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The objective of Deliverable 2.2 is to report the integration of benchmarking activities in the ELIXIR Tools Platform ecosystem. During the execution of the ELIXIR-EXCELERATE project the original tasks of WP2 have evolved to the generation of a integrated platform, OpenEBench, that supersedes the scope of initial Task 2.3 (section 5). For this reason, this first deliverable after the availability of the release of OpenEBench, presented in ELIXIR All Hands meeting, June 2018, contains also adoi:10.5281/zenodo.1452560 fatcat:6zkiwkxwprdvhijwywptobt5vu
more »... iled presentation of the management of Tools Monitoring metrics in the platform. Rationale and motivations about OpenEBench as ELIXIR integrated platform is described in section 8.1. OpenEBench aims to become a central hub for information about tools performance, covering three main aspects: i) scientific performance, ii) software quality, and iii) technical performance. Scientific performance is largely based in the work of scientific communities. OpenEBench provides a central data warehouse where such communities can expose their benchmarking activities for experts and non-experts users covering software developers, community participants, researchers, and/or funding bodies. The data model supporting OpenEBench efforts was outlined in a previous deliverable (D2.1: Creation of a database warehouse infrastructure for storing and organizing data for online performance assessment experiments) and will not be described here. Future developments of OpenEBench will focus in the generation of an automated platform for supporting scientific benchmarking activities driven by communities. This development will be largely related to the evolution of the ELIXIR platform for software deployment (Biocontainers), and the deployment for solutions for workflow specification and execution from ELIXIR's interoperability and compute platforms. Tools monitoring activity in OpenEBench is the central issue in D2.2. We describe the series of metrics that are being currently implemented (Section 8.2), and show how data can be obtained from the p [...]
The Plant Resistance Genes database (PRGdb; http: //prgdb.org) has been redesigned with a new user interface, new sections, new tools and new data for genetic improvement, allowing easy access not only to the plant science research community but also to breeders who want to improve plant disease resistance. The home page offers an overview of easy-toread search boxes that streamline data queries and directly show plant species for which data from candidate or cloned genes have been collected.doi:10.1093/nar/gkx1119 pmid:29156057 pmcid:PMC5753367 fatcat:efetbhidavfodglsajcnadsqay
more »... lk data files and curated resistance gene annotations are made available for each plant species hosted. The new Gene Model view offers detailed information on each cloned resistance gene structure to highlight shared attributes with other genes. PRGdb 3.0 offers 153 reference resistance genes and 177 072 annotated candidate Pathogen Receptor Genes (PRGs). Compared to the previous release, the number of putative genes has been increased from 106 to 177 K from 76 sequenced Viridiplantae and algae genomes. The DRAGO 2 tool, which automatically annotates and predicts (PRGs) from DNA and amino acid with high accuracy and sensitivity, has been added. BLAST search has been implemented to offer users the opportunity to annotate and compare their own sequences. The improved section on plant diseases displays useful information linked to genes and genomes to connect complementary data and better address specific needs. Through, a revised and enlarged collection of data, the development of new tools and a renewed portal, PRGdb 3.0 engages the plant science community in developing a consensus plan to improve knowledge and strategies to fight diseases that afflict main crops and other plants.
The identification of orthologs—genes in different species which descended from the same gene in their last common ancestor—is a prerequisite for many analyses in comparative genomics and molecular evolution. Numerous algorithms and resources have been conceived to address this problem, but benchmarking and interpreting them is fraught with difficulties (need to compare them on a common input dataset, absence of ground truth, computational cost of calling orthologs). To address this, the Questdoi:10.1093/nar/gkaa308 pmid:32374845 pmcid:PMC7319555 fatcat:fuzbeoshlvegddgzcylkiwvqpu