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1. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update

Author

Ostrovsky, AE, Mahmoud, A, Lonie, AJ, Syme, A, Fouilloux, A, Bretaudeau, A, Nekrutenko, A, Kumar, A, Eschenlauer, AC, DeSanto, AD, Guerler, A, Serrano-Solano, B, Batut, B, Gruening, BA, Langhorst, BW, Carr, B, Raubenolt, BA, Hyde, CJ, Bromhead, CJ, Barnett, CB, Royaux, C, Gallardo, C, Blankenberg, D, Fornika, DJ, Baker, D, Bouvier, D, Clements, D, Morais, DADL, Tabernero, DL, Lariviere, D, Nasr, E, Afgan, E, Zambelli, F, Heyl, F, Psomopoulos, F, Coppens, F, Price, GR, Cuccuru, G, Le Corguille, G, Von Kuster, G, Akbulut, GG, Rasche, H, Hans-Rudolf, H, Eguinoa, I, Makunin, I, Ranawaka, IJ, Taylor, JP, Joshi, J, Hillman-Jackson, J, Goecks, J, Chilton, JM, Kamali, K, Suderman, K, Poterlowicz, K, Yvan, LB, Lopez-Delisle, L, Sargent, L, Bassetti, ME, Tangaro, MA, van den Beek, M, Cech, M, Bernt, M, Fahrner, M, Tekman, M, Foell, MC, Schatz, MC, Crusoe, MR, Roncoroni, M, Kucher, N, Coraor, N, Stoler, N, Rhodes, N, Soranzo, N, Pinter, N, Goonasekera, NA, Moreno, PA, Videm, P, Melanie, P, Mandreoli, P, Jagtap, PD, Gu, Q, Weber, RJM, Lazarus, R, Vorderman, RHP, Hiltemann, S, Golitsynskiy, S, Garg, S, Bray, SA, Gladman, SL, Leo, S, Mehta, SP, Griffin, TJ, Jalili, V, Yves, V, Wen, V, Nagampalli, VK, Bacon, WA, de Koning, W, Maier, W, Briggs, PJ, Ostrovsky, AE, Mahmoud, A, Lonie, AJ, Syme, A, Fouilloux, A, Bretaudeau, A, Nekrutenko, A, Kumar, A, Eschenlauer, AC, DeSanto, AD, Guerler, A, Serrano-Solano, B, Batut, B, Gruening, BA, Langhorst, BW, Carr, B, Raubenolt, BA, Hyde, CJ, Bromhead, CJ, Barnett, CB, Royaux, C, Gallardo, C, Blankenberg, D, Fornika, DJ, Baker, D, Bouvier, D, Clements, D, Morais, DADL, Tabernero, DL, Lariviere, D, Nasr, E, Afgan, E, Zambelli, F, Heyl, F, Psomopoulos, F, Coppens, F, Price, GR, Cuccuru, G, Le Corguille, G, Von Kuster, G, Akbulut, GG, Rasche, H, Hans-Rudolf, H, Eguinoa, I, Makunin, I, Ranawaka, IJ, Taylor, JP, Joshi, J, Hillman-Jackson, J, Goecks, J, Chilton, JM, Kamali, K, Suderman, K, Poterlowicz, K, Yvan, LB, Lopez-Delisle, L, Sargent, L, Bassetti, ME, Tangaro, MA, van den Beek, M, Cech, M, Bernt, M, Fahrner, M, Tekman, M, Foell, MC, Schatz, MC, Crusoe, MR, Roncoroni, M, Kucher, N, Coraor, N, Stoler, N, Rhodes, N, Soranzo, N, Pinter, N, Goonasekera, NA, Moreno, PA, Videm, P, Melanie, P, Mandreoli, P, Jagtap, PD, Gu, Q, Weber, RJM, Lazarus, R, Vorderman, RHP, Hiltemann, S, Golitsynskiy, S, Garg, S, Bray, SA, Gladman, SL, Leo, S, Mehta, SP, Griffin, TJ, Jalili, V, Yves, V, Wen, V, Nagampalli, VK, Bacon, WA, de Koning, W, Maier, W, and Briggs, PJ

Abstract

Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

Published
2022

2. Community-driven ELIXIR activities in single-cell omics [version 1; peer review: awaiting peer review]

Author

Czarnewski, P., Mahfouz, Ahmed, Calogero, R. A., Palagi, P. M., Portell-Silva, L., Gonzalez-Uriarte, A., Soneson, C., Burdett, T., Szomolay, B., Videm, P., Hotz, H. R., Papatheodorou, I., Hancock, J. M., Gruening, B., Haerty, W., Krause, Roland, Capella-Gutierrez, S., Lesko?ek, B., Alessandri, L., Arigoni, M., Rezen, T., Botzki, A., Ferk, P., Lindvall, J., Heil, Katharina, Ishaque, N., Korpelainen, E., Czarnewski, P., Mahfouz, Ahmed, Calogero, R. A., Palagi, P. M., Portell-Silva, L., Gonzalez-Uriarte, A., Soneson, C., Burdett, T., Szomolay, B., Videm, P., Hotz, H. R., Papatheodorou, I., Hancock, J. M., Gruening, B., Haerty, W., Krause, Roland, Capella-Gutierrez, S., Lesko?ek, B., Alessandri, L., Arigoni, M., Rezen, T., Botzki, A., Ferk, P., Lindvall, J., Heil, Katharina, Ishaque, N., and Korpelainen, E.

Published
2022

3. The RNA workbench 2.0: next generation RNA data analysis

Author

Fallmann, J, Videm, P, Bagnacani, A, Batut, B, Doyle, MA, Klingstrom, T, Eggenhofer, F, Stadler, PF, Backofen, R, Gruening, B, Fallmann, J, Videm, P, Bagnacani, A, Batut, B, Doyle, MA, Klingstrom, T, Eggenhofer, F, Stadler, PF, Backofen, R, and Gruening, B

Abstract

RNA has become one of the major research topics in molecular biology. As a central player in key processes regulating gene expression, RNA is in the focus of many efforts to decipher the pathways that govern the transition of genetic information to a fully functional cell. As more and more researchers join this endeavour, there is a rapidly growing demand for comprehensive collections of tools that cover the diverse layers of RNA-related research. However, increasing amounts of data, from diverse types of experiments, addressing different aspects of biological questions need to be consolidated and integrated into a single framework. Only then is it possible to connect findings from e.g. RNA-Seq experiments and methods for e.g. target predictions. To address these needs, we present the RNA Workbench 2.0 , an updated online resource for RNA related analysis. With the RNA Workbench we created a comprehensive set of analysis tools and workflows that enables researchers to analyze their data without the need for sophisticated command-line skills. This update takes the established framework to the next level, providing not only a containerized infrastructure for analysis, but also a ready-to-use platform for hands-on training, analysis, data exploration, and visualization. The new framework is available at https://rna.usegalaxy.eu , and login is free and open to all users. The containerized version can be found at https://github.com/bgruening/galaxy-rna-workbench.

Published
2019

4. Community-Driven Data Analysis Training for Biology

Author

Batut, B. (Bérénice), Hiltemann, S. (Saskia), Bagnacani, A. (Andrea), Baker, D. (Dannon), Bhardwaj, V. (Vivek), Blank, C. (Clemens), Bretaudeau, A. (Anthony), Brillet-Guéguen, L. (Loraine), Čech, M. (Martin), Chilton, J. (John), Clements, D. (Dave), Doppelt-Azeroual, O. (Olivia), Erxleben, A. (Anika), Freeberg, M.A. (Mallory Ann), Gladman, S. (Simon), Hoogstrate, Y. (Youri), Hotz, H.-R. (Hans-Rudolf), Houwaart, T. (Torsten), Jagtap, P. (Pratik), Larivière, D. (Delphine), Le Corguillé, G. (Gildas), Manke, T. (Thomas), Mareuil, F. (Fabien), Ramírez, F. (Fidel), Ryan, D. (Devon), Sigloch, F.C. (Florian Christoph), Soranzo, N. (Nicola), Wolff, J. (Joachim), Videm, P. (Pavankumar), Wolfien, M. (Markus), Wubuli, A. (Aisanjiang), Yusuf, D. (Dilmurat), Taylor, J. (James), Backofen, R. (Rolf), Nekrutenko, A. (Anton), Grüning, B. (Björn), Batut, B. (Bérénice), Hiltemann, S. (Saskia), Bagnacani, A. (Andrea), Baker, D. (Dannon), Bhardwaj, V. (Vivek), Blank, C. (Clemens), Bretaudeau, A. (Anthony), Brillet-Guéguen, L. (Loraine), Čech, M. (Martin), Chilton, J. (John), Clements, D. (Dave), Doppelt-Azeroual, O. (Olivia), Erxleben, A. (Anika), Freeberg, M.A. (Mallory Ann), Gladman, S. (Simon), Hoogstrate, Y. (Youri), Hotz, H.-R. (Hans-Rudolf), Houwaart, T. (Torsten), Jagtap, P. (Pratik), Larivière, D. (Delphine), Le Corguillé, G. (Gildas), Manke, T. (Thomas), Mareuil, F. (Fabien), Ramírez, F. (Fidel), Ryan, D. (Devon), Sigloch, F.C. (Florian Christoph), Soranzo, N. (Nicola), Wolff, J. (Joachim), Videm, P. (Pavankumar), Wolfien, M. (Markus), Wubuli, A. (Aisanjiang), Yusuf, D. (Dilmurat), Taylor, J. (James), Backofen, R. (Rolf), Nekrutenko, A. (Anton), and Grüning, B. (Björn)

Abstract

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org. We developed an infrastructure that facilitates data analysis training in life sciences. It is an interactive learning platform tuned for current types of data and research problems. Importantly, it provides a means for community-wide content creation and maintenance and, finally, enables trainers and trainees to use the tutorials in a variety of situations, such as those where reliable Internet access is unavailable.

Published
2018
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5. Community-Driven Data Analysis Training for Biology

Author

Batut, B, Hiltemann, Saskia, Bagnacani, A, Baker, D, Bhardwaj, V, Blank, C, Bretaudeau, A, Brillet-Gueguen, L, Cech, M, Chilton, J, Clements, D, Doppelt-Azeroual, O, Erxleben, A, Freeberg, MA, Gladman, S, Hoogstrate, Youri, Hotz, HR, Houwaart, T, Jagtap, P, Lariviere, D, Le Corguille, G, Manke, T, Mareuil, F, Ramirez, F, Ryan, D, Sigloch, FC, Soranzo, N, Wolff, J, Videm, P, Wolfien, M, Wubuli, A, Yusuf, D, Taylor, J, Backofen, R, Nekrutenko, A, Gruning, B, Batut, B, Hiltemann, Saskia, Bagnacani, A, Baker, D, Bhardwaj, V, Blank, C, Bretaudeau, A, Brillet-Gueguen, L, Cech, M, Chilton, J, Clements, D, Doppelt-Azeroual, O, Erxleben, A, Freeberg, MA, Gladman, S, Hoogstrate, Youri, Hotz, HR, Houwaart, T, Jagtap, P, Lariviere, D, Le Corguille, G, Manke, T, Mareuil, F, Ramirez, F, Ryan, D, Sigloch, FC, Soranzo, N, Wolff, J, Videm, P, Wolfien, M, Wubuli, A, Yusuf, D, Taylor, J, Backofen, R, Nekrutenko, A, and Gruning, B

Published
2018

6. The RNA workbench: Best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

Author

Grüning, B.A. (Björn A.), Fallmann, J. (Jörg), Yusuf, D. (Dilmurat), Will, S. (Sebastian), Erxleben, A. (Anika), Eggenhofer, F. (Florian), Houwaart, T. (Torsten), Batut, B. (Bérénice), Videm, P. (Pavankumar), Bagnacani, A. (Andrea), Wolfien, M. (Markus), Lott, S.C. (Steffen C.), Hoogstrate, Y. (Youri), Hess, W.R. (Wolfgang R.), Wolkenhauer, O. (Olaf), Hoffmann, S. (Steve), Akalin, A. (Altuna), Ohler, U. (Uwe), Stadler, P.F. (Peter F.), Backofen, R. (Rolf), Grüning, B.A. (Björn A.), Fallmann, J. (Jörg), Yusuf, D. (Dilmurat), Will, S. (Sebastian), Erxleben, A. (Anika), Eggenhofer, F. (Florian), Houwaart, T. (Torsten), Batut, B. (Bérénice), Videm, P. (Pavankumar), Bagnacani, A. (Andrea), Wolfien, M. (Markus), Lott, S.C. (Steffen C.), Hoogstrate, Y. (Youri), Hess, W.R. (Wolfgang R.), Wolkenhauer, O. (Olaf), Hoffmann, S. (Steve), Akalin, A. (Altuna), Ohler, U. (Uwe), Stadler, P.F. (Peter F.), and Backofen, R. (Rolf)

Abstract

RNA-based regulation has become a major research topic in molecular biology. The analysis of epigenetic and expression data is therefore incomplete if RNA-based regulation is not taken into account. Thus, it is increasingly important but not yet standard to combine RNA-centric data and analysis tools with other types of experimental data such as RNA-seq or ChIP-seq. Here, we present the RNA workbench, a comprehensive set of analysis tools and consolidated workflows that enable the researcher to combine these two worlds. Based on the Galaxy framework the workbench guarantees simple access, easy extension, flexible adaption to personal and security needs, and sophisticated analyses that are independent of command-line knowledge. Currently, it includes more than 50 bioinformatics tools that are dedicated to different research areas of RNA biology including RNA structure analysis, RNA alignment, RNA annotation, RNA-protein interaction, ribosome profiling, RNA-seq analysis and RNA target prediction. The workbench is developed and maintained by experts in RNA bioinformatics and the Galaxy framework. Together with the growing community evolving around this workbench, we are committed to keep the workbench up-to-date for future standards and needs, providing researchers with a reliable and robust framework for RNA data analysis.

Published
2017
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7. The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

Author

Gruning, BA, Fallmann, J, Yusuf, D, Will, S, Erxleben, A, Eggenhofer, F, Houwaart, T, Batut, B, Videm, P, Bagnacani, A, Wolfien, M, Lott, SC, Hoogstrate, Youri, Hess, WR, Wolkenhauer, O, Hoffmann, S, Akalin, A, Ohler, U, Stadler, PF, Backofen, R, Gruning, BA, Fallmann, J, Yusuf, D, Will, S, Erxleben, A, Eggenhofer, F, Houwaart, T, Batut, B, Videm, P, Bagnacani, A, Wolfien, M, Lott, SC, Hoogstrate, Youri, Hess, WR, Wolkenhauer, O, Hoffmann, S, Akalin, A, Ohler, U, Stadler, PF, and Backofen, R

Published
2017

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