ferran sanz – grib (imim-upf) bioinformatics: how it can support the family of international...

Ferran Sanz – GRIB (IMIM-UPF)Ferran Sanz – GRIB (IMIM-UPF)

Bioinformatics: How it can supportthe Family of International Classifications?

Ferran SanzResearch Programme on Biomedical Informatics (GRIB)

Hospital del Mar Research Institute (IMIM)Universitat Pompeu Fabra


Bioinformatics constitutes an expanding discipline that deals with the management and analysis of the huge amount of information that is generated in both biomedical research and clinical practice.

It contributes to a more accurate and comprehensive understanding of the molecular mechanisms of diseases, thus providing a valuable basis for their more rigorous classification.


Integration of heterogeneous biomedical information in orderto gain a more complete and powerful view on diseases and therapeutics

INTEGRATIVE BIOINFORMATICS

Clinical Data

Biomedical imaging

‘omics & Systems Biology

Drugs & other chemicals

Biomedical literature


Information on the genetic basis of human diseases is abundant but scattered among different sources


• A comprehensive resource on gene-disease associations

• Integrates information from publicly available databases and the literature (text mining)

• Freely available at: http://ibi.imim.es/DisGeNET

http://ibi.imim.es/DisGeNET

http://ibi.imim.es/DisGeNET

Ferran Sanz – GRIB (IMIM-UPF)

source databases

CTDhuman

UniProt GADMGD

RDG

Mouse and rat genes projected into human

orthologs

Curated Predicted Literature

LHGDN

BeFreeCTD

mouse & rat


v2.1 (2014 release)

Database Statistics


Knowledge pockets in genetics of diseases


Simplified data model of DisGeNET RDF representation


The DisGeNET association type ontology


Examples of the questions that can be answered using DisGeNET


DisGeNET web interface: the Search view and the Browser view


disease centric view

gene centric view

network clustering

Analysis of gene-disease networks

• Groups of diseases based on shared genetic background

• Disease classification

• Disease comorbidities

• Groups of genes based on shared diseases

• Might reveal common functional processes underlying diseases


Identification of shared mechanisms for comorbid diseases:

COPD comorbidities

Application


COPD comorbidome

Mol

ecul

ar C

omor

bidi

ty In

dex

(MCI

)

Proportion of shared proteins targeted by chemical compounds present in tobacco smoke


Connexion between COPD and Anemia at the molecular level

Ferran Sanz – GRIB (IMIM-UPF)18

Database of psychiatric disorders and their genes

Set of tools for data exploration and analysis

http://www.psygenet.org/




To develop a resource on psychiatric diseases and their genes

Goal

• Initial focus in three psychiatric disorders: MD, AUD, CUD• Close collaboration between psychiatry experts and

bioinformaticians• Gather and integrate data scattered in publications and

other public databases


Bio-Entity Finder and Relation Extraction

Gene-disease associations

Retrieval based on terminology selected by experts

PsyGeNET database

Gene-disease associations

Reviewed by expertsGAD

CTDRGD

MGDPsyCUR

900,000 abstracts

800 associations

2,000 assoc


PsyGeNET database

Dataset Original data source

Genes Disease terms

Associations

GAD GAD 844 20 1165

MODELSMGD

33 6 39RGD

CURATEDCTD

755 37 2061PsyCUR

ALL 1317 37 2823


PsyGeNET PsyCUR Exclusive to PsyCUR

CTD1 OMIM2

Depression 877 546 501 70 6Alcoholism 555 221 198 34 6Cocaine use disorder

132 33 18 108 0

1Comparative Toxicogenomics Database2Online Mendelian Inheritance on Man

Number of genes associated to each psychiatric disorder in PsyGeNET

PsyGeNET contains information on the genetics of psychiatric disorders not available in other resources


Depression, alcoholism and cocaine use disorders might be linked at the molecular level through shared genes

Disease A Disease B genes A Genes B Shared genes

Jaccard Index

Depression Alcoholism 877 555 169 0.13*

Depression Cocaine UD 877 132 63 0.07*

Alcoholism Cocaine UD 555 132 57 0.06*

*significant by bootstrap testing


Functional analysis of psychiatric disease genes

• GO and pathway enrichment of individual diseases and

their comorbidities

• Enrichment analysis of depression genes with cocaine

and alcohol chemicals


Serotonin and dopamine signaling


Alcohol use disorder

• 232 genes in PsyGeNET curated

• 386 genes targeted by alcohol compounds in CTD

• 51 genes in common

Cocaine use disorder

• 126 genes in PsyGeNET curated

• 291 genes targeted by cocaine compounds in CTD

• 59 genes in common


J. Piñero

Integrative Biomedical Informatics Group (GRIB)http://grib.upf.edu

M. CasesF. Sanz L.Furlong

S.GrosdidierN.Queralt

A. Bravo

M.A. Mayer A. Gutiérrez

A. Bauer-Mehren

http://grib.upf.edu/

ferran sanz – grib (imim-upf) bioinformatics: how it can support the family of international...

Documents

shared diseases

ferran sanzresearch

comorbid diseases

study of diseases

genedisease associations

biomedical research

rat genes

data integration