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
Database content
Query the database
Data download
Data submission
ArrayExpress

3. Components of a functional genomics experiment
Sample source
Sample treatments
RNA extraction protocol
Labelling protocol
Sample source
Sample treatments
Template preparation
Array
Normalized data
Raw data
Sample
Data analysis
Sample
Library
Chip
Data analysis
Library preparation
Array design information
Location of each element
Description of each element
Hybridization protocol
Cluster amplification
Image
Scanning protocol
Software specifications
Sequencing and imaging
Quantification matrix
Software specifications
From images to sequences
Quality Control
Sequence alignment
Assembly
Specific steps depending on the application
Control array elements
Normalization method

4. ArrayExpress www.ebi.ac.uk/arrayexpress/
Is a public repository for functional genomics data, mostly generated using microarray or high throughput sequencing (HTS) assays
Serves the scientific community as an archive for data supporting publications, together with GEO at NCBI and CIBEX at DDBJ
Provides easy access to well annotated microarray data in a structured and standardized format
Facilitates the sharing of microarray designs and experimental protocols
Based on FGED standards: MIAME checklist, MAGE-TAB format and MO Ontology.
MINSEQE checklist for HTS data ( 4
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5. 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 5
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6. 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 6
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7. 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:
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
Array Design Format file, describes the design of an array, i. e. the sequence located at each feature on the array and annotation of the sequences.
Data files
Raw and processed data files. The ‘raw’ data files are the files produced by the microarray image analysis software, such as CEL files for Affymetrix or GPR files from GenePix.
The processed data file is a ‘data matrix’ file containing processed values, as provided by the data submitter. 7
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8. Reporting standards What semantics (or ontology) should we use to best describe its annotation?
Ontology, which is a formal specification of terms in a particular subject area and the relations among them.
Its purpose is to provide a basic, stable and unambiguous description of such terms and relations in order to avoid improper and inconsistent use of the terminology pertaining to a given domain.
Thus far, Gene Ontology (GO) has been the most successful ontology initiative. GO is a controlled vocabulary used to describe the biology of a gene product in any organism. 8
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9. Reporting standards for microarrays MGED ontology (MO)
The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data
The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME
Also check Open Biomedical Ontologies (OBO) initiative ( for the development of life-science ontologies 9
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10. ArrayExpress – two databases

11. How to query AE and Atlas?
AE Archive
Query by experiment, sample and experimental factor annotations
Filter on species, array platform, molecule assayed and technology used
Gene Expression Atlas
Gene and/or condition queries
Query across experiments and across platforms
ArrayExpress

12. ArrayExpress – two databases12
ArrayExpress

13. How much data in AE Archive?13
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14. Archive by species 14
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15. Browsing the AE Archive
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16. 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 16
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17. Browsing the AE Archive
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18. 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 18
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19. Experimental factor ontology (EFO) An example
ArrayExpress & Atlas

20. Searching AE Archive Simple query - EFO20
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21. 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’ 21
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22. 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

23. 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 23
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24. How does processed data look?
Samples
Sample annotation
Genes
Gene expression levels or count level data
Gene annotations 24
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25. AE Archive – SDRF file 25
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26. 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. 26
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27. AE Archive – ADF file 27
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28. AE Archive – Old interface28
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29. AE Archive – all files 29
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30. AE Archive – all files 30
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31. 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 31
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32. Searching AE Archive Advanced query - fieldnames
Searches
Example
accession
Experiment primary or secondary accession
accession:E-MEXP-568
array
Array design accession or name
array:AFFY-2 OR array:Agilent* ef
Experimental factor, the name of the main variables in an experiment.
ef:celltype OR ef:compound
efv
Experimental factor value. Has EFO expansion.
efv:fibroblast
expdesign
Experiment design type
expdesign:”dose response”
exptype
Experiment type. Has EFO expansion.
exptype:RNA-seq
gxa
Presence in the Gene Expression Atlas. Only value is gxa:true.
ef:compound AND gxa:true
pmid
PubMed identifier
pmid: sa
Sample attribute values. Has EFO expansion.
sa:wild_type
species
Species of the samples. Has EFO expansion.
species:”homo sapiens” AND ef:cellline 32
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33. Searching AE Archive Advanced query
Filtering experiments by counts of a particular attribute
Experiments fulfilling certain count criteria can also be searched for e.g. having more than 10 assays (hybridizations)
Filter
What is filtered
assaycount:[x TO y]
filter on the number of of assays where x <= y and both values are between 0 and 99,999 (inclusive) . To count excluding the values given use curly brackets e.g. assaycount:{1 TO 5} will find experiments with 2-4 assays. Single numbers may also be given e.g. assaycount:10 will find experiments with 10 assays.
efcount:[x TO y]
filter on the number of experimental factors
samplecount:[x TO y]
filter on the number of samples
sacount:[x TO y]
filter on the number of sample attribute categories
rawcount:[x TO y]
filter on the number of raw files
fgemcount:[x TO y]
filter on the number of final gene expression matrix (processed data) files
miamescore:[x TO y]
filter on the MIAME compliance score (maximum score is 5)
date:yyyy-mm-dd
filter by release date
date: will search for experiments released on 1st of Dec 2009
date:2009* - will search for experiments released in 2009
date:[ ] - will search for experiments released between 1st of Jan and end of May 2008 33
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34. Searching AE Archive Advanced query – an example
ArrayExpress & Atlas

35. Exercise 1
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36. ArrayExpress – two databases36
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37. Gene Expression Atlas 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 37
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38. Gene Expression Atlas Atlas construction
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
ArrayExpress

39. Gene Expression Atlas Atlas construction
ArrayExpress

40. Gene Expression Atlas Atlas construction

41. Gene Expression Atlas
ArrayExpress

42. 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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43. Atlas home page The ‘Genes’ search box43
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44. Atlas home page The ‘Conditions’ search box44
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45. Atlas home page A single gene query45
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46. Atlas gene summary page46
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47. Atlas experiment page 47
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48. Atlas experiment page – HTS data
ArrayExpress & Atlas

49. Atlas home page A ‘Conditions’ only query
ArrayExpress & Atlas

50. Atlas heatmap view
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51. Atlas list view 51 ArrayExpress
Click the ‘expression profile’ link to view the experiment page 51
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52. Atlas data download 52
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53. Atlas gene-condition query53
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54. Atlas query refining 54
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55. Atlas gene-condition query55
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56. Atlas query refining 56
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57. Atlas query refining 57
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58. Exercises 2 & 3
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59. Data submission to AE
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60. Data submission to AE www.ebi.ac.uk/microarray/submissions.html
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61. 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.
ArrayExpress & Atlas

62. Types of data that can be submitted
ArrayExpress & Atlas

63. 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 & Atlas