Expanding the Galaxy's reference data.

Summary: Properly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows users to easily organize, retrieve and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. In addition, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenie's remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS (CernVM File System) repository from GalaxyProject.org, with mirrors across the USA, Canada, Europe and Australia, enabling easy use outside of Galaxy. Availability and implementation: The ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 22.01, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license. Access to existing data is also available through CVMFS, with instructions at https://galaxyproject.org/admin/reference-data-repo/. No new data were generated or analyzed in support of this research.

Training Infrastructure as a Service.

BACKGROUND: Hands-on training, whether in bioinformatics or other domains, often requires significant technical resources and knowledge to set up and run. Instructors must have access to powerful compute infrastructure that can support resource-intensive jobs running efficiently. Often this is achieved using a private server where there is no contention for the queue. However, this places a significant prerequisite knowledge or labor barrier for instructors, who must spend time coordinating deployment and management of compute resources. Furthermore, with the increase of virtual and hybrid teaching, where learners are located in separate physical locations, it is difficult to track student progress as efficiently as during in-person courses. FINDINGS: Originally developed by Galaxy Europe and the Gallantries project, together with the Galaxy community, we have created Training Infrastructure-as-a-Service (TIaaS), aimed at providing user-friendly training infrastructure to the global training community. TIaaS provides dedicated training resources for Galaxy-based courses and events. Event organizers register their course, after which trainees are transparently placed in a private queue on the compute infrastructure, which ensures jobs complete quickly, even when the main queue is experiencing high wait times. A built-in dashboard allows instructors to monitor student progress. CONCLUSIONS: TIaaS provides a significant improvement for instructors and learners, as well as infrastructure administrators. The instructor dashboard makes remote events not only possible but also easy. Students experience continuity of learning, as all training happens on Galaxy, which they can continue to use after the event. In the past 60 months, 504 training events with over 24,000 learners have used this infrastructure for Galaxy training.

Using Galaxy to Perform Large-Scale Interactive Data Analyses-An Update.

Modern biology continues to become increasingly computational. Datasets are becoming progressively larger, more complex, and more abundant. The computational savviness necessary to analyze these data creates an ongoing obstacle for experimental biologists. Galaxy (galaxyproject.org) provides access to computational biology tools in a web-based interface. It also provides access to major public biological data repositories, allowing private data to be combined with public datasets. Galaxy is hosted on high-capacity servers worldwide and is accessible for free, with an option to be installed locally. This article demonstrates how to employ Galaxy to perform biologically relevant analyses on publicly available datasets. These protocols use both standard and custom tools, serving as a tutorial and jumping-off point for more intensive and/or more specific analyses using Galaxy. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Finding human coding exons with highest SNP density Basic Protocol 2: Calling peaks for ChIP-seq data Basic Protocol 3: Compare datasets using genomic coordinates Basic Protocol 4: Working with multiple alignments Basic Protocol 5: Single cell RNA-seq.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update.

Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update.

High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.

Analysis of next-generation sequencing data using Galaxy.

The extraordinary throughput of next-generation sequencing (NGS) technology is outpacing our ability to analyze and interpret the data. This chapter will focus on practical informatics methods, strategies, and software tools for transforming NGS data into usable information through the use of a web-based platform, Galaxy. The Galaxy interface is explored through several different types of example analyses. Instructions for running one's own Galaxy server on local hardware or on cloud computing resources are provided. Installing new tools into a personal Galaxy instance is also demonstrated.

Using Galaxy to perform large-scale interactive data analyses.

Innovations in biomedical research technologies continue to provide experimental biologists with novel and increasingly large genomic and high-throughput data resources to be analyzed. As creating and obtaining data has become easier, the key decision faced by many researchers is a practical one: where and how should an analysis be performed? Datasets are large and analysis tool set-up and use is riddled with complexities outside of the scope of core research activities. The authors believe that Galaxy provides a powerful solution that simplifies data acquisition and analysis in an intuitive Web application, granting all researchers access to key informatics tools previously only available to computational specialists working in Unix-based environments. We will demonstrate through a series of biomedically relevant protocols how Galaxy specifically brings together (1) data retrieval from public and private sources, for example, UCSC's Eukaryote and Microbial Genome Browsers, (2) custom tools (wrapped Unix functions, format standardization/conversions, interval operations), and 3rd-party analysis tools.

The UCSC Genome Browser database: update 2011.

The University of California, Santa Cruz Genome Browser (http://genome.ucsc.edu) offers online access to a database of genomic sequence and annotation data for a wide variety of organisms. The Browser also has many tools for visualizing, comparing and analyzing both publicly available and user-generated genomic data sets, aligning sequences and uploading user data. Among the features released this year are a gene search tool and annotation track drag-reorder functionality as well as support for BAM and BigWig/BigBed file formats. New display enhancements include overlay of multiple wiggle tracks through use of transparent coloring, options for displaying transformed wiggle data, a 'mean+whiskers' windowing function for display of wiggle data at high zoom levels, and more color schemes for microarray data. New data highlights include seven new genome assemblies, a Neandertal genome data portal, phenotype and disease association data, a human RNA editing track, and a zebrafish Conservation track. We also describe updates to existing tracks.

The UCSC Genome Browser database: update 2010.

The University of California, Santa Cruz (UCSC) Genome Browser website (http://genome.ucsc.edu/) provides a large database of publicly available sequence and annotation data along with an integrated tool set for examining and comparing the genomes of organisms, aligning sequence to genomes, and displaying and sharing users' own annotation data. As of September 2009, genomic sequence and a basic set of annotation 'tracks' are provided for 47 organisms, including 14 mammals, 10 non-mammal vertebrates, 3 invertebrate deuterostomes, 13 insects, 6 worms and a yeast. New data highlights this year include an updated human genome browser, a 44-species multiple sequence alignment track, improved variation and phenotype tracks and 16 new genome-wide ENCODE tracks. New features include drag-and-zoom navigation, a Wiki track for user-added annotations, new custom track formats for large datasets (bigBed and bigWig), a new multiple alignment output tool, links to variation and protein structure tools, in silico PCR utility enhancements, and improved track configuration tools.

The ENCODE Project at UC Santa Cruz.

The goal of the Encyclopedia Of DNA Elements (ENCODE) Project is to identify all functional elements in the human genome. The pilot phase is for comparison of existing methods and for the development of new methods to rigorously analyze a defined 1% of the human genome sequence. Experimental datasets are focused on the origin of replication, DNase I hypersensitivity, chromatin immunoprecipitation, promoter function, gene structure, pseudogenes, non-protein-coding RNAs, transcribed RNAs, multiple sequence alignment and evolutionarily constrained elements. The ENCODE project at UCSC website (http://genome.ucsc.edu/ENCODE) is the primary portal for the sequence-based data produced as part of the ENCODE project. In the pilot phase of the project, over 30 labs provided experimental results for a total of 56 browser tracks supported by 385 database tables. The site provides researchers with a number of tools that allow them to visualize and analyze the data as well as download data for local analyses. This paper describes the portal to the data, highlights the data that has been made available, and presents the tools that have been developed within the ENCODE project. Access to the data and types of interactive analysis that are possible are illustrated through supplemental examples.