ArrayExpress and Expression Atlas: Mining Functional Genomics data

Gabriella Rustici, PhDFunctional Genomics TeamEBI-EMBL

gabry@ebi.ac.uk

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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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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Components of a FG experiment

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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/)

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Community standards for data requirement MIAME = Minimal Information About a Microarray Experiment

MINSEQE = Minimal Information about a high-throughput Nucleotide SEQuencing Experiment

The checklist:

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Requirements MIAME MINSEQE1. 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)

MAGE-TAB is a simple spreadsheet format that uses a number of different files to capture information about a microarray or HTS experiments

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.

ADFArray Design Format file that describes probes on an array, e.g. sequence, genomic mapping location (for array data ony)

Data files Raw 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.

Standards for microarray & sequencingMAGE-TAB format

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ArrayExpress – two databases

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

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ArrayExpress – two databases

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

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HTS data in AE Archive(as of mid-September 2012)

Microarray vs HTS

RNA-, DNA-, ChIP-seq breakdown

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ArrayExpresswww.ebi.ac.uk/arrayexpress/

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Browsing the AE Archive

The direct link to raw and processed data. An icon indicates that this type of data is available.

The total number of experiments and assay retrieved

Species investigated

Curated title of experiment

The date when the data were loaded in the

ArchiveAE unique

experiment IDNumber of

assays

The list of experiments retrieved can be printed, saved as Tab-delimited format or exported to Excel or as RSS feed

loaded in Atlas flag

Raw sequencing data available in

ENA

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Browsing the AE Archive

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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

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Building EFOAn 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

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Exploring EFOAn example

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More information at: http://www.ebi.ac.uk/efo

Searching AE ArchiveSimple query

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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

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AE Archive – experiment view

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SDRF file – sample & data relationship

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ArrayExpress – two databases

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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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• 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 constructionExperiment selection criteria during curation

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Expression Atlas constructionAnalysis pipeline

gene

s

Cond.1 Cond.2 Cond.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

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Expression Atlas constructionAnalysis 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

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gene

sCond.1 Cond.2 Cond.3Exp.1

gene

s

Cond.4 Cond.5 Cond.6Exp. 2

gene

s

Cond.X Cond.Y Cond.ZExp. 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

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Expression Atlas construction

Expression Atlas construction

Summary of the “verdicts” from different experiments

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Expression Atlas

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Atlas home pagehttp://www.ebi.ac.uk/gxa/

Query for genes

Query for conditionsRestrict query by direction of differential expression

The ‘advanced query’ option allows building more complex queries

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Atlas home pageThe ‘Genes’ and ‘Conditions’ search boxes

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Atlas home pageA single gene query

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Atlas gene summary page

Atlas experiment page

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Atlas experiment page – HTS data

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Atlas home pageA ‘Conditions’ only query

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Atlas heatmap view

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Atlas gene-condition query

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Atlas advanced search

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Atlas advanced search

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Atlas advanced search

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A glimpse of what’s coming…“Differential atlas”

“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

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A glimpse of what’s coming…“Differential atlas” mock-up (1)

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A glimpse of what’s coming…“Differential atlas” mock-up (2)

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• Gene expression in normal tissues, not looking for differentially expressed genes based on different conditions

• E.g. “Give me all the genes expressed in normal human kidney”

• Can also filter genes by expression level (e.g. FPKM values)

• Start with Illumina Body Map 2.0 RNA-seq data

• 16 tissues: adrenal, adipose, brain, breast, colon, heart, kidney, liver, lung, lymph, ovary, prostate, skeletal muscle, testes, thyroid, and white blood cells

• We are working on something similar for mouse

A glimpse of what’s coming…“Baseline atlas”

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A glimpse of what’s coming…“Baseline atlas” mock-up display

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Data submission to AE

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Data submission to AEwww.ebi.ac.uk/microarray/submissions.html

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Submission of HTS dataArrayExpress acts as a “broker” for submitter.

• Meta-data and processed data: ArrayExpress• Raw sequence reads* (e.g. fastq, bam): ENA

*See http://www.ebi.ac.uk/ena/about/sra_data_format for accepted read file format

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Find out more

• Visit our eLearning portal, Train online, at http://www.ebi.ac.uk/training/online/ for courses on ArrayExpress and Atlas

• Watch this short YouTube video on how to navigate the MAGE-TAB submission tool: http://youtu.be/KVpCVGpjw2Y

• Email us at: miamexpress@ebi.ac.uk

• Atlas mailing list: arrayexpress-atlas@ebi.ac.uk

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