Data on overlapping brain disorders and emerging drug targets in human Dopamine Receptors Interaction Network

Avijit Podder,N Latha

doi:10.1016/j.dib.2017.04.001

Abstract

Intercommunication of Dopamine Receptors (DRs) with their associate protein partners is crucial to maintain regular brain function in human. Majority of the brain disorders arise due to malfunctioning of such communication process. Hence, contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature. In our earlier research article entitled “Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network” (Podder et al., 2014) [1], we had depicted a holistic interaction map of human Dopamine Receptors. Given emphasis on the topological parameters, we had characterized the functionality along with the vulnerable properties of the network. In support of this, we hereby provide an additional data highlighting the genetic overlapping of various brain disorders in the network. The data indicates the sharing nature of disease genes for various neurological and psychiatric disorders in dopamine receptors connecting protein-protein interactions network. The data also indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network.

Highlights

Bioinformatics Infrastructure Facility, Sri Venkateswara College (University of Delhi), Benito Juarez Road, Dhaula Kuan, 110021 New Delhi, India article info
Contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature
The data indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network. & 2017 Published by Elsevier Inc

Summary

Disease overlapping data analysis

Overlapping of genes between two diseases was measured using Jaccard or Tanimoto coefficient metrics [4] which compared the similarity and diversity of disease sets in our data. The intersecting set to the union set as the measure of similarity. It equals to zero if there are no intersecting elements and equals to one if all elements intersect. Based on the similarity matrix, the disease sets were further classified into different clusters using Neighbor Joining (NJ) algorithm which is a bottom-up or agglomerative clustering method to identify similar group of elements in the data [5]. The Phylip tool [6] was used to implement the NJ method in our data

Functional classification of data

Drug data analysis