.2 Protein Comment Method<br>Gene Ontology Analysis<br>Gene Ontology (GO) notes on the proteomics dimension from the UniProt-GOA database (http://www.ebi.ac.uk/GOA/).<br>First, the system converts the protein ID to the UniProt ID, then uses the UniProt ID to match the GO ID and retrieves the information from the UniProt-GOA database according to the GO ID.<br>If there is no protein information queried in the UniProt-GOA database, a protein sequence-based algorithmic software, InterProScan, is used to predict the PROTEIN's GO function.<br>The protein is then classified according to cell composition, molecular function, or physiological processes.<br>KEGG path notes<br>We use the KEGG pathway database to annotate protein pathways: first, we use the KEGG online service tool KAAS to annotate the submitted proteins, and then the commented proteins are matched into the corresponding pathways in the database through KEGG mapper.<br>Subcellular positioning We use software that predicts subcellular positioning, wolfpsort, to perform subcellular positioning annotations on submitted proteins.<br>For protonuclear organisms, we used CELLO software to predict and analyze the subcellular structure of its proteins.<br>3.2.3 Protein Function Enrichment<br>GO Enrichment Analysis<br>The GO annotation of proteins is divided into three broad categories: biological processes, cell composition, and molecular function.<br>Fisher's precision test method (Fisher's exact test) is used to test the differential expression protein in the identification of the protein as the background, GO enrichment test P-value value less than 0.05 is considered significant.<br>Path Enrichment Analysis<br>The Kyoto Encyclopedia of Genes and Genomes (KEGG) database is used for rich analysis of the path.<br>Finally, these channels are classified according to the KEGG website path level classification method.<br>3.2.4 Clustering based on protein functional enrichment<br>Clustering of functional riches of expression proteins (or different multiples of differences) based on different groupings is used to study potential linkages and differences in specific functions (GO, KEGG pathways, protein domains, etc.).<br>We first collect functional classification information and corresponding rich P-value values for the protein grouping rich, and then filter out the significant enrichment (P-value) in at least one protein grouping

To indicate significant changes in protein expression, we will One point two Or Zero point eight The fold change of is set to cut off.<br>Double tailed Fisher's exact test and standard error detection rate control method are used to correct the multiple hypothesis test. After correction, P value is obtained zero point zero five Is significant.<br>three point two Bioinformatics analysis method<br>3.2.1 Bioanalysis software<br>analysis<br>Software / methods<br>Version / website<br>Mass spectrum data analysis<br>Motif analysis<br>Go notes<br>Domain comment<br>KEGG notes<br>subcellular localization<br>Enrichment analysis<br>Clustering thermograph<br>Protein interaction<br>3.2.2 Protein annotation method<br>Gene ontology analysis<br>The annotation of Gene Ontology (go) on proteomic level comes from UniProt Goa database（ http://www.ebi.ac.uk/GOA/ )。<br>First, the system converts protein ID to UniProt ID, then uses UniProt ID to match GO ID, and extracts corresponding information from UniProt-GOA database based on GO ID.<br>If there is no protein information in UniProt Goa database, interprocan, an algorithm software based on protein sequence, will be used to predict the go function of the protein.<br>Then the proteins were classified according to cell composition, molecular function or physiological process.<br>KEGG access notes<br>We use KEGG pathway database to annotate the protein pathway: first, we use KEGG online service tool Kaas to annotate the submitted protein, and then we use KEGG mapper to match the annotated protein into the corresponding pathway in the database.<br>Subcellular localization we used wolfpsort, a software to predict subcellular localization, to annotate the subcellular localization of the submitted protein.<br>For prokaryotes, we used cello software to predict the subcellular structure of their proteins.<br>3.2.3 Protein function enrichment<br>Go enrichment analysis<br>The GO annotation of proteins can be divided into three categories: biological process, cell composition and molecular function.<br>Fisher's exact test is used to test the differential expression of proteins in the background of identified proteins, and the p-value of go enrichment test is less than zero point zero five Considered significant.<br>Pathway enrichment analysis<br>Kyoto Encyclopedia of genes and genes (KEGG) database is used for enrichment analysis of pathways.<br>Finally, according to the KEGG website path level classification method, these paths are classified.<br>3.2.4 Clustering analysis based on protein function enrichment<br>Cluster analysis based on the functional enrichment of differential expression proteins (or differential expression proteins with different differential multiples) in different groups was used to study the potential relationship and differences in specific functions (go, KEGG pathway, protein domain, etc.).<br>First, we collect the function classification information and corresponding enrichment p-value value of the protein groups used, and then screen out the significant enrichment (p-value) in at least one protein group<br>