1. Gramene Meeting, PAG 2015 Expression Atlas - a New Resource for Baseline and Differential Gene Expression for Plants Robert Petryszak Gene Expression Team Leader Functional Genomics Group

2. Hinxton Genome Campus

3. ArrayExpress www.ebi.ac.uk/arrayexpress Expression Atlas www.ebi.ac.uk/gxa The two databases Curation, Quality Control, Ontology Annotation, Statistical analysis

4. Experimental Factor Ontology (EFO) http://www.ebi.ac.uk/efo http://www.ebi.ac.uk/efo Systematically organized experimental factor terms controlled vocabulary + hierarchy (relationship) Used in EBI databases: and external projects (e.g. NHGRI GWAS Catalogue) Combine terms from a subset of well-maintained and compatible ontologies, e.g. Gene Ontology (cellular component + biological process terms) NCBI Taxonomy UBERON Plant Ontology (~50 terms, but new terms being added regularly)

5. Expression Atlas Baseline Expression e.g. which rice genes are expressed in a healthy leaf? Differential Expression e.g. which maize genes are up-regulated in moderate drought stress versus control?

6. Plant data sets in Atlas 226 (1) 2 (2) All (Baseline) 5 (2) 9 (1) Brassica oleracea 1 -- Oryza Sativa Indica 3 -- Populus trichocarpa 2 -- Vitis Vinifera 5 --

7. Atlas Search Search Atlas by gene, organism, condition (in any combination)

8. Baseline Atlas – Gene Page Corroboration, but different FPKMs

9. Baseline Atlas

10. Baseline Atlas - Hierarchical Clustering Plots

11. Differential Atlas Sort by/display log 2 fold change

12. Differential Atlas (Experiment Page)

13. Differential Atlas (Experiment Page Plots) Gene set overlap: GO, Reactome. InterPro MA plots

14. Genome Browser View of Expression

15. Expression data in Reactome Pathway Portal

16. Annotare – A submission tool for ArrayExpress

17. Automatically add sample characteristic column: cultivar?

18. Pokkali vs. IR29 Salt stress RNA-seq analysis Points in red represent genes that are DE (FDR = 0.05) Each line corresponds to a gene (94 genes – with different DE pattern across two time points between sensitive and tolerant – are shown) Which genes have a different DE pattern across time when comparing the salt tolerant and salt sensitive plants?

19. Plans for 2015 - General Deploy good enough normalisation method within (and across different?) RNA-seq experiments Show expression variability across biological replicates Report gene co-expression in single experiment Visualise expression of orthologues Automate QC for RNA-seq experiments Gene set overlap for user provided gene sets Search, presentation and branding improvements

20. Plans for 2015 - Gramene ✓ Baseline expression summary for pathways in Plant Reactome (Pathway Portal) Baseline resource publication for Gramene Atlas workflow Differential analysis of salt stress in Pokkali and IR29 Add Brachypodium distachyon data sets to Atlas Procure more maize experiments Facilitate plant experiment submissions in Annotare …

21. Molecular Atlas Cluster Leader Expression Analysis, Statistics, RNA-seq Curation Tools, Ontology, RDF Member States Funding: Alvis Brazma Eleanor Williams Amy Tang Catherine Snow Maria Keays Nuno Fonseca Simon JuppJames Malone Tony BurdettJulie McMurray Research Guidance John Marioni Wolfgang Huber Training, Outreach Amy Tang Acknowledgements Annotare Olga Melnichuk Emma Hastings Nikolay Kolesnikov Tony Burdett Array Express Submissions, Curation Web Services Elisabet Barrera Casanova Oliver Mannion Alfonso Munoz-Pomer Fuentes Ugis Sarkans Gramene Doreen Ware Justin Preece Pankaj Jaiswal Matt Geniza Reactome Antonio Fabregat Wolfgang Huber

22. Thank You

23. The two databases: how do they compare? ArrayExpress (Archive) Expression Atlas (Value added) Central objectExperimentGene and condition Microarray data Sequencing data RNA-seq data Query for… Experiment details and associated data Gene expression under certain conditions or in contrasting sample groups Download data for further analysis Submit data X Private data Curated data Yes (direct submissions) /No (GEO-imported) All curated and annotated to EFO ontology

24. Reporting standards - MAGE-TAB format A simple spreadsheet format that uses a number of tab-delimited text files Array Design Format file Probe names, sequence, genomic mapping location ADF (microarray only) Raw and processed data files 1.fq.gz.CEL A1.CEL Normalized.txt 2.fq.gz MAGE-TAB in FGED: http://www.mged.org/mage-tab/index.htmlhttp://www.mged.org/mage-tab/index.html Investigation Description Format file Experiment title + description Submitter’s details All protocols IDF Sample Data Relationship Format file SDRF /Seq lib Hyb/seq assays Data1.txt Data2.txt * *

25. Example SDRF: workflow from samples to data

26. Example IDF: expt. info and protocols

27. Quality control Normalisation (RMA) Moderated T-test FDR-adjustment Metadata CEL files Outlier samples Gene (Adj. P-Val, Log2Fold, T-statistic) Microarray Processing Pipeline (limma) Normalised expression values per probe set SamplesDisease Sample 1normal Sample 2diabetes Sample 3diabetes Sample 4normal Sample 5normal Sample 6diabetes Manually curated “comparison” Disease: diabetes vs. normal fold-change p-value Benjamini & Hochberg Insufficient replicates

28. Quality control Low-quality reads NNACGANNNNN Mapping (TopHat2) Baseline Quantification HTSeq Quantification HTSeq Summarization FASTQ files Metadata DESeq Differential Contamination: Fungi, microbes Gene (FPKM) Gene (Adj. P-Val, Log2Fold) Genome References RNA-seq Processing Pipeline (iRAP) https://code.google.com/p/irap

29. RNA-seq Processing Pipeline (iRAP) https://code.google.com/p/irap

30. RNA-seq Processing Pipeline (iRAP) https://code.google.com/p/irap