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WikiPathways (www.wikipathways.org) is a community-curated pathway database that enables researchers to capture rich, intuitive models of biological pathways. Importantly, pathway models from WikiPathways are a valuable source for pathway and network analysis approaches and the content is provided in different formats (e.g. RDF [1]), via dedicated apps for Cytoscape [2], and on the network data exchange platform NDEx [3]. This knowledge distribution enables the simple integration of pathway and interaction data in network analysis as highlighted in recent publications [4-6]. In response to the COVID-19 pandemic, WikiPathways established the COVID-19 community portal (http://covid.wikipathways.org), which currently contains over 20 COVID-19 related pathway models. We focus on pathway curation and the development of data analysis workflows, which are continuously executed with the newest knowledge and data. By analyzing and visualizing variations in pathway activity between cell-types, tissues, populations (e.g. male vs female, young vs old, healthy vs diseased, different ethnicities), and species, we want to provide insights into the exact mechanisms underlying differences in disease severity. As part of the international COVID-19 Disease Map project [7], curation efforts are aligned, conversions between formats are improved (e.g. between WikiPathways and Minerva [8]), and new software features are being developed. For our pathway editor PathVisio [9], we are planning to add detailed evidence and provenance information for interactions and support multi-species pathways with identifier mapping for both host (human) and virus (COVID-19) provided by BridgeDb [10]. In addition to ongoing curation efforts to grow and maintain the database, we have identified publication figures as a valuable resource. We estimate ~1000 pathway figures are published and indexed by PubMed Central each month]. These figures contain novel pathway content not present in the text nor captured in pathway databases. We performed optical character recognition (OCR) on published pathway figures to extract gene symbols [11]. A COVID-19 focused set of 221 pathway figures has already proven useful in our curation efforts (https://gladstone-bioinformatics.shinyapps.io/shiny-covidpathways). References Waagmeester A., et al. (2016) https://doi.org/10.1371/journal.pcbi.1004989 Kutmon M., et al. (2014) https://doi.org/10.12688/f1000research.4254.2 Pratt D., et al. (2015) https://dx.doi.org/10.1016%2Fj.cels.2015.10.001 Miller R.A., et al. (2019) https://doi.org/10.3389/fgene.2019.00059 Liesenborghs I., et al. (2020) https://doi.org/10.1167/iovs.61.4.24 Muñoz García A., et al. (2019) https://doi.org/10.1016/j.jsbmb.2019.01.003 Ostaszewski M., et al. (2020) https://doi.org/10.1101/379446 Bonnet E., et al. (2015) https://doi.org/10.1093/nar/gkv450 Kutmon M., et al. (2015) https://doi.org/10.1371/journal.pcbi.1004085 Van Iersel M.P., et al. (2010) https://doi.org/10.1186/1471-2105-11-5 Hanspers K., et al. (2020) https://doi.org/10.1101/2020.05.29.124503 |
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