The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update
Enis Afgan,Dannon Baker,Bérénice Batut,Marius van den Beek,Dave Bouvier,Martin Čech,John Chilton,Dave Clements,Nate Coraor,Björn Grüning,Aysam Guerler,Jennifer Hillman-Jackson,Saskia Hiltemann,Vahid Jalili,Helena Rasche,Nicola Soranzo,Jeremy Goecks,James Taylor,Anton Nekrutenko,Daniel Blankenberg +19 more
TLDR
Improvements to Galaxy's core framework, user interface, tools, and training materials enable Galaxy to be used for analyzing tens of thousands of datasets, and >5500 tools are now available from the Galaxy ToolShed.Abstract:
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.read more
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IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era.
Bui Quang Minh,Heiko A. Schmidt,Olga Chernomor,Dominik Schrempf,Dominik Schrempf,Michael D. Woodhams,Arndt von Haeseler,Arndt von Haeseler,Robert Lanfear +8 more
TL;DR: Some notable features of IQ-TREE version 2 are described and the key advantages over other software are highlighted.
Journal ArticleDOI
g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).
TL;DR: G:Profiler is now capable of analysing data from any organism, including vertebrates, plants, fungi, insects and parasites, and the 2019 update introduces an extensive technical rewrite making the services faster and more flexible.
Journal ArticleDOI
The nf-core framework for community-curated bioinformatics pipelines.
Philip Ewels,Alexander Peltzer,Sven Fillinger,Harshil Patel,Johannes Alneberg,Andreas Wilm,Maxime Garcia,Paolo Di Tommaso,Sven Nahnsen +8 more
TL;DR: The nf-core framework is introduced as a means for the development of collaborative, peerreviewed, best-practice analysis pipelines that can be used across all institutions and research facilities and introduces a higher degree of portability as compared to custom in-house scripts.
Journal ArticleDOI
Multi-omics Data Integration, Interpretation, and Its Application.
TL;DR: This review collected the tools and methods that adopt integrative approach to analyze multiple omics data and summarized their ability to address applications such as disease subtyping, biomarker prediction, and deriving insights into the data.
Journal ArticleDOI
Sustainable data analysis with Snakemake.
Felix Mölder,Kim Philipp Jablonski,Kim Philipp Jablonski,Brice Letcher,Michael B Hall,Christopher Tomkins-Tinch,Christopher Tomkins-Tinch,Vanessa Sochat,Jan Forster,Jan Forster,Soohyun Lee,Sven Twardziok,Alexander Kanitz,Alexander Kanitz,Andreas Wilm,Manuel Holtgrewe,Sven Rahmann,Sven Nahnsen,Johannes Köster,Johannes Köster +19 more
TL;DR: It is shown how the popular workflow management system Snakemake can be used to guarantee reproducibility, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.
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