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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 > comorbiditymiRNA(“TNFRSF11A”, “Symbol”). The gene Symbol TNFRSF11A is used as input to the comorbiditymiRNA. We show the comorbidities originated using the miRNA-disease associations information. The size of the nodes represents the degree of associations.
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Figure 6: Output figure and statistics of > comorbiditymiRNA(“TNFRSF11A”, “Symbol”). The gene Symbol TNFRSF11A is used as input to the comorbiditymiRNA. We show the comorbidities originated using the miRNA-disease associations information. The size of the nodes represents the degree of associations.

Mentions: MicroRNA (miRNA) performs its regulatory function through its target genes. Two diseases are connected if they share at least one gene and/or one miRNA that is statistically significant dysregulated (Goh et al., 2007). miRNAs with similar functions tend to be associated with diseases with similar phenotypes, and vice versa (Lu et al., 2008). Based on these hypothesis, we used a framework to identify miRNA-disease associations through the direct identified association from the miRNA-disease association database and indirect association from the combined database of miRNA-target and gene-disease associations. POGO makes use of microRNA-target databases, miR2Disease (Jiang et al., 2009), HMDD (Li et al., 2014), and gene-disease association databases, OMIM (McKusick, 2007), to explore the mRNA and miRNA association between diseases. We filtered out invalid miRNA-disease associations with incorrect disease names or miRNA names. We used National Library of Medicine2 to obtain the correct disease names. We used miRBase to get the correct miRNA names (Kozomara and Griffiths-Jones, 2011). For a miRNA-disease pair, firstly, POGO maps the causal genes of the disease. It uses a p-value to measure the significance of the association between the miRNA and the disease. OMIM diseases ids are mapped with ICD-9-CM codes based on the literature (Park et al., 2009). Neighborhood based benchmark method is used to identify the comorbidity pattern among diseases. We build the associated network as a bipartite graph; each common neighbor node is selected based on the Jaccard coefficient method (Goh et al., 2007). comorbiditymiRNA function of POGO takes as input any of the following options: a list of gene/miRNA symbols, a list of Entrez gene ids, an ICD-9 code, an GO id or an OMIM id. This function provides disease comorbidity associations and network based on the disease-miRNA associations. comorbiditymiRNA requires two parameters: id list and id type. An example and its output is given in Figure 6.


How to build personalized multi-omics comorbidity profiles.

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

Output figure and statistics of > comorbiditymiRNA(“TNFRSF11A”, “Symbol”). The gene Symbol TNFRSF11A is used as input to the comorbiditymiRNA. We show the comorbidities originated using the miRNA-disease associations information. 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 6: Output figure and statistics of > comorbiditymiRNA(“TNFRSF11A”, “Symbol”). The gene Symbol TNFRSF11A is used as input to the comorbiditymiRNA. We show the comorbidities originated using the miRNA-disease associations information. The size of the nodes represents the degree of associations.
Mentions: MicroRNA (miRNA) performs its regulatory function through its target genes. Two diseases are connected if they share at least one gene and/or one miRNA that is statistically significant dysregulated (Goh et al., 2007). miRNAs with similar functions tend to be associated with diseases with similar phenotypes, and vice versa (Lu et al., 2008). Based on these hypothesis, we used a framework to identify miRNA-disease associations through the direct identified association from the miRNA-disease association database and indirect association from the combined database of miRNA-target and gene-disease associations. POGO makes use of microRNA-target databases, miR2Disease (Jiang et al., 2009), HMDD (Li et al., 2014), and gene-disease association databases, OMIM (McKusick, 2007), to explore the mRNA and miRNA association between diseases. We filtered out invalid miRNA-disease associations with incorrect disease names or miRNA names. We used National Library of Medicine2 to obtain the correct disease names. We used miRBase to get the correct miRNA names (Kozomara and Griffiths-Jones, 2011). For a miRNA-disease pair, firstly, POGO maps the causal genes of the disease. It uses a p-value to measure the significance of the association between the miRNA and the disease. OMIM diseases ids are mapped with ICD-9-CM codes based on the literature (Park et al., 2009). Neighborhood based benchmark method is used to identify the comorbidity pattern among diseases. We build the associated network as a bipartite graph; each common neighbor node is selected based on the Jaccard coefficient method (Goh et al., 2007). comorbiditymiRNA function of POGO takes as input any of the following options: a list of gene/miRNA symbols, a list of Entrez gene ids, an ICD-9 code, an GO id or an OMIM id. This function provides disease comorbidity associations and network based on the disease-miRNA associations. comorbiditymiRNA requires two parameters: id list and id type. An example and its output is given in Figure 6.

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.