The ELIXIR::GA4GH Strategic Partnership

Bioschemas and 4OSS: recommendations on metadata for tools

Scalable Plant Research in Cloud Environments (Special Session)

This session will give participants an introduction to what is needed to use and create cloud-enabled bioinformatics pipelines. Speakers from several projects that are already using cloud computing to solve plant related research questions will be featured. Based on their hands-on experience, speakers will showcase usage of cloud computing in their projects, including bottlenecks and learned best practices. We will introduce participants to usage of established as well as emerging data repositories and standards. The focus will be on accessing and using these resources for FAIR data management strategies and integrative analysis leveraging the power and scalability of cloud computing, with a particular emphasis on resources created by the ELIXIR Galaxy working group, and by the larger ELIXIR community.

At the end of the session participants will be able to leverage cloud computing and data resources for their research questions according to best practices, using established production platforms.

Motivation

Plant research needs to cope with the major challenges of population growth and climate change adaptation. Sequencing of the DNA and RNA of crop and forest plants, as well as their pathogens and pests, has generated enormous quantities of data. High-throughput “omics” technologies are widely used and increasingly important to support plant biology research and breeding of diverse plant species for production of food, feed, fibre and other biomaterials, and bio-energy. Much of this data is found in well established repositories and data resources. However, large-scale automated phenotyping is now possible under controlled and field conditions, and there is classical phenotyping data available in literature and in dispersed databases. This data is heterogeneous, described in diverse ways, and difficult to find and re-use.

Significant advances in plant science can be obtained by integrating available genomic and genotyping data with diverse types of phenotyping data, including field and greenhouse experimental data, molecular, -omics and image data. Although most -omics data, and especially phenomic data, are being generated at an increasing scale from public and private research organizations, the dispersion of datasets and metadata among multiple repositories and their often poor description and annotation, make their use and exploitation still challenging or even unapproachable.

To help unlock the full potential of a multi-omics approach to plant science, it is essential to make plant data interoperable in accordance with the FAIR principles (i.e. Findable, Accessible, Interoperable and Reusable). Several standards have been built for the annotation of data sets.

For phenotyping data, ELIXIR has built an architecture based on the Breeding API (BrAPI, www.brapi.org ), an API for accessing data relevant for plant breeding developed by the international plant community. The implementation of BrAPI endpoints results in a distributed infrastructure for plant phenotyping data, allowing to access diverse datasets in different sites. To enable the interoperability of these datasets, the MIAPPE (www.miappe.org) standard for plant phenotypic data has been further developed and integration into BrAPI is ongoing. These technologies form the basis for a scalable analysis of plant phenotyping data and its integration with data in well established data archives ( www.elixir-europe.org/platforms/data/elixir-deposition-databases) such as

ArrayExpress (functional data, www.ebi.ac.uk/arrayexpress/ ),
PRIDE (proteomics, www.ebi.ac.uk/pride/archive/ ),
MetaboLights (metabolomics, www.ebi.ac.uk/metabolights/ ),
European Variant Archive (EVA, variant data, www.ebi.ac.uk/eva/ ) and
the European Nucleotide Archive (ENA, sequencing data, www.ebi.ac.uk/ena/ ).

All this creates a huge demand for compute resources that are easily accessible, scalable, and ideally equipped with a workbench that can handle large datasets and is easily deployable. On this side of the spectrum Cloud computing has gone from cutting edge to standard practice and is no longer solely the domain of computer science professionals. Many cloud providers (both scientific and commercial) exist, and private clouds exist in many universities and research institutions. The basic functionality of running cloud-native workloads can be performed on any of them, avoiding a lock-in scenario. Users are often not aware that an analysis is cloud-powered.

Projects like Bioconda ( bioconda.github.io ) and Biocontainers ( biocontainers.pro ) provide thousands of bioinformatics tools conveniently packaged for use in cloud environments, paving the way for taking bioinformatics data analysis to the cloud. Simultaneously, workflow environment systems, like Galaxy ( galaxyproject.org ) and Snakemake ( snakemake.readthedocs.io ) have been adapted to run in the cloud. As a result, cloud environments are now used in many life science research projects, and given its scalability, reproducibility and reduced costs, it is expected that more and more research projects will be conducted in this way.