Software Development to Enhance Echinobase

技术发展总结

SUMMARY TECHNICAL DEVELOPMENT Software Development to Enhance Echinobase The Technical Development component describes software development for Echinobase. 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 and features to Echinobase, including multi-species support, Gene, Literature and Community pages; and there are many additional types of content that will require support from the technical development team, including the GEO pipeline, single-cell omics, ortholog GO terms, tools to integrate paralog gene data and more. 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 plan to achieve this goal is the Xenbase GEO module that we will implement in Echinobase, 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 type of data we aim to support: assessing data requirements, data modeling, middleware software development, testing and optimization of trial releases, developing curator, user and data browsing interfaces. Prioritization of projects is done in consultation with the echinoderm research community via annual surveys and workshops at conferences, input from our scientific advisory board and collaborators, and, in the future, in consultation with the Alliance of Genome Resources (AGR) workgroups and other external resources such as the NCBI and UCSC. Planned new projects include ATAC-seq data from GEO, paralog data from DIOPT, single-cell RNA-seq, support for genome wide CRE predictions, and computed expression phenotypes. Technical Development will also support our increased use of machine learning as applied to Echinobase literature and content curation. Aim 1. Support expanded and novel content. Aim 2. Generate custom content and support variants and single-cell datasets. Aim 3. Interface with external resources.