GalaxyCloudRunner: enhancing scalable computing for Galaxy

Summary The existence of more than 100 public Galaxy servers with service quotas is indicative of the need for an increased availability of compute resources for Galaxy to use. The GalaxyCloudRunner enables a Galaxy server to easily expand its available compute capacity by sending user jobs to cloud resources. User jobs are routed to the acquired resources based on a set of configurable rules and the resources can be dynamically acquired from any of four popular cloud providers (AWS, Azure, GCP or OpenStack) in an automated fashion. Availability and implementation GalaxyCloudRunner is implemented in Python and leverages Docker containers. The source code is MIT licensed and available at https://github.com/cloudve/galaxycloudrunner. The documentation is available at http://gcr.cloudve.org/.

GalaxyCloudRunner: enhancing scalable computing for Galaxy

SummaryThe existence of more than 100 public Galaxy servers with service quotas is indicative of the need for an increased availability of compute resources for Galaxy to use. The GalaxyCloudRunner enables a Galaxy server to easily expand its available compute capacity by sending user jobs to cloud resources. User jobs are routed to the acquired resources based on a set of configurable rules and the resources can be dynamically acquired from any of 4 popular cloud providers (AWS, Azure, GCP, or OpenStack) in an automated fashion.Availability and implementationGalaxyCloudRunner is implemented in Python and leverages Docker containers. The source code is MIT licensed and available at https://github.com/cloudve/galaxycloudrunner. The documentation is available at http://gcr.cloudve.org/.ContactEnis Afgan ([email protected])Supplementary informationNone

Community Research Amid COVID-19 Pandemic: Genomics Analysis of SARS-CoV-2 over Public GALAXY server

Citizen Science has come up to perform analytics over the SARS-CoV-2 genome. Public GALAXY servers provide an automated platform for genomics analysis. Study includes design of GALAXY workflows for RNASEQ assembly and annotation as well as genomic variant discovery and perform analysis across four samples of SARS-CoV-2 infected humans obtained from the local population of Wuhan, China. It provides information about transcriptomics and genomic variants across the SARS-CoV-2 genome. Study can be extended to perform evolutionary and comparative study across each species of coronaviruses. Augmented and integrated study with cheminformatics and immunoinformatics will be a way forward for drug discovery and vaccine development.

Tool recommender system in Galaxy using deep learning

Galaxy is a web-based and open-source scientific data-processing platform. Researchers compose pipelines in Galaxy to analyse scientific data. These pipelines, also known as workflows, can be complex and difficult to create from thousands of tools, especially for researchers new to Galaxy. To make creating workflows easier, faster and less error-prone, a predictive system is developed to recommend tools facilitating further analysis. A model is created to recommend tools by analysing workflows, composed by researchers on the European Galaxy server, using a deep learning approach. The higher-order dependencies in workflows, represented as directed acyclic graphs, are learned by training a gated recurrent units (GRU) neural network, a variant of a recurrent neural network (RNN). The weights of tools used in the neural network training are derived from their usage frequencies over a period of time. The hyper-parameters of the neural network are optimised using Bayesian optimisation. An accuracy of 97% in predicting tools is achieved by the model for precision@1, precision@2 and precision@3 metrics. It is accessed by a Galaxy API to recommend tools in real-time. Multiple user interface (UI) integrations on the server communicate with this API to apprise researchers of these recommended tools [email protected]@[email protected]

Genomic variants among threatened Acropora corals

ABSTRACTGenomic sequence data for non-model organisms are increasingly available requiring the development of efficient and reproducible workflows. Here, we develop the first genomic resources and reproducible workflows for two threatened members of the reef-building coral genus Acropora. We generated genomic sequence data from multiple samples of the Caribbean A. cervicornis (staghorn coral) and A. palmata (elkhorn coral), and predicted millions of nucleotide variants among these two species and the Pacific A. digitifera. A subset of predicted nucleotide variants were verified using restriction length polymorphism assays and proved useful in distinguishing the two Caribbean Acroporids and the hybrid they form (“A. prolifera”). Nucleotide variants are freely available from the Galaxy server (usegalaxy.org), and can be analyzed there with computational tools and stored workflows that require only an internet browser. We describe these data and some of the analysis tools, concentrating on fixed differences between A. cervicornis and A. palmata. In particular, we found that fixed amino acid differences between these two species were enriched in proteins associated with development, cellular stress response and the host’s interactions with associated microbes, for instance in the Wnt pathway, ABC transporters and superoxide dismutase. Identified candidate genes may underlie functional differences in the way these threatened species respond to changing environments. Users can expand the presented analyses easily by adding genomic data from additional species as they become available.Article SummaryWe provide the first comprehensive genomic resources for two threatened Caribbean reef-building corals in the genus Acropora. We identified genetic differences in key pathways and genes known to be important in the animals’ response to the environmental disturbances and larval development. We further provide a list of candidate loci for large scale genotyping of these species to gather intra- and interspecies differences between A. cervicornis and A. palmata across their geographic range. All analyses and workflows are made available and can be used as a resource to not only analyze these corals but other non-model organisms.

SMART-RDA: A Galaxy Workflow for RNA-Seq Data Analysis

<p class="Els-Abstract-text">RNA-seq using the Next Generation Sequencing (NGS) approach is a common technology to analyze large-scale RNA transcript data for gene expression studies. However, an appropriate bioinformatics tool is needed to analyze a large amount of transcriptomes data from RNA-seq experiment. The aim of this study was to construct a system that can be easily applied to analyze RNA-seq data. RNA-seq analysis tool as SMART-RDA was constructed in this study. It is a computational workflow based on Galaxy framework to be used for analyzing RNA-seq raw data into gene expression information. This workflow was adapted from a well-known Tuxedo Protocol for RNA-seq analysis with some modifications. Expression value from each transcriptome was quantitatively stated as Fragments Per Kilobase of exon per Million fragments (FPKM). RNA-seq data of sterile and fertile oil palm (Pisifera) pollens derived from Sequence Read Archive (SRA) NCBI were used to test this workflow in local facility Galaxy server. The results showed that differentially gene expression in pollens might be responsible for sterile and fertile characteristics in palm oil Pisifera.</p><p><strong>Keywords:</strong> FPKM; Galaxy workflow; Gene expression; RNA sequencing.</p>

Integration of EGA secure data access into Galaxy

High-throughput molecular profiling techniques are routinely generating vast amounts of data for translational medicine studies. Secure access controlled systems are needed to manage, store, transfer and distribute these data due to its personally identifiable nature. The European Genome-phenome Archive (EGA) was created to facilitate access and management to long-term archival of bio-molecular data. Each data provider is responsible for ensuring a Data Access Committee is in place to grant access to data stored in the EGA. Moreover, the transfer of data during upload and download is encrypted. ELIXIR, a European research infrastructure for life-science data, initiated a project (2016 Human Data Implementation Study) to understand and document the ELIXIR requirements for secure management of controlled-access data. As part of this project, a full ecosystem was designed to connect archived raw experimental molecular profiling data with interpreted data and the computational workflows, using the CTMM Translational Research IT (CTMM-TraIT) infrastructure http://www.ctmm-trait.nl as an example. Here we present the first outcomes of this project, a framework to enable the download of EGA data to a Galaxy server in a secure way. Galaxy provides an intuitive user interface for molecular biologists and bioinformaticians to run and design data analysis workflows. More specifically, we developed a tool -- ega_download_streamer - that can download data securely from EGA into a Galaxy server, which can subsequently be further processed. This tool will allow a user within the browser to run an entire analysis containing sensitive data from EGA, and to make this analysis available for other researchers in a reproducible manner, as shown with a proof of concept study. The tool ega_download_streamer is available in the Galaxy tool shed: https://toolshed.g2.bx.psu.edu/view/yhoogstrate/ega_download_streamer.

Metavisitor, a suite of Galaxy tools for simple and rapid detection and discovery of viruses in deep sequence data

We present user-friendly and adaptable software to provide biologists, clinical researchers and possibly diagnostic clinicians with the ability to robustly detect and reconstruct viral genomes from complex deep sequence datasets. A set of modular bioinformatic tools and workflows was implemented as the Metavisitor package in the Galaxy framework. Using the graphical Galaxy workflow editor, users with minimal computational skills can use existing Metavisitor workflows or adapt them to suit specific needs by adding or modifying analysis modules. Metavisitor can be used on our Mississippi server, or can be installed on any Galaxy server instance and a pre-configured Metavisitor server image is provided. Metavisitor works with DNA, RNA or small RNA sequencing data over a range of read lengths and can use a combination of de novo and guided approaches to assemble genomes from sequencing reads. We show that the software has the potential for quick diagnosis as well as discovery of viruses from a vast array of organisms. Importantly, we provide here executable Metavisitor use cases, which increase the accessibility and transparency of the software, ultimately enabling biologists or clinicians to focus on biological or medical questions.

Merging OpenLifeData with SADI services using Galaxy and Docker

ABSTRACTSemantic Web technologies have been widely applied in Life Sciences, for example by data providers like OpenLifeData and Web Services frameworks like SADI. The recent OpenLifeData2SADI project offers access to the OpenLifeData data store through SADI services. This paper shows how to merge data from OpenLifeData with other extant SADI services in the Galaxy bioinformatics analysis platform, making semantic data amenable to complex analyses, as a worked example demonstrates. The whole setting is reproducible through a Docker image that includes a pre-configured Galaxy server with data and workflows that constitute the worked example.