Gabriella Rustici, PhD
Functional Genomics Team
EBI-EMBL gabry@ebi.ac.uk

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

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 (http://www.mged.org/minseqe/)
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Reporting standards for microarrays
MIAME checklist
 Minimal Information About a Microarray Experiment
 The 6 most critical elements contributing towards MIAME are:
1.
Essential sample annotation including experimental factors and their values (e.g. compound and dose)
2.
Experimental design including sample data relationships (e.g. which raw data file relates to which sample, ….)
3.
Sufficient array annotation (e.g. gene identifiers, genomic coordinates, probe sequences or array catalog number)
4.
Essential laboratory and data processing protocols
(e.g. normalization method used)
5.
Raw data for each hybridization (e.g. CEL or GPR files)
6.
Final normalized data for the set of hybridizations in the experiment
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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):
1.
General information about the experiment
2.
Essential sample annotation including experimental factors and their values (e.g. compound and dose)
3.
Experimental design including sample data relationships (e.g. which raw data file relates to which sample, ….)
4.
Essential experimental and data processing protocols
5.
Sequence read data with quality scores, raw intensities and processing parameters for the instrument
6.
Final processed data for the set of assays in the experiment
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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
SDRF
ADF
Data files
Investigation Description Format file, contains top-level information about the experiment including title, description, submitter contact details and protocols.
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.
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.
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.
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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.
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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
( www.obofoundry.org
) for the development of life-science ontologies
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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
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AE unique experiment ID
Curated title of experiment
Number of assays
Species investigated
The date when the data were loaded in the Archive loaded in
Atlas flag
The list of experiments retrieved can be printed, saved as Tabdelimited format or exported to
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Excel or as RSS feed
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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.
Raw sequencing data available in
ENA

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http://www.ebi.ac.uk/efo
 Application focused ontology modeling 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
 EFO terms map to multiple existing domain specific ontologies, such as the Disease Ontology and Cell Type Ontology
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An example
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Simple query - EFO
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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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• 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

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AE Archive – experiment view

Samples Sample annotation
Gene annotations
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Gene expression levels or count level data

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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
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Advanced query - fieldnames
Field name Searches accession Experiment primary or secondary accession
Example accession:E-MEXP-568 array Array design accession or name array:AFFY-2 OR array:Agilent* ef efv expdesign
Experimental factor, the name of the main variables in an experiment.
Experimental factor value. Has EFO expansion.
ef:celltype OR ef:compound efv:fibroblast
Experiment design type expdesign:”dose response”
Experiment type. Has EFO expansion.
exptype:RNA-seq exptype gxa ef:compound AND gxa:true Presence in the Gene Expression Atlas. Only value is gxa:true.
PubMed identifier pmid:16553887 pmid sa species
Sample attribute values. Has EFO expansion.
Species of the samples. Has EFO expansion.
sa:wild_type species:”homo sapiens” AND ef:cellline
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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)
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Filter assaycount:[x TO y]
What is filtered 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.
filter on the number of experimental factors efcount:[x TO y] samplecount:[x TO y] filter on the number of samples sacount:[x TO y] rawcount:[x TO y]
ArrayExpress filter on the number of sample attribute categories 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:2009-12-01 - will search for experiments released on 1st of Dec 2009
•date:2009* - will search for experiments released in 2009
•date:[2008-01-01 2008-05-31] - will search for experiments released between 1st of Jan and end of May 2008

Advanced query – an example
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Exercise 1

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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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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
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Atlas construction
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Atlas construction
 up-regulated
 down-regulated
 no change

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Query for gene(s)
http://www.ebi.ac.uk/gxa/
Restrict search by direction of differential expression
Query for condition(s)
The ‘advanced search’ option allows building more complex queries
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The ‘Genes’ search box & auto-complete function
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The ‘Conditions’ search box & ontology browsing
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A single gene query
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Experimental factors list
Expression plot
Table containing gene information and drop down menus for searching within the experiment

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A ‘Conditions’ only query
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Click the ‘ expression profile ’ link to view the experiment page

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Exercises 2, 3 & 4

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www.ebi.ac.uk/microarray/submissions.html
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• 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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• Email confirmation
• Curation
• The curation team will review your submission and will email 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.
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