1. ArrayExpress and Gene Expression Atlas: Mining Functional Genomics data
Gabriella Rustici, PhD
Functional Genomics Team EBI-EMBL

2. Talk structure Why do we need a database for functional genomics data?
ArrayExpress database
Archive
Gene Expression Atlas
ArrayExpress content
How to query the database
How to download data
How to submit data
ArrayExpress

3. 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
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4. 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?
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5. Components of a FG experiment
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6. ArrayExpress www.ebi.ac.uk/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 (
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7. Reporting standards for microarrays MIAME checklist
Minimal Information About a Microarray Experiment
The 6 most critical elements contributing towards MIAME are:
Essential sample annotation including experimental factors and their values (e.g. compound and dose)
Experimental design including sample data relationships (e.g. which raw data file relates to which sample, ….)
Sufficient array annotation (e.g. gene identifiers, genomic coordinates, probe sequences or array catalog number)
Essential laboratory and data processing protocols (e.g. normalization method used)
Raw data for each hybridization (e.g. CEL or GPR files)
Final normalized data for the set of hybridizations in the experiment
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8. Reporting standards for sequencing MINSEQE checklist
Minimal Information about a high-throughput Nucleotide SEQuencing Experiment
The proposed guidelines for MINSEQE are (still work in progress):
General information about the experiment
Essential sample annotation including experimental factors and their values (e.g. compound and dose)
Experimental design including sample data relationships (e.g. which raw data file relates to which sample, ….)
Essential experimental and data processing protocols
Sequence read data with quality scores, raw intensities and processing parameters for the instrument
Final processed data for the set of assays in the experiment
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9. Reporting standards for microarrays MAGE-TAB format
MAGE-TAB is a simple spreadsheet format that uses a number of different files to capture information about a microarray experiment. We now adapted it to handle HTS data:
IDF
Investigation Description Format file, contains top-level information about the experiment including title, description, submitter contact details and protocols.
SDRF
Sample 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 (for array data only)
Array Design Format file, describes the design of an array, i. e. the sequence located at each feature on the array and its annotation.
Data files
Raw and processed data files. The ‘raw’ data files are the files produced by the microarray image analysis software (e.g. CEL files for Affymetrix or GPR files from GenePix) or the trace data files (fastq, .srf or .sff) for HTS data.
The processed data file is a ‘data matrix’ file containing processed values (e.g. files in which the expression values are linked to gene IDs or genome coordinates)
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10. ArrayExpress – two databases

11. 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
ArrayExpress

12. ArrayExpress – two databases

13. 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.
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14. How much data in AE Archive?
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15. HTS data in AE Archive

16. Browsing the AE Archive
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17. Browsing the AE Archive
The date when the data were loaded in the Archive
Number of assays
AE unique experiment ID
Curated title of experiment
Species investigated
loaded in Atlas flag
Raw sequencing data available in ENA
The total number of experiments and assay retrieved
The direct link to raw and processed data. An icon indicates that this type of data is available.
The list of experiments retrieved can be printed, saved as Tab- delimited format or exported to Excel or as RSS feed
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18. Browsing the AE Archive
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19. Experimental factor ontology (EFO) http://www.ebi.ac.uk/efo
Application focused ontology modeling experimental factors (EFs) in AE – selected by default
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
EFO terms map to multiple existing domain specific ontologies, such as the Disease Ontology and Cell Type Ontology
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20. Building EFO An example Take all experimental factors sarcoma cancer
neoplasm
Kaposi’s sarcoma
disease
is the parent term
is a type of
is synonym of
Find the logical connection between them
disease
neoplasm
cancer
sarcoma
Kaposi’s sarcoma
[-]
Organize them in an ontology
sarcoma
cancer
neoplasm
disease
Kaposi’s sarcoma
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21. Exploring EFO
An example
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22. Searching AE Archive Simple query
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23. Searching AE Archive Simple query
Search across all fields:
AE accession number e.g. E-MEXP-568
Secondary accession numbers e.g. GEO series accession GSE5389
Experiment name
Submitter's experiment description
Sample attributes, experimental factor and values, including species (e.g. GeneticModification, Mus musculus, DREB2C over-expression)
Publication title, authors and journal name, PubMed ID
Synonyms for terms are always included in searches e.g. 'human' and 'Homo sapiens’
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24. AE Archive query output
Matches to exact terms are highlighted in yellow
Matches to synonyms are highlighted in green
Matches to child terms in the EFO are highlighted in pink

25. AE Archive – experiment view
MIAME or MINSEQE scores show how much the experiment is standard compliant
Link to files available.
This varies between sequencing and microarray data. For microarray experiments you also have array design file and you can view a graph of the experimental design
Raw data in ENA (it is a sequencing experiment), processed data downloadable as a zip
Experimental factor(s) and its values
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26. AE Archive – SDRF file
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27. SDRF file – sample & data relationship
New view. Now it is easy to identify the experimental variables investigated in each experiment. In this case only ‘Developmental stage’ is the only variable, with values 10hpi, 15hpi, 20hpi, 25hpi, 30hpi, 35hpi, 40hpi, 5hpi. Also easy to see relationship between samples and data files.
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28. Searching AE Archive Advanced query
Combine search terms
Enter two or more keywords in the search box with the operators AND, OR or NOT. AND is the default search term; a search for kidney cancer' will return hits with a match to ‘kidney' AND ‘cancer’
Search terms of more than one word must be entered inside quotes otherwise only the first word will be searched for. E.g. “kidney cancer”
Specify fields for searches
Particular fields for searching can also be specified in the format of fieldname:value
For more info see
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29. Searching AE Archive Advanced query – examples
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30. ArrayExpress – two databases

31. 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.
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32. Expression Atlas construction Experiment selection criteria
The criteria we use for selecting experiments for inclusion in the Atlas are as follows:
Array designs relating to experiment must be provided to enable re-annotation using Ensembl or Uniprot (or have the potential for this to be done)
High MIAME scores
Experiment must have 6 or more hybridizations
Sufficient replication and large sample size
EF and EFV must be well annotated
Adequate sample annotation must be provided
Processed data must be provided or raw data which can be renormalized must be available
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33. Expression Atlas construction Analysis pipeline
New meta-analytical tool for searching gene expression profiles across experiments in AE
Data is taken as normalized by the submitter
Gene-wise linear models (limma) and t-statistics are applied to calculate the strength of genes’ differential expression across conditions across experiments
The result is a two-dimensional matrix where rows correspond to genes and columns correspond to conditions, rather than samples.
The matrix entries are p-values together with a sign, indicating the significance and direction of differential expression
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34. Expression Atlas construction
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35. Expression Atlas construction
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36. Expression Atlas
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37. Atlas home page http://www.ebi.ac.uk/gxa/
Restrict query by direction of differential expression
Query for genes
Query for conditions
The ‘advanced query’ option allows building more complex queries
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38. Atlas home page The ‘Genes’ and ‘Conditions’ search boxes
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39. Atlas home page A single gene query
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40. Atlas gene summary page
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41. Atlas experiment page
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42. Atlas experiment page – HTS data
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43. Atlas home page A ‘Conditions’ only query
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44. Atlas heatmap view
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45. Atlas gene-condition query
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46. Atlas query refining
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47. Atlas gene-condition query
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48. Atlas query refining
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49. Atlas query refining
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50. Data submission to AE
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51. Data submission to AE www.ebi.ac.uk/microarray/submissions.html
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52. Submission of HTS gene expression data
Submit via MAGE-TAB submission route
Submit:
MAGE-TAB spreadsheet containing details of the samples and protocols used.
Trace data files for each sample (in SRF, FASTQ or SFF format )
Processed data files 
For non-human species we will supply your SRF or FASTQ files to the European Nucleotide Archive (ENA).
If you have human identifiable sequencing data you need to submit to the The European Genome-phenome Archive and not ArrayExpress. They will supply you with a suitable template for submission and store human identifiable data securely.
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53. Types of data that can be submitted
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54. What happens after submission?
confirmation
Curation
The curation team will review your submission and will you with any questions.
Possible reopening for editing
We will send you an accession number when all the required information has been provided.
We will load your experiment into ArrayExpress and provide you with a reviewer login for viewing the data before it is made public.
ArrayExpress

55. Find out more
Visit our eLearning portal, Train online, at for courses on ArrayExpress and Atlas
us at:
Atlas mailing list:
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