Pharmosome: an integrative and collective database for exploration and analysis of single nucleotide polymorphisms associated with disease

Implementation

We collected SNP-related data (e.g. SNP ID, annotation, pathway and phenotypes) from PharmGKB¹¹, NCBI^12,13, Ensemble¹⁴, DiseaseEnhancer¹⁵, GeneCards¹⁶ and Reactome¹⁷ in tab-delimited format in order to link between different available information. The collected data were categorized into four main sub-databases: SNP, Gene, Chemical and Disease. The Python 3+ programming language was utilized to read, select, filter and sort the data and to link the Python scripts with HTML and JavaScript codes.

Data collection. About 50% of the data was downloaded from PharmGKB, which is considered the most common database for SNP annotation. The data comprises the associated phenotype, the clinical perspectives and, considering storage space limitations, the remaining data are imported on-demand to use later by a set of Python functions we built to get access to the Application Programming Interface (API) of different databases. These other databases include GeneCards, DiseaseEnhancer, Ensembl and Reactome; thereby a user can return specific data using preset IDs and then export this information to the HTML interface. The number of data entries collected by Pharmosome is shown in Table 1.

Data fetching and exporting. We constructed a Python module containing 12 functions. These functions connect in different ways to import the data from the databases above either from tab-delimited files or using APIs. User-requested information is extracted and exported to the Pharmosome web interface. The Django web framework was used to build the Pharmosome web interface with HTML programming language. We used Django to build functions that receive the user input from the Pharmosome interface, process requests using a Python script, and finely export the result to the interface.

return render(request, 'WebInterface.html', context={})

Operation

Google Chrome browser is recommended to use the Pharmosome web interface (https://pharmosome.herokuapp.com/), but other internet browsers can also be used.

Functions of Pharmosome

SNP sub-database. In the SNP sub-database, users can enter the ID of a SNP (e.g. rs141033578) and receive output data about its related gene, chromosomal location, gene bands, summary of the normal function of the gene, the gene part responsible for enhancing the disease occurrence, pathway of defective gene (if available), gene transcripts and different splicing variants. Pharmosome also provides information that can be used to retrieve data of recent studies (specifically, SNP reference nucleotide and alternative nucleotide data and an explanation of how the SNP contributes to disease development and drug response). For example, an SNP in GSTP1 was associated with overall survival in 107 patients with metastatic colorectal cancer who received 5-FU/oxaliplatin combination chemotherapy that caused the replacement of isoleucine with valine at amino acid position 105 of the protein, which is known to substantially diminish enzyme activity¹⁸.

Disease sub-database. The Disease sub-database allows users to search for disease data collected from KEGG Disease and PharmGKB databases by entering the disease name. The search result is a list of genes responsible for the disease, the ID of the SNP occurring in this gene, a description of clinical annotations related to this gene and the gene-specific chemical used in the treatment. The sub-database can be used, for example, to identify the SNP, gene and chemical related to coronary artery disease caused by a high frequency of a particular polymorphism in the PLA2 gene. This gene encodes glycoprotein IIIa, which is associated with a high prevalence of premature myocardial infarction^19,20

Chemical sub-database. The Chemical sub-database has data from four sources: KEGG, PharmGKB, DrugBank and ChemSpider. After users enter a chemical name, the output is a list of the chemical name, trend and generic names, structure, description and pharmacodynamics. For example, by inputting bortezomib (a proteasome inhibitor), users receive output data relating to the clinical success of bortezomib, which established the ubiquitin (Ub)+proteasome system as a key therapeutic target in multiple myeloma^21,22.

Gene sub-database. The Gene sub-database links between different sources (NCBI GenBank, Ensemble, Reactome and DiseaseEnhancer). In particular, the DiseaseEnhancer dataset represents a new approach that determines the gene part that is responsible for enhancing occurrence of disease. NCBI provides information about gene name, location, a summary of gene function and chromosomal location. Reactome displays the pathway in which genes are involved and gives an overview description. The Gene sub-database can be used to identify the pathway, splicing variants, disease enhancing region, genomic and proteomic expression profile or even to get general information. An example of this would be looking at the SLCO1B1 gene, for which various groups tested the hypothesis of whether polymorphisms in SLCO1B1 affect pharmacokinetics and the effects of drugs in humans²³.

SNP collector. The SNP collector is a tool within Pharmosome that is designed to find all SNPs present on the gene, related to disease, associated with the chemical compound. Users can choose between different options to collect SNPs and their clinical annotations and chemicals related to each SNP. This tool can be used to find SNPs or other information. For example, it could be used to detect the bond work which occurs on location 118 of the mu opioid gene, which was 3 fold more vigorous than the wildtype in its interaction with b-endorphin Other regulatory-SNPs in this region of the gene can be linked to other phenotypes²⁴.

Pick primer. The detection of SNP existence in a DNA sample taken from patients depends on designing an appropriate primer. Primers should be compatible with the flanking sequences of SNP. As the presence of an SNP in the genome may result in disease and affect the choice of drug, there is a need to detect the presence of SNP e.g. for early disease diagnosis. The pick primer tool within Pharmosome has the important function of designing primers to detect an SNP in the genome by retrieving the SNP sequence record from the NCBI database, locating the SNP position and designing primers 50 bases before, after and within the SNP sequence.

SNP phylogeny. As discussed in previous sections, there is always some relationship between different SNPs due to the complicated interaction network between different genes. In order to determine how these SNPs are related to each other, the SNP phylogeny tool constructs a phylogenetic tree that illustrates the relationships between different SNPs²⁵ by downloading SNP and flanking sequences and commencing multiple sequence alignment to determine how far each sequence is related to others. This function could be used clarify connections in studies, such as that of Thompson et al. that showed an association of 43 SNPs in 16 genes with the response drug of atorvastatin²⁶.

Workflow

Figure 1 shows the flow of information to meet the needs of users.

Figure 1. Pharmosome workflow illustrating the processing in the background of the database and the relationships and links between different interface webpages.