EVpedia: an integrated database of high-throughput data for systemic analyses of extracellular vesicles

Figures & data

Fig. 1 Snapshot of EVpedia homepage (http://evpedia.info). General introduction of EVs and statistics of EVpedia are provided in the “Home” menu.

Table I Statistics of EVpedia, Exocarta, and Vesiclepedia

	EVpedia			Exocarta	Vesiclepedia
	All	Eukaryotes	Prokaryotes	Eukaryotes	Eukaryotes
Publications	5,578	4,847	731	–	–
High-throughput proteomesa
Studies	90	73	17	15	25
Datasets	129	106	23	19	32
Proteins	52,786	49,260	3,526	9,554	16,789
High-throughput transcriptomes
mRNA
Studies	15	15	0	2	4
Datasets	19	19	0	3	5
mRNAs	164,519	164,519	0	2,307	19,353
miRNA
Studies	9	9	0	4	9
Datasets	22	22	0	11	18
miRNAs	13,287	13,287	0	739	913
Lipidomes
Studies	16	15	1	9	11
Datasets	20	19	1	10	13
Lipids	345	329	16	190	201

a High-throughput proteomes in which at least 50 vesicular proteins were identified (5).

Table II Analytic tools in EVpedia, Exocarta, and Vesiclepedia

	EVpedia	Exocarta	Vesiclepedia
Search
Protein search	O	O	O
Sequence search	O	X	X
Browse	O	O	O
Analysis
Gene Ontology enrichment analysis	O	X	X
Network analysis	O	X	X
Set analysis	O	X	X
Experiment
Isolation strategy	O	O	O
High-throughput analysis strategy	O	O	O
PubMed link	O	O	O
Upload	O	X	X

Fig. 2 Browse function of EVpedia. With “Browse” menu, the protein list from the individual dataset or all the datasets can be browsed. In this figure, we browsed the vesicular proteins of the urine from normal donors (20).

Fig. 3 Gene Ontology enrichment and network analyses. All analyses in this figure involved mRNA transcriptome of EVs derived from Homo sapiens mast cell HMC-1 (21). In the “Analysis – Gene Ontology enrichment analysis” menu (a), by defining the species from which the analyzed list of proteins originates, the type of Gene Ontology terms (i.e. biological process, molecular function, and cellular component), and the cut-off of the enrichment p-value, the Gene Ontology enrichment analysis can be performed. In “Analysis – Network analysis” menu (b), by defining the species to which the functional interactome data belong, the number of additional nodes, and confidence of the interactome data, one can perform the network analysis of a protein list in EVpedia.

Fig. 4 Set analysis of EVpedia. In “Analysis – Set analysis” menu, the dataset of the “proteome+transcriptome (mRNA)”, “transcriptome (miRNA)”, and “lipidome” can be chosen for set analysis. Especially for the datasets of “proteome+transcriptome (mRNA)”, information from ortholog identification is used to build the Venn diagram (a). For example, the Venn diagram is drawn by comparing the ortholog clusters of vesicular proteomes from Homo sapiens SW480 and SW620 colorectal cancer cells (20). The functional network of SW480-specific vesicular proteins can be drawn (b).

Fig. 5 Publications in EVpedia. In “Publications” menu, one can browse papers related to prokaryotic (a) and eukaryotic (b) EVs. The bar graph shows the number of EV publications for each year. One can search the papers with a keyword in the type of “category”, “bibliography”, “title”, “author”, and “title+abstract”.

Choi DS, Kim DK, Kim YK, Gho YS. Proteomics, transcriptomics, and lipidomics of exosomes and ectosomes. Proteomics. 2013 10.1002/pmic.201200329.

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Wang Z, Hill S, Luther JM, Hachey DL, Schey KL. Proteomic analysis of urine exosomes by multidimensional protein identification technology (MudPIT). Proteomics. 2012; 12: 329–38.

 PubMed Web of Science ®Google Scholar

Ekström K, Valadi H, Sjöstrand M, Malmhäll C, Bossios A, Eldh M, etal. Characterization of mRNA and microRNA in human mast cell-derived exosomes and their transfer to other mast cells and blood CD34 progenitor cells. J Extracellular Vesicles. 2012; 1 18389, doi: http://dx.doi.org/10.3402/jev.v1i0.18389.

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