1. ArrayExpress and Expression Atlas: Mining Functional Genomics data Gabriella Rustici, PhD Functional Genomics Team EBI-EMBL gabry@ebi.ac.uk

2. What is functional genomics (FG)? The aim of FG is to understand the function of genes and other parts of the genome FG experiments typically utilize genome-wide assays to measure and track many genes (or proteins) in parallel under different conditions High-throughput technologies such as microarrays and high-throughput sequencing (HTS) are frequently used in this field to interrogate the transcriptome 2ArrayExpress

3. What biological questions is FG addressing? When and where are genes expressed? How do gene expression levels differ in various cell types and states? What are the functional roles of different genes and in what cellular processes do they participate? How are genes regulated? How do genes and gene products interact? How is gene expression changed in various diseases or following a treatment? 3ArrayExpress

4. Components of a FG experiment ArrayExpress4

5. FG public repositories: ArrayExpress  Is a public repository for FG data, which provides easy access to well annotated data in a structured and standardized format  Serves the scientific community as an archive for data supporting publications, together with GEO at NCBI and CIBEX at DDBJ  Facilitates the sharing of experimental information associated with the data such as microarray designs, experimental protocols,……  Based on community standards: MIAME guidelines & MAGE-TAB format for microarray, MINSEQE guidelines for HTS data (http://www.mged.org/minseqe/) ArrayExpress5

6. Community standards for data requirement  MIAME = Minimal Information About a Microarray Experiment  MINSEQE = Minimal Information about a high-throughput Nucleotide SEQuencing Experiment  The checklist: ArrayExpress6 RequirementsMIAMEMINSEQE 1. Experiment design / background description 2. Sample annotation and experimental factor 3. Array design annotation (e.g. probe sequence) 4. All protocols (wet-lab bench and data processing) 5. Raw data files (from scanner or sequencing machine) 6. Processed data files (normalised and/or transformed)

7. MAGE-TAB is a simple spreadsheet format that uses a number of different files to capture information about a microarray or HTS experiments IDFInvestigation Description Format file, contains top-level information about the experiment including title, description, submitter contact details and protocols. SDRFSample and Data Relationship Format file contains the relationships between samples and arrays, as well as sample properties and experimental factors, as provided by the data submitter. ADF Array Design Format file that describes probes on an array, e.g. sequence, genomic mapping location (for array data ony) Data filesRaw and processed data files. The ‘raw’ data files are the trace data files (.srf or.sff). Fastq format files are also accepted, but SRF format files are preferred. The trace data files that you submit to ArrayExpress will be stored in the European Nucleotide Archive (ENA). The processed data file is a ‘data matrix’ file containing processed values, e.g. files in which the expression values are linked to genome coordinates.European Nucleotide Archive Standards for microarray & sequencing MAGE-TAB format ArrayExpress7

8. What is an experimental factor?  The main variable(s) studied in the experiment  It often is the independent variable of the microarray or HTS experiment. Values of the experimental factor (“factor values”) should vary.  Examples: ArrayExpress8 Experiment designFactor Factor ValuesNot factor  human blood samples vs mouse blood samples organismHomo sapiens, Mus musculus organism part (blood only) lung samples from male C57BL/6 mice vs lung samples from male 129 mice strainC57BL/6, 129 organism part (lung only), sex (male only)

9. ArrayExpress9 ArrayExpress – two databases

10. What is the difference between them? ArrayExpress Archive Central object: experiment Query to retrieve experimental information and associated data Expression Atlas Central object: gene/condition Query for gene expression changes across experiments and across platforms ArrayExpress10

11. ArrayExpress – two databases ArrayExpress11

12. ArrayExpress Archive – when to use it? Find FG experiments that might be relevant to your research Download data and re-analyze it. Often data deposited in public repositories can be used to answer different biological questions from the one asked in the original experiments. Submit microarray or HTS data that you want to publish. Major journals will require data to be submitted to a public repository like ArrayExpress as part of the peer-review process. ArrayExpress12

13. How much data in AE Archive? (as of mid-September 2012) ArrayExpress13

14. HTS data in AE Archive (as of mid-September 2012) Microarray vs HTS RNA-, DNA-, ChIP- seq breakdown ArrayExpress14

15. ArrayExpress www.ebi.ac.uk/arrayexpress/ ArrayExpress15

16. Browsing the AE Archive Search results in tabular format; each row represents an experiment. For each experiment data is available for download and metadata for further exploration ArrayExpress16 Filters are available to refine a search as well as a search box to search experiments for any term of interest, using the Experimental Factor Ontology (EFO)

17. Searching AE with the Experimental factor ontology (EFO)  Application focused ontology modeling the relationship between experimental factors (EFs) in AE  Developed to: increase the richness of annotations that are currently made in AE Archive to promote consistency to facilitate automatic annotation and integrate external data  EFs are transformed into an ontological representation, forming classes and relationships between those classes  Combine terms from a subset of well-maintained and compatible ontologies, e.g. Gene Ontology, NCBI Taxonomy ArrayExpress17

18. Building EFO An example sarcoma cancer neoplasm disease Kaposi’s sarcoma Take all experimental factors sarcoma cancer neoplasm Kaposi’s sarcoma disease is the parent term is a type of disease is synonym of neoplasm is a type of cancer is a type of sarcoma Find the logical connection between them disease neoplasm cancer sarcoma Kaposi’s sarcoma [-] Organize them in an ontology ArrayExpress18

19. Exploring EFO An example ArrayExpress19 More information at: http://www.ebi.ac.uk/efo

20. Hands-on exercise 1 Find RNA-seq experiments studying human prostate adenocarcinoma ArrayExpress20

21. ArrayExpress – two databases ArrayExpress21

22. Expression Atlas – when to use it? Find out if the expression of a gene (or a group of genes with a common gene attribute, e.g. GO term) change(s) across all the experiments available in the Expression Atlas; Discover which genes are differentially expressed in a particular biological condition that you are interested in. ArrayExpress 22

23. Array (platform) designs relating to the experiment must be provided. Probe annotation must be adequate to enable re- annotation of external references (e.g. Ensembl gene ID, Uniprot ID) At least 3 replicates for each value of the experimental factor Maximum 4 experimental factors Adequate sample annotation using EFO terms Presence of data files: CEL raw data files for Affymetrix assays, processed data files for non-Affymetrix ones Expression Atlas construction Experiment selection criteria during curation ArrayExpress23

24. Expression Atlas construction Analysis pipeline genes Cond.1Cond.2Cond.3 Linear model* (Bio/C Limma ) Cond.1 Cond.2 Cond.3 Input data (Affy CEL, non-Affy processed) 1= differentially expressed 0 = not differentially expressed Output: 2-D matrix * More information about the statistical methodology: http://nar.oxfordjournals.org/content/38/suppl_1/D690.full http://nar.oxfordjournals.org/content/38/suppl_1/D690.full ArrayExpress24

25. Expression Atlas construction Analysis pipeline “Is gene X differentially expressed in condition 1 in this experiment?” Cond.1 mean Cond.2 mean Cond.3 mean Mean of all samples = a single expression value for gene X Compare and calculate statistic ArrayExpress25

26. genes Cond.1Cond.2Cond.3 Exp.1 genes Cond.4Cond.5Cond.6 Exp. 2 genes Cond.XCond.YCond.Z Exp. n Statistical test Statistical test Statistical test Each experiment has its own “verdict” or “vote” on whether a gene is differentially expressed or not under a certain condition ArrayExpress26 Expression Atlas construction

27. Summary of the “verdicts” from different experiments ArrayExpress27

28. Expression Atlas ArrayExpress28

29. Expression Atlas home page http://www.ebi.ac.uk/gxa/ Query for genes Query for conditions Restrict query by direction of differential expression The ‘advanced query’ option allows building more complex queries ArrayExpress29

30. Hands-on exercise 2 Find genes in the “androgen receptor signaling pathway” which are (i) expressed in prostate carcinoma and (ii) involved in regulation of transcription from RNA Pol II Hands-on exercise 3 Find information on Tbx1 expression in human brain ArrayExpress30