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1. Data Source

Mass spectrometry (MS) is an ideal detector to quantify and identify proteins in cancers. All differentially expressed proteins (DEPs) in dbDEPC are collected from curated cancer proteomic research literatures. The following work flow demonstrates the data collection process:

External resource version

Resource Version URL
UniprotKB 2011-03-05 ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase
HUGO 2011-05-08 http://www.genenames.org/cgi-bin/hgnc_downloads.cgi
GOA 2011-05-03 ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/
GO 2011-05-06 http://www.geneontology.org/GO.downloads.ontology.shtml
KEGG 2011-04-18 ftp://ftp.genome.jp/pub/kegg
STRNG 9.0 http://string-db.org/
CanProVar 2010-05-07 http://bioinfo.vanderbilt.edu/canprovar/datadownload.php

 


2. How to Search?

You can search by proteins, cancers or browser all the MS experiments.

Then you’ll see the related MS experiment results of your queries, and you can use the conditions to filter the results.

Later, by clicking the ‘view protein’, you’ll see all the differentially expressed proteins of your queries.


3. Protein Information Page.

This page delineates the detail information for each differentially expressed protein.

Basic information is given in the Protein Summary section, including UniportKB Accession, ID, protein description, function, subcellular location, etc.

Cancer Profile section reveals the differential expression pattern across multiple cancers in four types of experimental design (Normal vs. Cancer; Metastasis; Treatment; Cancer vs. Cancer).

Curated literatures and Experiments details are given in MS Experiment section.

Validation Assays section provides other low throughput validation confirmation for the MS indentified differentially expressed protein.

Sequence variations are highlighted on the protein sequence. The yellow-marked sites were reported related with cancers, while the green ones come from dbSNP.

In Association DEPs section, we provide hyperlinks to other DEPs, which are associated with the DEP on this page with highest confidence score.

Annotation information is given in the Function Annotation section, including terms in Gene Ontology and pathway in KEGG.


4. Expression Profile.

Select a design type of your interested experiment first, input protein IDs or names and then select cancers to draw a DEPs profile heatmap in cancers.

Heatmap of differential expressed proteins across multiple cancers is visualized. Expression change ('up' or 'down') and confidence score are illustrated by color grade.


5. DEPs Association Network.

Select a design type of your interested experiment first, input protein IDs or names, select cancers, and then type in a confidence score to find associated DEPs in a network.

A DEPs association network is visualized. Blue nodes are your query proteins. Others are associated DEPs in dbDEPC database. Red DEP is always up regulated, green is always down regulated, and grey one has conflict expression patterns in different studies. The below part lists the associated DEP ids in each cancer of your queries, and by clicking the hyperlink, you will jump to other DEPs’ detail page.


6. DEPs intersection of different MS experiments

You could select at most three experiments, and click ‘Intersection’ to view the overlap of up- or down- regulated DEPs in these experiments of your interest.

A Venn diagram demonstrates the overlap of DEPs among these experiments. The below part contains the intersection DEPs identified by these experiments.


7. Upload your Findings

We would appreciate you share your finding with other users. Please leave your email, describe your experiment briefly (what cancer, what design). Then upload your published papers or a text file containing DEPs. We will manually review the upload files. Then we extract information and deposit them into dbDEPC monthly.