We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available.

/pdf/deeptools2-a-next-generation-web-server-for-deep-sequencing-le4u24oph2.pdf

deepTools2: a next generation web server for deep-sequencing data analysis

IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

https://openresearch-repository.anu.edu.au/bitstream/1885/212335/1/01_Bui_IQ-TREE_2%253A_New_Models_and_2020.pdf

IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era.

Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.

g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 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.

/pdf/the-galaxy-platform-for-accessible-reproducible-and-onagm5kjs7.pdf

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

We present a Galaxy based web server for processing and visualizing deeply sequenced data. The web server’s core functionality consists of a suite of newly developed tools, called deepTools, that enable users with little bioinformatic background to explore the results of their sequencing experiments in a standardized setting. Users can upload pre-processed files with continuous data in standard formats and generate heatmaps and summary plots in a straightforward, yet highly customizable manner. In addition, we offer several tools for the analysis of files containing aligned reads and enable efficient and reproducible generation of normalized coverage files. As a modular and open-source platform, deepTools can easily be expanded and customized to future demands and developments. The deepTools webserver is freely available at http://deeptools.ie-freiburg.mpg. de and is accompanied by extensive documentation and tutorials aimed at conveying the principles of deep-sequencing data analysis. The web server can be used without registration. deepTools can be installed locally either stand-alone or as part of Galaxy.

/pdf/deeptools-a-flexible-platform-for-exploring-deep-sequencing-3zt2pyqfo4.pdf

deepTools: a flexible platform for exploring deep-sequencing data

Motivation Large biomedical datasets, such as those from genomics and imaging, are increasingly being stored on commercial and institutional cloud computing platforms. This is because cloud-scale computing resources, from robust backup to highspeed data transfer to scalable compute and storage, are needed to make these large datasets usable. However, one challenge for large-scale biomedical data on the cloud is providing secure access, especially when datasets are distributed across platforms. While there are open Web protocols for secure authentication and authorization, these protocols are not in wide use in bioinformatics and are difficult to use for even technologically sophisticated users. Results We have developed a generic and extensible approach for securely accessing biomedical datasets distributed across cloud computing platforms. Our approach combines OpenID Connect and OAuth2, best-practice Web protocols for authentication and authorization, together with Galaxy (https://galaxyproject.org), a web-based computational workbench used by thousands of scientists across the world. With our enhanced version of Galaxy, users can access and analyze data distributed across multiple cloud computing providers without any special knowledge of access/authorization protocols. Our approach does not require users to share permanent credentials (e.g., username, password, API key), instead relying on automatically-generated temporary tokens that refresh as needed. Our approach is generalizable to most identity providers and cloud computing platforms. To the best of our knowledge, Galaxy is the only computational workbench where users can access biomedical datasets across multiple cloud computing platforms using best-practice Web security approaches and thereby minimize risks of unauthorized data access and credential use. Availability and Implementation Freely available for academic and commercial use under the open-source Academic Free License (https://opensource.org/licenses/AFL-3.0) from the following Github repositories: https://github.com/galaxyproject/galaxy and https://github.com/galaxyproject/cloudauthz Contact jalili@ohsu.edu, goecksj@ohsu.edu

https://www.biorxiv.org/content/biorxiv/early/2018/12/26/506238.full.pdf

Cloud Bursting Galaxy: Federated Identity and Access Management

The rising adoption and incorporation of computers into everyday life requires human-computer interaction methods to be efficient and easy to understand. Simultaneously, complexities of underlying computer systems are increasing, inherently requiring deeper understanding and a detailed level of human-computer interaction methods. Software wizards are one important example from the category of tools that simplify this interaction. Through a simple, domain-specific, and targeted set of guided questions, wizards allow complex tasks to be completed quickly and simply. Tasks accomplished by wizards range from simple information collection to complex system configuration. Because wizards are task-specific, their lifespan is short and thus must be easily and quickly adapted such that the cost associated with wizard maintenance is minimized. This paper outlines such wizard requirements and provides a metamodeling approach to wizard generation. A domain-specific modeling language is presented, which has been shown to be helpful in the generation of domainspecific wizards that are capable of adapting to changing requirements.

/pdf/using-domain-specific-modeling-to-generate-user-interfaces-42s68toxoa.pdf

Using Domain-Specific Modeling to Generate User Interfaces for Wizards.

Growth in availability of data collection devices has allowed individual researchers to gain access to large quantities of data that needs to be analyzed. As a result, many labs and departments have acquired considerable compute resources. However, effective and efficient utilization of those resources remains a barrier for the individual researchers because the distributed computing environments are difficult to understand and control. We introduce a methodology and a tool that automatically manipulates and understands job submission parameters to realize a range of job execution alternatives across a distributed compute infrastructure. Generated alternatives are presented to a user at the time of job submission in the form of tradeoffs mapped onto two conflicting objectives, namely job cost and runtime. Such presentation of job execution alternatives allows a user to immediately and quantitatively observe viable options regarding their job execution, and thus allows the user to interact with the environment at a true service level. Generated job execution alternatives have been tested through simulation and on real-world resources and, in both cases, the average accuracy of the runtime of the generated and perceived job alternatives is within 5%.

Scheduling and planning job execution of loosely coupled applications. J Supercomput

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.

This paper described the design and implementation of G-BLAST – a Grid Service for one of the most widely used bioinformatics application Basic Local Alignment Search Tool (BLAST). G-BLAST uses the factory design pattern to provide application developers a common interface to incorporate multiple implementations of BLAST. The process of application selection, resource selection, scheduling, and monitoring is completely hidden from the end-user through the web-based user interfaces and the programmatic interfaces enable users to employ G-BLAST as part of a bioinformatics pipeline. G-BLAST uses an adaptive scheduler to select the best application and the best set of resources available that will provide the shortest turnaround time when executed in a grid environment. G-BLAST is successfully deployed on a campus and regional grid and several BLAST applications are tested for different combinations of input parameters and computational resources. Experimental results illustrate the overall performance improvements obtained with G-BLAST.

Enis Afgan

Papers

Cloud Bursting Galaxy: Federated Identity and Access Management

Using Domain-Specific Modeling to Generate User Interfaces for Wizards.

Scheduling and planning job execution of loosely coupled applications. J Supercomput

GalaxyCloudRunner: enhancing scalable computing for Galaxy.

G-BLAST: A Grid Service for BLAST.