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

Fueled by the radically increased capacity to generate data over the past decade, the field of biomedical research has been constrained by the ability to analyze data. Galaxy, an open genomics and biomedical research platform, has been democratizing access to data analysis tools with its effective and accessible web interface. However, the scale of data and the scope of tools required have proven to be a significant challenge for any monolithic deployment of the Galaxy application. We have found that a distributed and federated approach to utilizing compute and storage resources is necessary. This paper describes the ongoing efforts in creating a ubiquitous platform capable of simultaneously utilizing dedicated as well as on-demand cloud resources.

Galaxy cluster to cloud - genomics at scale

Background: Processing of Next-Generation Sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized post-analysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers, towards seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform. Findings: We present an approach for abstracting the complex data operations of multi-step, bioinformatics pipelines for NGS data analysis. As examples, we have deployed two pipelines for RNAseq and CHIPseq, pre-configured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines is as simple as running a single bioinformatics tool. This is achieved through a ‘meta-script’ that automatically starts the Bio-Docklets, and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface (API). The pipelne output is postprocessed using the Visual Omics Explorer (VOE) framework, providing interactive data visualizations that users can access through a web browser. Conclusions: The goal of our approach is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts, on any computing environment whether a laboratory workstation, university computer cluster, or a cloud service provider. Besides end-users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

https://www.biorxiv.org/content/biorxiv/early/2017/03/15/116962.full.pdf

Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines

Application Information Services for distributed computing environments

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.

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

Basic Local Alignment Search Tool (BLAST) is a heavily used bioinformatics application that has gotten significant attention from the high performance computing community. The authors have taken BLAST execution a step further and enabled it to execute on grid resources. Adapting BLAST to execute on the grid brings up concerns regarding grid resource heterogeneity, which inevitably cause difficulty with application availability, fault tolerance, interoperability, and variability in performance of individual segments that are being distributed throughout grid resources. In addition difficulties arise because of tools, technologies, and middleware dependencies that an application developer must deal with. This paper describes Dynamic BLAST and experiences with developing and deploying the application on grid resources over a two year period. Dynamic BLAST is a BLAST-specific metascheduler, a multithreaded, master-worker type application that handles all aspects of a BLAST job submission on the grid for the user. It was developed with the goal of bringing the grid closer to a typical scientist by eliminating the initial learning curve necessary for use of many grid applications. Associated research and development have resulted in the authors' extensive experience in dealing with grid related issues with respect to available tools and technologies. Lessons learned and suggestions are also presented in this paper.

Enis Afgan

Papers

Galaxy cluster to cloud - genomics at scale

Bio-Docklets: Virtualization Containers for Single-Step Execution of NGS Pipelines

Application Information Services for distributed computing environments

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

Experiences with developing and deploying dynamic BLAST