Babelomics functional interpretation of genome scale experiments valencia march 2008
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Babelomics Functional interpretation of genome-scale experiments Valencia, March 2008. David Montaner [email protected] http://bioinfo.cipf.es Bioinformatics Department Centro de Investigacion Principe Felipe (Valencia).

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Babelomics Functional interpretation of genome-scale experiments Valencia, March 2008

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Babelomics functional interpretation of genome scale experiments valencia march 2008

BabelomicsFunctional interpretation of genome-scale experimentsValencia, March 2008

David Montaner

[email protected]

http://bioinfo.cipf.es

Bioinformatics Department

Centro de Investigacion Principe Felipe

(Valencia)


Babelomics functional interpretation of genome scale experiments valencia march 2008

Babelomics: A systems biology web resource for the functional interpretation of genome-scale experiments.

http://www.babelomics.org


Genome scale experiment output

Genome-scale experiment output

Functional

Interpretation


Functional interpretation

Functional interpretation

To “interpret” experimental results is to use current knowledge to rearrange them in a meaningful way.

Experimental results observed in the lab (not always a wet-lab).

  • Recorded to:

  • Test a hypothesis.

  • Get a first insight of a biological process.

DB

information.

Already tested and stored


Babelomics functional interpretation of genome scale experiments valencia march 2008

Babelomics imported databases

ENSEMBL

www.ensembl.org

GO

KEGG

Interpro

Transcription

Factors

Cisred

Bioentities

Literature

Gene

expression

Homo sapiens

HGNC symbol

EMBL acc

UniProt/Swiss-Prot

UniProtKB/TrEMBL

Ensembl IDs

RefSeq

EntrezGene

Affymetrix

Agilent

PDB

Protein Id

IPI….

Mus musculus

Rattus norvegicus

Ensembl ID

Gallus gallus

Drosophila melanogaster

Caenorhabditis elegans

Saccharmoyces cerevisae

Arabidopsis thaliana


Babelomics tools

Babelomics tools

FatiGO: Finds differential distributions of Gene Ontology terms between two groups of genes.

FatiGOplus: an extension of FatiGO for InterPro motifs, pathways and SwissProt KW , transcription factors (TF), gene expression in tissues, bioentities from scientific literature, cis-regulatory elements CisRed.

Tissues Mining Tool: compares reference values of gene expression in tissues to your results.

MARMITE Finds differential distributions of bioentities extracted from PubMed between two groups of genes.

FatiScan: detect significant functions with Gene Ontology, InterPromotifs, Swissprot KW and KEGG pathways in lists of genes ordered according to differents characteristics.

MarmiteScan: Use chemical and disease-related information to detect related blocks of genes in a gene list with associated values.

GSEA: Detects blocks of functionally related genes with significant coordinate over- or under-expression using the Gene Set Enrichment Analysis.


Babelomics tools1

Babelomics tools

Information about genes which can be coded in binary variables, tags, labels.

Information about genes which has to be coded into continuous numerical values.


Fatigo

FatiGO

  • Compare two lists of genes.

  • Compare one list of genes against the rest of the genome.

  • One statistical test (Fisher’s exact) for each Block of annotation.

    • Multiple testing context.

    • Filtering of annotation is convenient. We test les terms; more interesting ones.


Babelomics functional interpretation of genome scale experiments valencia march 2008

A

B

Biosynthesis

6

2

No biosynthesis

4

8

Testing the distribution of functional terms among two groups of genes(remember, we have to test hundreds of Blocks)

One Gene List (A)

The other list (B)

Are this two groups of genes carrying out different biological roles?

Biosynthesis 60%

Biosynthesis 20%

Sporulation 20%

Sporulation 20%

Genes in group A have significantly to do with biosynthesis, but not with sporulation.


Babelomics functional interpretation of genome scale experiments valencia march 2008

A

B

Biosynthesis

6

2

No biosynthesis

4

8000

Testing the distribution of functional terms among two groups of genes(remember, we have to test hundreds of Blocks)

One Gene List (A)

The other list (B)

Are this two groups of genes carrying out different biological roles?

Biosynthesis 60%

Biosynthesis 20%

Sporulation 20%

Sporulation 20%

Genes in group A have significantly to do with biosynthesis, but not with sporulation.


Fatigo babelomics

FatiGO - Babelomics

  • Deal with duplicated genes

    • Exclude them

    • Include them

  • Try to reduce the background space of genes of interest

    • Using just genes with some annotation.

    • Use just genes annotated at certain level of the GO ontology.


Functional interpretation1

Functional interpretation

To “interpret” experimental results is to use current knowledge to rearrange them in a meaningful way.

Experimental results observed in the lab (not always a wet-lab).

  • Recorded to:

  • Test a hypothesis.

  • Get a first insight of a biological process.

DB

information.

Already tested and stored


Fatigo selecting the database

Organism

FatiGO – selecting the database


Fatigo selecting the database1

Database

FatiGO – selecting the database


Fatigo your own database

Your own Database

FatiGO – your own database


Fatigo introducing your data

Gene List2

Rest of genome

Gene List1

FatiGO – introducing your data


Fatigo results

FatiGO Results

Gene group1 is enriched in this functional block

Gene group2 is enriched in this functional block

percentages

p-values

corrected

p-values


Fatigo approach may not be very powerfull

Very few genes selected to arrive to a significant conclusion on GO1 and GO2

Functional Classes expressed as blocks in A and B

FatiGO approach may not be very powerfull

A B

GO1 GO2

-

Significantly over-expressed in B

If a threshold basedon the experimental values is applied, and the resulting selection of genes compared for enrichment of a functional term, this might not be found

t-test with two tails.

p<0.05

statistic

Significantly over-expressed in A

+


Fatiscan

FatiScan

  • Interpret a ranked list of genes.

  • There is not need for choosing a cut-off. All information is included.

  • One statistical test for each Block of annotation.

    • Multiple testing context.

    • Filtering of annotation is convenient. We test les terms; more interesting ones.


Fatiscan1

Organism

DataBases

Gene List

ordered

according the

experimental

value

FatiScan


Babelomics functional interpretation of genome scale experiments valencia march 2008

Testing along an ordered list

Annotation label A

Annotation label B

Annotation label C

B

C

A

List of genes

+

  • Index ranking genes according to some biological aspect under study.

  • Database that stores gene class membership information.

  • FatiScan searches over the whole ordered list, trying to find runs of functionally related genes.

Block of genes enriched in the annotation A

Annotation C is homogeneously distributed along the list

Block of genes enriched in the annotation B

-


Fatiscan results

FatiScan results

B

C

A

List of genes

+

-


Fatiscan results1

Functional Blocks over-represented among genes over-expressed in A

Functional Blocks over-represented among genes over-expressed in B

FatiScan results

A B

+

Gene ranking index

-


Babelomics functional interpretation of genome scale experiments valencia march 2008

FatiScan

List of genes ranked by biological criteria

+

Fisher´s test

Significant Functional terms

-

Al-Shahrour et al., 2005 Bioinformatics; 2007 BMC Bioinformatics


Fatiscan example two classes

FatiScan Example – two classes

TumorControl

t ~Tumor mean expression – Control mean expression

+ t

Proliferation

Is more associated with the genes on the top of the list

All genes in the array

Is more associated with the genes that show higher expression in Tumors

- t


Fatiscan example survival analysis

FatiScan Example - Survival Analysis

  • Cromer et all. Identification of genes associated with tumorigenesis and metastatic potential of hypopharyngeal cancer by microarray analysis. Oncogene 2004, 23(14) : 2484-2498.

  • 34 hypopharyngeal cancer samples taken from patients undergoing surgery.

  • Analyzed using Affymetrix HG-U95A microarrays (~12650 distinct transcription features ).

  • Disease free survival time after intervention was recorded

Cox proportional hazards model

h(t) = h0 (t) * exp (b * gene expression)


Babelomics functional interpretation of genome scale experiments valencia march 2008

Gene Ontology: biological process

Hazard increased with expression

+ b

lowest p-value = 0.96

Hazard decreased with expression

- b


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