1. VT iPlant Workshop September 12-14, 2011 The focus of the workshop was incorporation of tools into the DE and/or Atmosphere to facilitate a systems biology-based integration and analysis of three sets of omics data from VT. Usability study conducted. Attendees: VT faculty and students, Dan, Matt, Fusheng, Ray, Christos, Nick Provart, Bjoern Usadel

2. Goals of the VT iPlant Workshop 1 1. Using RNA Seq, microarray, and metabolomics data, related to plant stress responses provided by VT research groups, upload, and subsequently implement, tools in iPlant’s Discovery Environment and/or in Atmosphere to “mine for meaning” in the oceans of omics data provided. 2. What did we discover? What might we discover? 1. Using RNA Seq, microarray, and metabolomics data, related to plant stress responses provided by VT research groups, upload, and subsequently implement, tools in iPlant’s Discovery Environment and/or in Atmosphere to “mine for meaning” in the oceans of omics data provided. 2. What did we discover? What might we discover?

3. Goals of the VT iPlant Workshop 2 2. To determine whether the DE is “naïve-user” friendly by having Plant Biology graduate students and faculty attempt to conduct omics analyses there.

4. Plant Biologists (“Naïve Users”) Eva Collakova and Yihu Fang (NSF grant) John McDowell and Kevin Fedkenheuer Guillaume Pilot and Yu Shi Jim Westwood and Gunjune Kim Jason Holliday Ruth Grene Eva Collakova and Yihu Fang (NSF grant) John McDowell and Kevin Fedkenheuer Guillaume Pilot and Yu Shi Jim Westwood and Gunjune Kim Jason Holliday Ruth Grene

5. Bioinformatics Grad Student Heroes (No Grad Students, No Workshop) Kuan Yang Elijah Myers Haktan Suran Akshay Kakumanu Curtis Klumas Kuan Yang Elijah Myers Haktan Suran Akshay Kakumanu Curtis Klumas

6. Tools Under Study *MapMan/Robin. Statistical analysis of microarray data, and visualization of pathway enrichment for metabolites, transcripts, and peptides from omics datasets http://mapman.mpimp-golm.mpg.de/ (Dr. B. Usadel).http://mapman.mpimp-golm.mpg.de/ *Ontologizer. GO enrichment and visualization http://compbio.charite.de/index.php/ontologizer2.html http://compbio.charite.de/index.php/ontologizer2.html *GeneMania. (available at BAR, Dr. N. Provart) Network inference and visualization. http://www.genemania.org AtSubP Prediction of subcellular location in Arabidopsis. http://bioinfo3.noble.org/AtSubP/index.html and ePlant (Dr. N. Provart) http://bioinfo3.noble.org/AtSubP/index.html *Bayesian Biclustering to identify clusters of genes showing common patterns of expression over time. http://www.people.fas.harvard.edu/~junliu/BBC/ http://www.people.fas.harvard.edu/~junliu/BBC/ SBGN Beacon Stress signaling pathway project)** http://vanted.ipk-gatersleben.de/addons/sbgn-edu * In DE or Atmosphere ** Separately funded by NSF *MapMan/Robin. Statistical analysis of microarray data, and visualization of pathway enrichment for metabolites, transcripts, and peptides from omics datasets http://mapman.mpimp-golm.mpg.de/ (Dr. B. Usadel).http://mapman.mpimp-golm.mpg.de/ *Ontologizer. GO enrichment and visualization http://compbio.charite.de/index.php/ontologizer2.html http://compbio.charite.de/index.php/ontologizer2.html *GeneMania. (available at BAR, Dr. N. Provart) Network inference and visualization. http://www.genemania.org AtSubP Prediction of subcellular location in Arabidopsis. http://bioinfo3.noble.org/AtSubP/index.html and ePlant (Dr. N. Provart) http://bioinfo3.noble.org/AtSubP/index.html *Bayesian Biclustering to identify clusters of genes showing common patterns of expression over time. http://www.people.fas.harvard.edu/~junliu/BBC/ http://www.people.fas.harvard.edu/~junliu/BBC/ SBGN Beacon Stress signaling pathway project)** http://vanted.ipk-gatersleben.de/addons/sbgn-edu * In DE or Atmosphere ** Separately funded by NSF

7. Data Integration and Hypothesis Generation Gene expression data Visually identified responsive genes and metabolites mapped on to pathways Visualization Visually-identified enriched pathways Cell-based,inferred regulatory networks Interactive analysis of single or multiple time point data in DE (e.g., Co-expression analysis, Ontologizer, MapMan, Cytoscape/Gene Mania, BAR tools, Bi-clustering) Experiments: Omics data collected, stats analysis completed, inc. RNA-Seq analysis. Experiments: Omics data collected, stats analysis completed, inc. RNA-Seq analysis. Metabolite Data Identify sub-cellular locations of gene products, (e.g., GO, SUBA, Interactome, SubP) /rg/ber/lz Testable Hypotheses New Experiments Phenotypic Data Protein data (Y2H, proteomics) Input Data THINK, THINK, THINK

8. What regulatory pathways operate in Arabidopsis in hostile environments? (Kilian et al,. 2007). What is the mechanistic basis for observed early embryo abortions in fertilized maize and rice ovaries that occur under levels of drought stress that have less drastic effects on vegetative tissue, where the tissue survives the imposed stress? (Pereira, Grene). Does the transcriptome and metabolome of Sitka spruce vary in terms of temporal expression, constituents, and network architecture among populations adapted to different climatic regimes? (Holliday) What regulatory pathways operate in Arabidopsis in hostile environments? (Kilian et al,. 2007). What is the mechanistic basis for observed early embryo abortions in fertilized maize and rice ovaries that occur under levels of drought stress that have less drastic effects on vegetative tissue, where the tissue survives the imposed stress? (Pereira, Grene). Does the transcriptome and metabolome of Sitka spruce vary in terms of temporal expression, constituents, and network architecture among populations adapted to different climatic regimes? (Holliday) VT iPlant Workshop September 12-14, 2011 Biological Questions Presented (source of data) VT iPlant Workshop September 12-14, 2011 Biological Questions Presented (source of data)

9. A Stress Data Set 1. Different stresses, two tissues, (root and shoot), time course series, 30 minutes to 24 hours. Some stresses applied to roots, some to shoots (Arabidopsis. Kilian et al, 2007 data). 2. Data have been published, but only a superficial analysis included. 3. Data have been entered into tool set available at BAR (Bio- Array Resource, University of Toronto, Nick Provart Group). 4. What more can we learn from further analysis of the data, using our workflow approach? 1. Different stresses, two tissues, (root and shoot), time course series, 30 minutes to 24 hours. Some stresses applied to roots, some to shoots (Arabidopsis. Kilian et al, 2007 data). 2. Data have been published, but only a superficial analysis included. 3. Data have been entered into tool set available at BAR (Bio- Array Resource, University of Toronto, Nick Provart Group). 4. What more can we learn from further analysis of the data, using our workflow approach?

10. RootShoot (negLog 10 P) 0 2 4 Enriched GO terms for salt stress responses in Arabidopsis roots and shoots Ontologizer used for enrichment, heat map drawn subsequently

11. Data Set: RNA-Seq data, currently available. Tuxedo suite of tools employed. Desired End Product: A network displaying novel, tissue-specific, inferred connections among genomic events, as revealed by deep sequencing, and associated regulatory mechanisms that may give rise to the susceptibility of early maize and rice embryos to drought stress. Data Set: RNA-Seq data, currently available. Tuxedo suite of tools employed. Desired End Product: A network displaying novel, tissue-specific, inferred connections among genomic events, as revealed by deep sequencing, and associated regulatory mechanisms that may give rise to the susceptibility of early maize and rice embryos to drought stress. What is the mechanistic basis for observed early embryo abortions in fertilized maize and rice ovaries that occur under levels of drought stress that have less drastic effects on vegetative tissue, where the tissue survives the imposed stress? (Pereira, Grene).

12. Results from applying PAGE, a GO enrichment algorithm to results from RNA-Seq data (maize drought experiment) RNA-Seq data analyzed with Tuxedo Suite tools, with some modifications RNA-Seq data analyzed with Tuxedo Suite tools, with some modifications

13. Conclusions/Suggestions 1 1.Atmosphere is an excellent tool for the analysis of very large data sets, such as those generated in work using deep sequencing and in genomic analyses, ( for example, those conducted by evolutionary biologists, such as Jason). 2.Expert help is needed to interact with, and incorporate tools into, the DE. This was provided at the workshop by Dan, Matt et al., and the bioinformatics grad students. 3.Matt explained that the DE, in a more finished form, will include workflows. Hopefully, the means will be provided to build custom work flows in the DE, and to share those workflows with colleagues. 1.Atmosphere is an excellent tool for the analysis of very large data sets, such as those generated in work using deep sequencing and in genomic analyses, ( for example, those conducted by evolutionary biologists, such as Jason). 2.Expert help is needed to interact with, and incorporate tools into, the DE. This was provided at the workshop by Dan, Matt et al., and the bioinformatics grad students. 3.Matt explained that the DE, in a more finished form, will include workflows. Hopefully, the means will be provided to build custom work flows in the DE, and to share those workflows with colleagues.

14. Conclusions/Suggestions 2 1.It is important to provide a mechanism within the iPlant CI for giving proper credit to scientists who write tools that are used in the DE or in Atmosphere. Such documentation is essential for promotion and tenure dossiers. 2.The iPlant support desk needs to refine and optimize its response to user inquiries, especially to naïve users. 3.Matt explained that a social network tool will be added soon. Users of this network should be encouraged to provide detailed descriptions of how they used specific tools, and the degree of success that they had. This is crucial for naïve users who will need to browse the tools available within the DE and Atmosphere in order to make informed selections that are best suited to their desired biological goals. 1.It is important to provide a mechanism within the iPlant CI for giving proper credit to scientists who write tools that are used in the DE or in Atmosphere. Such documentation is essential for promotion and tenure dossiers. 2.The iPlant support desk needs to refine and optimize its response to user inquiries, especially to naïve users. 3.Matt explained that a social network tool will be added soon. Users of this network should be encouraged to provide detailed descriptions of how they used specific tools, and the degree of success that they had. This is crucial for naïve users who will need to browse the tools available within the DE and Atmosphere in order to make informed selections that are best suited to their desired biological goals.

15. Next Steps at VT 1. Jason, Haktan, Ruth, Curtis, Elijah, and Kuan are working on a manuscript presenting the results of their analysis of spruce omics data from Jason’s lab, using tools that are available either in the DE or in Atmosphere. 2. Ruth, Akshay, Madan, Curtis, and Elijah are working on a manuscript on the results of analysis of RNA-Seq data from drought-stressed maize reproductive and leaf meristem, using tools in the DE and Atmosphere. 1. Jason, Haktan, Ruth, Curtis, Elijah, and Kuan are working on a manuscript presenting the results of their analysis of spruce omics data from Jason’s lab, using tools that are available either in the DE or in Atmosphere. 2. Ruth, Akshay, Madan, Curtis, and Elijah are working on a manuscript on the results of analysis of RNA-Seq data from drought-stressed maize reproductive and leaf meristem, using tools in the DE and Atmosphere.

16. Thanks from VT! We are grateful for the opportunity to work together on important problems. Ruth, especially, is grateful to iPlant for providing a framework for our communal projects at VT, allowing us to entice our plant biology students to dip their toes into the ocean of analytical possibilities afforded by bioinformatics, cloud computing, and the sharing of resources. Thanks. Matt and Dan, for providing that introduction! Bonus: A home-grown approach to visualization of proposed interactive networks was presented. Nick and Bjoern agreed to collaborate with Ruth to automate network construction. Next year: Workflows, social networks, and lots of insights! We are grateful for the opportunity to work together on important problems. Ruth, especially, is grateful to iPlant for providing a framework for our communal projects at VT, allowing us to entice our plant biology students to dip their toes into the ocean of analytical possibilities afforded by bioinformatics, cloud computing, and the sharing of resources. Thanks. Matt and Dan, for providing that introduction! Bonus: A home-grown approach to visualization of proposed interactive networks was presented. Nick and Bjoern agreed to collaborate with Ruth to automate network construction. Next year: Workflows, social networks, and lots of insights!