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HOODS: finding context-specific neighborhoods of proteins, chemicals and diseases.

Palleja A, Jensen LJ - PeerJ (2015)

Bottom Line: Clustering algorithms are often used to find groups relevant in a specific context; however, they are not informed about this context.We present a simple algorithm, HOODS, which identifies context-specific neighborhoods of entities from a similarity matrix and a list of entities specifying the context.We illustrate its applicability by finding disease-specific neighborhoods of functionally associated proteins, kinase-specific neighborhoods of structurally similar inhibitors, and physiological-system-specific neighborhoods of interconnected diseases.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen N , Denmark ; The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen Ø , Denmark.

ABSTRACT
Clustering algorithms are often used to find groups relevant in a specific context; however, they are not informed about this context. We present a simple algorithm, HOODS, which identifies context-specific neighborhoods of entities from a similarity matrix and a list of entities specifying the context. We illustrate its applicability by finding disease-specific neighborhoods of functionally associated proteins, kinase-specific neighborhoods of structurally similar inhibitors, and physiological-system-specific neighborhoods of interconnected diseases. HOODS can be used via a simple interface at http://hoods.jensenlab.org, from where the source code can also be downloaded.

No MeSH data available.


Related in: MedlinePlus

Example neighborhoods produced by HOODS.(A) A protein neighborhood related to Leigh disease. (B) Two neighborhoods of kinase inhibitors that target VEGFR2, and (C) Two disease neighborhoods of related eye diseases. In all examples, the labeled entities are shown in blue. For the disease protein neighborhood and the two kinase inhibitor neighborhoods, the molecular structures are shown within the nodes where available. The widths of the edges represent the relative similarities, which are based on the STRING interaction scores between the proteins, the Tanimoto coefficients between the chemical compounds, and the number of genes shared between the diseases, respectively.
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fig-3: Example neighborhoods produced by HOODS.(A) A protein neighborhood related to Leigh disease. (B) Two neighborhoods of kinase inhibitors that target VEGFR2, and (C) Two disease neighborhoods of related eye diseases. In all examples, the labeled entities are shown in blue. For the disease protein neighborhood and the two kinase inhibitor neighborhoods, the molecular structures are shown within the nodes where available. The widths of the edges represent the relative similarities, which are based on the STRING interaction scores between the proteins, the Tanimoto coefficients between the chemical compounds, and the number of genes shared between the diseases, respectively.

Mentions: As an example of the disease neighborhoods we chose the Leigh disease, which is a rare neurometabolic disorder caused by mutations in genes encoding subunits of the mitochondrial respiratory chain or assembly factors of respiratory chain complexes (Diaz et al., 2011). The highest scoring neighborhood with more than one protein not associated to the disease contains 12 proteins, 10 of which are labeled with the disease: 8 assembly factors of cytochrome c oxidase (COX) (Diaz et al., 2011); one mitochondrial COX subunits (Diaz et al., 2011); one mitochondrial ATP synthase subunit (Kucharczyk, Rak & di Rago, 2009). In addition, there are two proteins that were not labeled with the disease, namely MT-CO3, and FDXR (Fig. 3A). MT-CO3 is one of three mitochondrial COX subunits and has already been linked to Leigh disease (Tiranti et al., 2000); the text mining strategy failed to find this link, but HOODS was able to recover it. FDXR is a new candidate protein that is likely to be involved in Leigh disease considering their role in the biosynthesis and covalent attachment of the prostetic heme A group of COX (Barros & Tzagoloff, 2002; Moraes, Diaz & Barrientos, 2004; Diaz et al., 2011).


HOODS: finding context-specific neighborhoods of proteins, chemicals and diseases.

Palleja A, Jensen LJ - PeerJ (2015)

Example neighborhoods produced by HOODS.(A) A protein neighborhood related to Leigh disease. (B) Two neighborhoods of kinase inhibitors that target VEGFR2, and (C) Two disease neighborhoods of related eye diseases. In all examples, the labeled entities are shown in blue. For the disease protein neighborhood and the two kinase inhibitor neighborhoods, the molecular structures are shown within the nodes where available. The widths of the edges represent the relative similarities, which are based on the STRING interaction scores between the proteins, the Tanimoto coefficients between the chemical compounds, and the number of genes shared between the diseases, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493695&req=5

fig-3: Example neighborhoods produced by HOODS.(A) A protein neighborhood related to Leigh disease. (B) Two neighborhoods of kinase inhibitors that target VEGFR2, and (C) Two disease neighborhoods of related eye diseases. In all examples, the labeled entities are shown in blue. For the disease protein neighborhood and the two kinase inhibitor neighborhoods, the molecular structures are shown within the nodes where available. The widths of the edges represent the relative similarities, which are based on the STRING interaction scores between the proteins, the Tanimoto coefficients between the chemical compounds, and the number of genes shared between the diseases, respectively.
Mentions: As an example of the disease neighborhoods we chose the Leigh disease, which is a rare neurometabolic disorder caused by mutations in genes encoding subunits of the mitochondrial respiratory chain or assembly factors of respiratory chain complexes (Diaz et al., 2011). The highest scoring neighborhood with more than one protein not associated to the disease contains 12 proteins, 10 of which are labeled with the disease: 8 assembly factors of cytochrome c oxidase (COX) (Diaz et al., 2011); one mitochondrial COX subunits (Diaz et al., 2011); one mitochondrial ATP synthase subunit (Kucharczyk, Rak & di Rago, 2009). In addition, there are two proteins that were not labeled with the disease, namely MT-CO3, and FDXR (Fig. 3A). MT-CO3 is one of three mitochondrial COX subunits and has already been linked to Leigh disease (Tiranti et al., 2000); the text mining strategy failed to find this link, but HOODS was able to recover it. FDXR is a new candidate protein that is likely to be involved in Leigh disease considering their role in the biosynthesis and covalent attachment of the prostetic heme A group of COX (Barros & Tzagoloff, 2002; Moraes, Diaz & Barrientos, 2004; Diaz et al., 2011).

Bottom Line: Clustering algorithms are often used to find groups relevant in a specific context; however, they are not informed about this context.We present a simple algorithm, HOODS, which identifies context-specific neighborhoods of entities from a similarity matrix and a list of entities specifying the context.We illustrate its applicability by finding disease-specific neighborhoods of functionally associated proteins, kinase-specific neighborhoods of structurally similar inhibitors, and physiological-system-specific neighborhoods of interconnected diseases.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen N , Denmark ; The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen Ø , Denmark.

ABSTRACT
Clustering algorithms are often used to find groups relevant in a specific context; however, they are not informed about this context. We present a simple algorithm, HOODS, which identifies context-specific neighborhoods of entities from a similarity matrix and a list of entities specifying the context. We illustrate its applicability by finding disease-specific neighborhoods of functionally associated proteins, kinase-specific neighborhoods of structurally similar inhibitors, and physiological-system-specific neighborhoods of interconnected diseases. HOODS can be used via a simple interface at http://hoods.jensenlab.org, from where the source code can also be downloaded.

No MeSH data available.


Related in: MedlinePlus