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

Custos is open source software that provides user, group, and resource credential management services for science gateways. This paper describes the resource credential, or secrets, management service in Custos that allows science gateways to safely manage security tokens, SSH keys, and passwords on behalf of users. Science gateways such as Galaxy are well-established mechanisms for researchers to access cyberinfrastructure and, increasingly, couple it with other online services, such as user-provided storage or compute resources. To support this use case, science gateways need to operate on behalf of the users to connect, acquire, and release these resources, which are protected by a variety of authentication and access mechanisms. Storing and managing the credentials associated with these access mechanisms must be done using “best of breed” software and established security protocols. The Custos Secrets Service allows science gateways to store and retrieve these credentials using secure protocols and APIs while the data is protected at rest. Here, we provide implementation details for the service, describe the available APIs and SDKs, and discuss integration with Galaxy as a use case.

Custos Secrets: a Service for Managing User-Provided Resource Credential Secrets for Science Gateways

Cloud computing is a common platform for delivering software to end users. However, the process of making complex-to-deploy applications available across different cloud providers requires isolated and uncoordinated application-specific solutions, often locking-in developers to a particular cloud provider. Here, we present the CloudLaunch application as a uniform platform for discovering and deploying applications for different cloud providers. CloudLaunch allows arbitrary applications to be added to a catalog with each application having its own customizable user interface and control over the launch process, while preserving cloud-agnosticism so that authors can easily make their applications available on multiple clouds with minimal effort. It then provides a uniform interface for launching available applications by end users across different cloud providers. Architecture details are presented along with examples of different deployable applications that highlight architectural features.

CloudLaunch: Discover and Deploy Cloud Applications

Jetstream (NSF Award 1445604) Year Program Year 2 Annual Report (Dec 1, 2015 – Nov 30, 2016)

The paper presents new approach to the analysis of the time required for rendering, depending on the complexity of the 3D object. Ray tracing is used to measure program complexity measure parameter based on the rendering time. Parametric analysis using ray tracing program is achieved by correlation between image parameters and rendering time. Electronic imaging and image processing (rendering) are considered in the 3D virtual photorealistic imaging design and electronic imaging creation and optimization. We are attempting to treat the complexity as a measure in the process of creating and rendering a complex image. The work is directed towards defining and optimizing the rendering time in the photorealistic 3D image creation process. The correlation between large numbers of image components obtained by the ray tracing program is used to measure the scene complexity regarding of render time. To discover which space variable to use as complexity measure, we cross-correlated variables output by ray tracing program, keeping track of the maximum values. Based on this method, the highest correlation was observed on variable Bounding box succeeded tests, and that way obtained the complexity measure for the image. That way the original research is performed in creating analytical (data-visual) methods for determining the image rendering parameters. The results based on experimental verification on a large number of performed standard tests on real sets of graphical contents, and established the impact of graphical content complexity to the rendering time at distributed cluster computing systems.

Impact of 3D graphic structure complexity to the rendering time

For many years Apache Hadoop has been used as a synonym for processing data in the MapReduce fashion. However, due to the complexity of developing MapReduce applications, adoption of this paradigm in genetics has been limited. To alleviate some of the issues, we have previously developed Cloudfl ow - a high-level pipeline framework that allows users to create sophisticated biomedical pipelines using predefined code blocks while the framework automatically translates those into the MapReduce execution model. With the introduction of the YARN resource management layer, new computational processing models such as Apache Spark are now plugable into the Hadoop ecosystem. In this paper we describe the extension of Cloudfl ow to support Apache Spark without any adaptions to already implemented pipelines. The described performance evaluation demonstrates that Spark can bring an additional boost for analysing next generation sequencing (NGS) data to the field of genetics. The Cloudflow framework is open source and freely available at https://github.com/genepi/cloud flow.

/pdf/cloudflow-enabling-faster-biomedical-pipelines-with-pp5dokqyoi.pdf

Enis Afgan

Papers

Custos Secrets: a Service for Managing User-Provided Resource Credential Secrets for Science Gateways

CloudLaunch: Discover and Deploy Cloud Applications

Jetstream (NSF Award 1445604) Year Program Year 2 Annual Report (Dec 1, 2015 – Nov 30, 2016)

Impact of 3D graphic structure complexity to the rendering time

Cloudflow - enabling faster biomedical pipelines with MapReduce and Spark