HPD: an online integrated human pathway database enabling systems biology studies

Abstract

Background: Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database; however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities.

Results: We developed the Human Pathway Database (HPD) by integrating heterogeneous human pathway data that are either curated at the NCI Pathway Interaction Database (PID), Reactome, BioCarta, KEGG or indexed from the Protein Lounge Web sites. Integration of pathway data at syntactic, semantic, and schematic levels was based on a unified pathway data model and data warehousing-based integration techniques. HPD provides a comprehensive online view that connects human proteins, genes, RNA transcripts, enzymes, signaling events, metabolic reaction events, and gene regulatory events. At the time of this writing HPD includes 999 human pathways and more than 59,341 human molecular entities. The HPD software provides both a user-friendly Web interface for online use and a robust relational database backend for advanced pathway querying. This pathway tool enables users to 1) search for human pathways from different resources by simply entering genes/proteins involved in pathways or words appearing in pathway names, 2) analyze pathway-protein association, 3) study pathway-pathway similarity, and 4) build integrated pathway networks. We demonstrated the usage and characteristics of the new HPD through three breast cancer case studies.

Conclusion: HPD http://bio.informatics.iupui.edu/HPD is a new resource for searching, managing, and studying human biological pathways. Users of HPD can search against large collections of human biological pathways, compare related pathways and their molecular entity compositions, and build high-quality, expanded-scope disease pathway models. The current HPD software can help users address a wide range of pathway-related questions in human disease biology studies.

Figure 1

Pathway scale distributions for HPD molecular entity data and molecular event data. A pathway scale refers to the number of molecular entities or molecular events involved in a given pathway. The frequency on the y-axis refers to the count of all pathways falling in the category of a particular pathway scale size on the x-axis. (a) Distributions of pathway scale by counting molecular entities. (b) Distributions of pathway scale by counting molecular events.

Figure 2

An overview core functionality of the online HPD software. (a) The HPD home page showing a query protein input box (supports multiple protein search using either gene names or UniProt identifiers). (b) A Web page containing the list of pathways retrieved as a result of a query protein input. Links to obtain pathway-pathway similarity matrix and pathway-protein association matrix and to download query-retrieved pathways were also shown on the Web page. (c) An advanced search page in which users may search pathways using gene name(s), with any term that appears within pathway names or with a list of one or more HPD Pathway IDs. This search retrieves a page similar to in (b). (d) A hyperlinked Web page showing pathway detailed information on molecular entities (Proteins, Complexes and Compounds) within the query pathway. (e) A hyperlinked Web page showing detailed pathway information on pathways similar to the query pathway. (f) A hyperlinked Web page showing detailed pathway information on molecular events (Interaction/Reaction/Regulation) within the query pathway. (g) A hyperlinked Web page showing pathway image link and reference articles.

Figure 3

A BRCA1-retrieved HPD pathway-protein association matrix. The matrix shows few HPD pathways involving the query protein BRCA1 (on the y-axis) against pathway molecular components (on the x-axis). A red cell in the matrix indicate that the molecular entity is present in the pathway, whereas a white cell in the matrix indicate that the molecular entity is absent from the pathway. Only few HPD pathways are shown. These BRCA1-retrieved pathways are sorted by their shared protein counts among all pairwise pathway comparisons in these pathways.

Figure 4

A BRCA1-retrieved HPD pathway-pathway similarity matrix. This is an interactive heat map containing similarity scores among the pathways involving query protein(s) or gene name(s). The tooltip shows the two pathway IDs and names corresponding to the particular cell pointed to along with their similarity score. A right click context menu shows the links (HPD_644 and HPD_340) to a Web page containing pathway information (as shown in Figure 2d). The "Compare HPD_644 against HPD_340" option will redirect to a page with a pathway-protein matrix showing proteins shared by these two pathways. The legend above the map indicates the range of similarity score (0 to 1).

Figure 5

A breast cancer-specific pathway-pathway similarity network. In this pathway-pathway similarity network, 25 HPD pathways derived from different sources are shown. The color and shapes in the diagram were drawn to indicate original HPD pathway data sources, based on the shape/color legend shown in the upper left corner. The subnetwork pathways and host pathways are also indicated with directed cyan edges. Edges are labeled with the number (in red) of molecular entities shared by the connected pathways. The count of molecular entity overlap between each pair of related pathways is labeled as red-colored numbers on the edge. Only pathway pairs with a similarity score and overlap above the threshold {S_{i, j} ≥ 0.2, AND |P_i ∩ P_j| >2} are shown.

Figure 6

A comparison of HPD "Multiple Protein Search" feature with those in Panther and KEGG. The example shows that different pathway databases retrieved different numbers of pathways. Three genes (AURKA, BRCA1, and FOXA1) were used to build this example. The KEGG database returned only one pathway in the result. Panther returned four pathways. HPD returned 25 pathways. The difference is primarily due to the use of "similar pathways" concept to allow retrieval of pathways that matched only partial gene/protein list.

Figure 7

An integrated pathway model involving FOXA1 in breast cancer. The figure shows how information from different pathway database sources are readily integrated, queried, and analyzed together in HPD for FOXA1-related breast cancer signaling studies.

Figure 8

An overview of pathway data integration process. The figure shows the whole process of pathway data integration and the basic statistics of pathway data sources.