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How to build personalized multi-omics comorbidity profiles.

Moni MA, Liò P - Front Cell Dev Biol (2015)

Bottom Line: However, there is a lack of effective and efficient bioinformatics and statistical software for true integrative data analysis.The functions of POGO offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high-throughput and clinical data analysis pipelines.POGO is compliant with the Bioconductor standard and it is freely available at www.cl.cam.ac.uk/~mam211/POGO/.

View Article: PubMed Central - PubMed

Affiliation: Computer Laboratory, University of Cambridge Cambridge, UK ; Department of Computer Science and Engineering, Pabna University of Science and Technology Pabna, Bangladesh ; Bone Biology, Garvan Institute of Medical Research, The University of New South Wales Sydney, NSW, Australia.

ABSTRACT
Multiple diseases (acute or chronic events) occur together in a patient, which refers to the disease comorbidities, because of the multi ways associations among diseases. Due to shared genetic, molecular, environmental, and lifestyle-based risk factors, many diseases are comorbid in the same patient. Methods for integrating multiple types of omics data play an important role to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. Moreover, integrated omics and clinical information may potentially improve prediction accuracy of disease comorbidities. However, there is a lack of effective and efficient bioinformatics and statistical software for true integrative data analysis. With the availability of the wide spread huge omics, phenotype and ontology information, it is becoming more and more practical to help doctors in clinical diagnostics and comorbidity prediction by providing appropriate software tool. We developed an R software POGO to compute novel estimators of the disease comorbidity risks and patient stratification. Starting from an initial diagnosis, omics and clinical data of a patient the software identifies the association risk of disease comorbidities. The input of this software is the initial diagnosis of a patient and the output provides evidence of disease comorbidities. The functions of POGO offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high-throughput and clinical data analysis pipelines. POGO is compliant with the Bioconductor standard and it is freely available at www.cl.cam.ac.uk/~mam211/POGO/.

No MeSH data available.


Output figure and statistics of >comorbiditySNP (c(“TNFSF11”, “TNFRSF11B”, “TNFRSF11A”, “A2M”, “TGFBR3”), “Symbol”). We show an example of disease comorbidity through the SNPs-gene-disease associations. Here the square nodes represent the genes symbols, circles represent SNPs ids, and spheres represent diseases names. The size of the nodes represents the degree of associations.
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Figure 4: Output figure and statistics of >comorbiditySNP (c(“TNFSF11”, “TNFRSF11B”, “TNFRSF11A”, “A2M”, “TGFBR3”), “Symbol”). We show an example of disease comorbidity through the SNPs-gene-disease associations. Here the square nodes represent the genes symbols, circles represent SNPs ids, and spheres represent diseases names. The size of the nodes represents the degree of associations.

Mentions: At present there are only a few databases of genetic variations associated with diseases. Despite the needs for analyzing SNP and disease association, most of the existing databases are based only on functional variants at specific locations on the genome, or deal with only a few genes correlated with disease. There is no integrated resource to widely support genes, SNPs, and disease associated information. Therefore, we integrated data from different databases (dbSNP Sherry et al., 2001, HGVbase Fredman et al., 2002, JSNP Hirakawa et al., 2002, GAD Becker et al., 2004 and OMIM McKusick, 2007) and literature Yang et al., 2008 for studying SNPs-diseases associations. We integrated the information to present the interrelationships among SNPs located in genes, genes associated with diseases, and SNPs associated with diseases. It can aid the understanding of the genes which cause diseases and the impact of SNPs on diseases. For associated information among genetic variation and diseases, we built a database, SNP, which is a combined database of genes, genetic variation and diseases for the utilization in POGO. Two diseases are connected if they share at least one SNP that is statistically significant dysregulated to the disease related gene. Our software is designed to capture the relationships between SNPs associated with disease and disease-causing genes. POGO computes disease-disease association by adopting semantic similarity measures and hypergeometric test. Neighborhood based benchmark method is used to identify the comorbidity pattern among diseases (Goh et al., 2007). We built the associated network as a bipartite graph; each common neighbor node is selected based on the Jaccard coefficient method (Goh et al., 2007). comorbiditySNP function of POGO takes as input any of these three options: a list of gene symbols, a list of Entrez gene ids, SNPs ids or an OMIM id. This function provides disease comorbidity associations and network based on the SNPs-gene-disease associations. comorbiditySNP requires two parameters: id list and id type. An example and its output is given in Figure 4.


How to build personalized multi-omics comorbidity profiles.

Moni MA, Liò P - Front Cell Dev Biol (2015)

Output figure and statistics of >comorbiditySNP (c(“TNFSF11”, “TNFRSF11B”, “TNFRSF11A”, “A2M”, “TGFBR3”), “Symbol”). We show an example of disease comorbidity through the SNPs-gene-disease associations. Here the square nodes represent the genes symbols, circles represent SNPs ids, and spheres represent diseases names. The size of the nodes represents the degree of associations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4478898&req=5

Figure 4: Output figure and statistics of >comorbiditySNP (c(“TNFSF11”, “TNFRSF11B”, “TNFRSF11A”, “A2M”, “TGFBR3”), “Symbol”). We show an example of disease comorbidity through the SNPs-gene-disease associations. Here the square nodes represent the genes symbols, circles represent SNPs ids, and spheres represent diseases names. The size of the nodes represents the degree of associations.
Mentions: At present there are only a few databases of genetic variations associated with diseases. Despite the needs for analyzing SNP and disease association, most of the existing databases are based only on functional variants at specific locations on the genome, or deal with only a few genes correlated with disease. There is no integrated resource to widely support genes, SNPs, and disease associated information. Therefore, we integrated data from different databases (dbSNP Sherry et al., 2001, HGVbase Fredman et al., 2002, JSNP Hirakawa et al., 2002, GAD Becker et al., 2004 and OMIM McKusick, 2007) and literature Yang et al., 2008 for studying SNPs-diseases associations. We integrated the information to present the interrelationships among SNPs located in genes, genes associated with diseases, and SNPs associated with diseases. It can aid the understanding of the genes which cause diseases and the impact of SNPs on diseases. For associated information among genetic variation and diseases, we built a database, SNP, which is a combined database of genes, genetic variation and diseases for the utilization in POGO. Two diseases are connected if they share at least one SNP that is statistically significant dysregulated to the disease related gene. Our software is designed to capture the relationships between SNPs associated with disease and disease-causing genes. POGO computes disease-disease association by adopting semantic similarity measures and hypergeometric test. Neighborhood based benchmark method is used to identify the comorbidity pattern among diseases (Goh et al., 2007). We built the associated network as a bipartite graph; each common neighbor node is selected based on the Jaccard coefficient method (Goh et al., 2007). comorbiditySNP function of POGO takes as input any of these three options: a list of gene symbols, a list of Entrez gene ids, SNPs ids or an OMIM id. This function provides disease comorbidity associations and network based on the SNPs-gene-disease associations. comorbiditySNP requires two parameters: id list and id type. An example and its output is given in Figure 4.

Bottom Line: However, there is a lack of effective and efficient bioinformatics and statistical software for true integrative data analysis.The functions of POGO offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high-throughput and clinical data analysis pipelines.POGO is compliant with the Bioconductor standard and it is freely available at www.cl.cam.ac.uk/~mam211/POGO/.

View Article: PubMed Central - PubMed

Affiliation: Computer Laboratory, University of Cambridge Cambridge, UK ; Department of Computer Science and Engineering, Pabna University of Science and Technology Pabna, Bangladesh ; Bone Biology, Garvan Institute of Medical Research, The University of New South Wales Sydney, NSW, Australia.

ABSTRACT
Multiple diseases (acute or chronic events) occur together in a patient, which refers to the disease comorbidities, because of the multi ways associations among diseases. Due to shared genetic, molecular, environmental, and lifestyle-based risk factors, many diseases are comorbid in the same patient. Methods for integrating multiple types of omics data play an important role to identify integrative biomarkers for stratification of patients into groups with different clinical outcomes. Moreover, integrated omics and clinical information may potentially improve prediction accuracy of disease comorbidities. However, there is a lack of effective and efficient bioinformatics and statistical software for true integrative data analysis. With the availability of the wide spread huge omics, phenotype and ontology information, it is becoming more and more practical to help doctors in clinical diagnostics and comorbidity prediction by providing appropriate software tool. We developed an R software POGO to compute novel estimators of the disease comorbidity risks and patient stratification. Starting from an initial diagnosis, omics and clinical data of a patient the software identifies the association risk of disease comorbidities. The input of this software is the initial diagnosis of a patient and the output provides evidence of disease comorbidities. The functions of POGO offer flexibility for diagnostic applications to predict disease comorbidities, and can be easily integrated to high-throughput and clinical data analysis pipelines. POGO is compliant with the Bioconductor standard and it is freely available at www.cl.cam.ac.uk/~mam211/POGO/.

No MeSH data available.