AAAKB: A manually curated database for tracking and predicting genes of Abdominal aortic aneurysm (AAA)

Abstract

Abdominal aortic aneurysm (AAA), an extremely dangerous vascular disease with high mortality, causes massive internal bleeding due to aneurysm rupture. To boost the research on AAA, efforts should be taken to organize and link the information about AAA-related genes and their functions. Currently, most researchers screen through genetic databases manually, which is cumbersome and time-consuming. Here, we developed "AAAKB" a manually curated knowledgebase containing genes, SNPs and pathways associated with AAA. In order to facilitate researchers to further explore the mechanism network of AAA, AAAKB provides predicted genes that are potentially associated with AAA. The prediction is based on the protein interaction information of genes collected in the database, and the random forest algorithm (RF) is used to build the prediction model. Some of these predicted genes are differentially expressed in patients with AAA, and some have been reported to play a role in other cardiovascular diseases, illustrating the utility of the knowledgebase in predicting novel genes. Also, AAAKB integrates a protein interaction visualization tool to quickly determine the shortest paths between target proteins. As the first knowledgebase to provide a comprehensive catalog of AAA-related genes, AAAKB will be an ideal research platform for AAA. Database URL: http://www.lqlgroup.cn:3838/AAAKB/.

Fig 1. Abdominal aortic aneurysm knowledgebase (AAAKB) scheme.

Manually curated AAA-related genes were collected and further used to predict AAA candidate genes.

Fig 2. Functional enrichment analysis for known genes in AAAKB.

The network of enriched terms is colored by cluster ID, and nodes sharing the same cluster are typically close to each other.

Fig 3. The browse page of AAAKB.

It provides access to information on genes, SNPs, pathways, gene ontologies and candidates.

Fig 4. Performance measures of different classifiers for predicting human and mouse candidate genes.

(A) Shows the evaluation of the four classifiers using the performance measures of Accuracy, Precision, Recall, and F1 score. (B) Shows the ROC curve of the four classifiers.

Fig 5. Separate gene expression analysis in AAA patients and mouse models compared to normal controls.

(A) Volcano plots of differentially expressed genes. (B) Intersections of differentially expressed genes and candidate genes predicted by RF. (C) Gene ontology analysis of genes in the intersections.

Fig 6. The shortest interaction path between a pair of given proteins.

AAAKB was equipped with R package ‘cisPath’. The shortest interaction path was derived from two given proteins based on PPI data downloaded from the STRING database.