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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. 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.

Slides for the "Supporting the COVID-19 Data portal: viral data cleaning from human reads and submission to ENA” session of the 2nd Galaxy-ELIXIR webinar series: Open Data Infrastructures to tackle COVID-19 pandemic, organized in January/February 2021.

Background The ability of a genotype to produce different phenotypes according to its surrounding environment is known as phenotypic plasticity. Within different individuals of the same species, phenotypic plasticity can vary greatly. This contrasting response is caused by gene-by-environment interactions (GxE). Understanding GxE interactions is particularly important in agronomy, since selected breeds and varieties may have divergent phenotypes according to their growing environment. Industrial microbes such as Saccharomyces cerevisiae are also faced with a large range of fermentation conditions that affect their technological properties. Finding the molecular determinism of such variations is a critical task for better understanding the genetic bases of phenotypic plasticity and can also be helpful in order to improve breeding methods. Results In this study we implemented a QTL mapping program using two independent cross (~ 100 progeny) in order to investigate the molecular basis of yeast phenotypic response in a wine fermentation context. Thanks to whole genome sequencing approaches, both crosses were genotyped, providing saturated genetic maps of thousands of markers. Linkage analyses allowed the detection of 78 QTLs including 21 with significant interaction with the environmental conditions. Molecular dissection of a major QTL demonstrated that the sulfite pump Ssu1p has a pleiotropic effect and impacts the phenotypic plasticity of several traits. Conclusions The detection of QTLs and their interactions with environment emphasizes the complexity of yeast industrial traits. The validation of the interaction of SSU1 allelic variants with the nature of the fermented juice increases knowledge about the impact of the sulfite pump during fermentation. All together these results pave the way for exploiting and deciphering the genetic determinism of phenotypic plasticity. Electronic supplementary material The online version of this article (10.1186/s12864-018-5145-4) contains supplementary material, which is available to authorized users.

Figure S4. Linkage between translocation markers in M2xF15 and SBxGN crosses. The data represented are the number of segregant according to their genotype for the marker next to the chromosomal break point. Genotypes A strong linkage is shown with less than 10% recombinants. (PDF 4 kb)

Figure S6. Variance explained by QTL according to cross. Each dot represent a QTL. Bigger points indicate average. There is no significant difference between the two cross (Wilcoxon test pval > 0,05). (PDF 721 kb)

Figure S5. Variation of QTL effect according to condition. For each QTL, the values shown are the difference between the phenotypic values measured for all the segregant that inherited the allele of SB or M2 minus those that inherited from GN or F15. A star means a significant difference (Wilcoxon, α = 0.05). (PDF 25 kb)

Figure S7. Impact of QTL XV_M2xF15_26284 on V50_80. Panel A. The norm of reaction of the segregants that inherited from M2 or F15 for QTL XV_M2xF15_26284. Dashed lines show the reaction norm of each segregant according to their allele inheritance. Full lines show average value of the all the segregant according to marker inheritance. Panel B. Enrichment of strains having inherited the F15 allele in cluster 2. Norm of reaction of each individual is shown in dotted line and faceted according to their cluster. M2xF15 progeny clones are colored according to their allele inheritance at XV_M2xF15_26284 (red for F15 and blue for M2). A grey solid line shown the average norm of reaction of each cluster. Number of M2xF15 progeny clones within each cluster is indicated by n=; the first number indicate the number of strains from that inherit from M2 and the second one from strain that inherit from F15. A star means a disequilibrium in the theoretical homogeneous distribution 0.5 / 0.5 chis square test, α = 0.05). Panel C. Coverage difference between M2 and F15 strains within genomic region of QTL XV_M2xF15_26284. (PDF 154 kb)

Figure S3. Heritability is correlated to the number of QTLs detected. The data represented are the number of QTLs identified according by cross and by trait according to the average heritability by cross and by trait among the three conditions. (PDF 21 kb)

Figure S1. Meiosis emphases phenotypic novelty. Panel A. PCA of winemaking properties of M2xF15 and SBxGN progenies and 31 CWS in M15. Panel B. Correlation circle indicating the correlation of the variables for axes 1 and 2. Panel C. Average phenotypic distance computed from M15 condition for CWS, and the two M2xF15 and SBxGN cross. (PDF 455 kb)

Figure S2. Clustering of Norm of reaction for each trait. Norm of reaction of each individual is shown in dotted line and are colored and faceted according their cluster. Solid line shown the average norm of reaction of each cluster. Number of strains within each cluster is indicated by n. (PDF 2768 kb)

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.