Xenbase - Tech Development

Component: TECHNOLOGY DEVELOPMENT PROJECT SUMMARY/ABSTRACT The Technology Development component describes software development for Xenbase. This includes code in the database, web applications, user and curator interfaces. New data types and new curation efforts require new systems, tools and software interfaces to be built. Importantly, interfaces must be designed to allow our community members to browse and interrogate the data via our web portal. We have recently added many new content types to Xenbase, including RNA- seq and ChIP-seq data from GEO, and anatomical and expression phenotypes; and there are many additional types of content that will require support from technology development team, including single-cell omics, clustering tools for omics visualization, tolls to visualize human disease variants and Xenopus phenotypes. As many of the biologists generating and accessing these data are not bioinformaticians, we need to develop tools and interfaces that are intuitive and easy to use. An excellent example of how we achieved this is our recently released GEO module that allows non-experts to view, interact with, and understand RNA-seq data through a simple, intuitive and interactive web interface. Over the next five years, we will proceed through numerous steps for each new to type of data we aim to support: assessing data requirements, data modeling, middleware software development, testing and optimization of trial releases, develop curator, user and data browsing interfaces. Prioritization of projects is done in consultation with the Xenopus research community via annual surveys and workshops at conferences, input from our external advisory board and collaborators, and, in the future, in consultation with the Alliance of Genome Resources (AGR) workgroups. Planned new projects include ATAC-seq data from GEO, orthology data from DIOPT, single-cell RNA-seq, support for human disease variants and single-cell omics. Technology Development will also support our increased use of machine learning as applied to Xenopus literature curation. Aim 1: Support expanded and novel content. Aim 2: Generate custom content and support variants and single-cell datasets via external resources. Aim 3: Interface with external resources.