Welcome to SeptiSearch! Here you can browse, explore, and download curated molecular results derived from sepsis studies. The app currently catalogs over 25,000 unique molecules from more than 100 publications.
To get started, select one of the tabs at the top of the page, described below:
SeptiSearch was created by Travis Blimkie, Jasmine Tam & Arjun Baghela from the Hancock Lab at the University of British Columbia. If you'd like to learn more about SeptiSearch, or to report bugs or issues, click the button below to visit our About page.
SeptiSearch is a Shiny app in which you can browse, explore, and download curated molecular signatures derived from sepsis studies. The app currently allows access to over 24,000 unique molecules from 100 publications. It was created by Travis Blimkie, Jasmine Tam & Arjun Baghela from the Hancock Lab at the University of British Columbia. The last update to the data was performed on September 20th, 2021. Travis is the main developer for the Shiny app and handles maintenance & updates. Jasmine performed all the signature curation from datasets in peer-reviewed reaearch articles and publicly available pre-prints. Arjun served as the supervisor for the project.
If you encounter a problem or bug with the app, please submit an issue at the Github page. Include with your issue details on the problem so we can reproduce it, and any inputs if relevant (e.g. your list of genes submitted to the Perform Pathway Enrichment tab).
We would like to acknowledge and thank the Canadian Institutes of Health Research (CIHR) for providing the funding for this project.
Gene Set Variation Analysis is performed using the GSVA package, and the heatmap visualization is created with pheatmap. Specified parameters include the gsva method and a Gaussian kernel. Genes with zero variance across all samples are removed prior to the analysis. Example data for GSVA represents a subset of the GEO record GSE65682.
Input gene mapping between ID types is performed using data obtained via the biomaRt package. Biological pathway/term enrichment is performed using ReactomePA and enrichR. The following resources are searched using enrichR: MSigDB's Hallmark collection, and the three main GO databases (Biological Process, Cellular Component & Molecular Function). For both methods, the results are filtered using an adjusted p-value threshold of 0.05.
SeptiSearch is written in R, and uses the following packages & resources: