Nimisha Asati
ILDgenDB Database
Published in
2 min readOct 7, 2018

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Interstitial Lung Disease Genetic Database

ILDgenDB is an integrated resource to facilitate genetic based ILD diagnosis and prognosis. This resource is a repertoire of ILDs related genetic data, and contains 299 literature curetted disease candidate genes (DCGs) and their association with SNPs, miRNA, biomarkers, pathways, biological processes, etc.

The DCGs involvement in ILDs was verified by mining disease related data from databases such as GAD, OMIM, GHR, CTD, DISEASE and GeneCards. Annotations and characterization of all DCGs were carried out through gene ontology (GO), phenotype and pathways mapping analyses. Associations of DCGs with regulatory elements such as miRNAs and SNPs were also provided. Biomarkers used for ILDs diagnostics were manually curetted and were incorporated into the database. SNP-Gene (DCGs), miRNA-DCGs, miRNA-SNP and SNP-miRNA-DCG interaction analyses were incorporated into the resource after manual verifications of their relevance. All these data were interlinked and integrated into the web resource, and user may access data using ten different diverse ‘key words” as queries.

Architecture of ILDgenDB knowledge resource

This comprehensive web resource provides genetic and other data primarily for 24 major interstitial lung diseases :

Salient features of ILDgenDB resource

  • An integrated high quality knowledge resource for ILD diseases candidate genes (DCGs), and their functional annotation and interactions with regulatory elements.
  • DCGs involvements in various biological processes are provided and analyzed to decipher mechanism of disease.
  • Candidate potential biomarkers of ILDs and their cross referencing results of ILD genomics experiments are also provided.
  • Potential significant SNP-Gene (DCGs), miRNA-DCGs, miRNA-SNP and SNP-miRNA-DCG interactions are provided to facilitate better therapeutics.
  • To provide systematic information retrieval for diverse users, this resource facilitates a query system with 10 different key words. This resource also provides “Browse” facility to access all genetic data through disease name.

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Nimisha Asati
ILDgenDB Database

Teaching Assistant | Data analysis | Big data | Statistical modelling | Deep Learning | LinkedIn: https://www.linkedin.com/in/Nimisha-Asati