BIG: a large-scale data integration tool for renal physiology

Abstract

Due to recent advances in high-throughput techniques, we and others have generated multiple proteomic and transcriptomic databases to describe and quantify gene expression, protein abundance, or cellular signaling on the scale of the whole genome/proteome in kidney cells. The existence of so much data from diverse sources raises the following question: "How can researchers find information efficiently for a given gene product over all of these data sets without searching each data set individually?" This is the type of problem that has motivated the "Big-Data" revolution in Data Science, which has driven progress in fields such as marketing. Here we present an online Big-Data tool called BIG (Biological Information Gatherer) that allows users to submit a single online query to obtain all relevant information from all indexed databases. BIG is accessible at http://big.nhlbi.nih.gov/.

Keywords: BIG data; data science; kidney physiology; systems biology.

Fig. 1.

The workflow and logic architecture of Biological Information Gatherer (BIG). The BIG is accessible through a web browser. Users submit the queries from the web browsers, and the embedded PHP codes (a scripting programing language for web site development) are executed to retrieve queried information from the MySQL database. The database was constructed based on a large dataset integrated from multiple individual experimental datasets. These databases are listed at https://hpcwebapps.cit.nih.gov/ESBL/Database/.

Fig. 2.

A screen-shot image of the web portal for Renal Epithelial Transcriptome and Proteome Databases. BIG can be accessed via center icon “Search All”. Link: https://hpcwebapps.cit.nih.gov/ESBL/Database/.

Fig. 3.

A screen-shot image of the BIG data query page. Queries are based on entry of official gene symbols. User can also use a keyword to search possible gene symbols and then select “Cell Type” or “All”.

Fig. 4.

A screen-shot image of a typical BIG data output page for query term “Akt1” and cell type “Collecting Duct”.

Fig. 5.

The histogram of current BIG record distribution. The numbers of records for each official gene symbol in BIG were calculated. X-axis shows bins corresponding to numbers of records; Y-axis shows the numbers of official gene symbols within each bin.

Fig. 6.

The 20 official gene symbols with the most records. X-axis is the numbers of records; Y-axis shows the official gene symbols.