The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

Grüning, Björn; Fallmann, Jörg; Yusuf, Dilmurat; Will, Sebastian; Erxleben, Anika; Eggenhofer, Florian; Houwaart, Torsten; Batut, Bérénice; Videm, Pavankumar; Bagnacani, Andrea; Wolfien, Markus; Lott, Steffen C.; Hoogstrate, Youri; Hess, Wolfgang R.; Hoffmann, Steve; Akalin, Altuna; Ohler, Uwe; Stadler, Peter F.; Backofen, Rolf

doi:10.1093/nar/gkx409

Cited by 40 publications

(25 citation statements)

References 29 publications

(39 reference statements)

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“…For example, the main Galaxy server [ 9 ] integrates many genomic tools, and the few integrated metagenomics tools such as Kraken [ 10 ] or VSearch [ 11 ] have been showcased in the published windshield splatter analysis [ 12 ]. The tools can also be selected and combined to build Galaxy flavors focusing on specific type of analysis, for example, the Galaxy RNA workbench [ 13 ] or the specialized Galaxy server of the Huttenhower lab [ 14 ]. However, none of these solutions is dedicated to microbiota data analysis in general and with the community-standard tools.…”

Section: Resultsmentioning

confidence: 99%

ASaiM: a Galaxy-based framework to analyze microbiota data

et al. 2018

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BackgroundNew generations of sequencing platforms coupled to numerous bioinformatics tools have led to rapid technological progress in metagenomics and metatranscriptomics to investigate complex microorganism communities. Nevertheless, a combination of different bioinformatic tools remains necessary to draw conclusions out of microbiota studies. Modular and user-friendly tools would greatly improve such studies.FindingsWe therefore developed ASaiM, an Open-Source Galaxy-based framework dedicated to microbiota data analyses. ASaiM provides an extensive collection of tools to assemble, extract, explore, and visualize microbiota information from raw metataxonomic, metagenomic, or metatranscriptomic sequences. To guide the analyses, several customizable workflows are included and are supported by tutorials and Galaxy interactive tours, which guide users through the analyses step by step. ASaiM is implemented as a Galaxy Docker flavour. It is scalable to thousands of datasets but also can be used on a normal PC. The associated source code is available under Apache 2 license at https://github.com/ASaiM/framework and documentation can be found online (http://asaim.readthedocs.io).ConclusionsBased on the Galaxy framework, ASaiM offers a sophisticated environment with a variety of tools, workflows, documentation, and training to scientists working on complex microorganism communities. It makes analysis and exploration analyses of microbiota data easy, quick, transparent, reproducible, and shareable.

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Section: Resultsmentioning

confidence: 99%

ASaiM: a Galaxy-based framework to analyze microbiota data

et al. 2018

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“…Recently, the 'RNA workbench' extension of Galaxy was published [113], which features many of the approaches outlined here. This enables their high-throughput application in sophisticated (partially pre-defined) workflows for non-expert scientists [114].…”

Section: Discussionmentioning

confidence: 99%

Structure and Interaction Prediction in Prokaryotic RNA Biology

2018

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Many years of research in RNA biology have soundly established the importance of RNA-based regulation far beyond most early traditional presumptions. Importantly, the advances in "wet" laboratory techniques have produced unprecedented amounts of data that require efficient and precise computational analysis schemes and algorithms. Hence, many methods that attempt topological and functional classification of novel putative RNA-based regulators are available. In this review, we technically outline thermodynamics-based standard RNA secondary structure and RNA-RNA interaction prediction approaches that have proven valuable to the RNA research community in the past and present. For these, we highlight their usability with a special focus on prokaryotic organisms and also briefly mention recent advances in whole-genome interactomics and how this may influence the field of predictive RNA research.

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“…Currently, there are many competing methods for the analysis of information obtained from high-throughput sequencing, as well as for the integration of different data types. There is a need for standardization of tools and pipelines that are also accessible to researchers outside the immediate bioinformatics community (Gruning et al 2017). In addition, standard methods to examine the degree of DNA methylation attributable to cell heterogeneity are based on a subset of clearly defined cell types from healthy donors (Cardenas et al 2016).…”

Section: Challenges Of Epigenetic and Omic Approachesmentioning

confidence: 99%

Developmental programming of the HPA axis and related behaviours: epigenetic mechanisms

Matthews

McGowan

2019

Journal of Endocrinology

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It has been approximately 30 years since the seminal discoveries of David Barker and his colleagues, and research is beginning to unravel the mechanisms that underlie developmental programming. The early environment of the embryo, foetus and newborn have been clearly linked to altered hypothalamic–pituitary–adrenal (HPA) function and related behaviours through the juvenile period and into adulthood. A number of recent studies have shown that these effects can pass across multiple generations. The HPA axis is highly responsive to the environment, impacts both central and peripheral systems and is critical to health in a wide variety of contexts. Mechanistic studies in animals are linking early exposures to adversity with changes in gene regulatory mechanisms, including modifications of DNA methylation and altered levels of miRNA. Similar associations are emerging from recent human studies. These findings suggest that epigenetic mechanisms represent a fundamental link between adverse early environments and developmental programming of later disease. The underlying biological mechanisms that connect the perinatal environment with modified long-term health outcomes represent an intensive area of research. Indeed, opportunities for early interventions must identify the relevant environmental factors and their molecular targets. This new knowledge will likely assist in the identification of individuals who are at risk of developing poor outcomes and for whom early intervention is most effective.

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The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

Cited by 40 publications

References 29 publications

ASaiM: a Galaxy-based framework to analyze microbiota data

ASaiM: a Galaxy-based framework to analyze microbiota data

Structure and Interaction Prediction in Prokaryotic RNA Biology

Developmental programming of the HPA axis and related behaviours: epigenetic mechanisms

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