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HPIDB specifications


Unique identifier OMICS_02906
Restrictions to use None
Community driven No
Data access File download, Browse
User data submission Not allowed
Version 2.0
Maintained Yes


  • person_outline Bindu nduri

Publications for HPIDB

HPIDB citations


Comparative Genomics of the Zoonotic Pathogen Ehrlichia chaffeensis Reveals Candidate Type IV Effectors and Putative Host Cell Targets

PMCID: 5263134
PMID: 28180111
DOI: 10.3389/fcimb.2016.00204

[…] esis (Moumène and Meyer, ), and the T4Es we predicted using the S4TE algorithm for E. chaffeensis are good candidates for further biological analysis. In addition, the human interactome predicted via HPIDB provides useful information on the possible mode of action of these putative T4Es within the host cell. This study is proof-of-concept that comparative effectomics allows the identification of i […]


Literature Mining and Ontology based Analysis of Host Brucella Gene–Gene Interaction Network

Front Microbiol
PMCID: 4673313
PMID: 26696993
DOI: 10.3389/fmicb.2015.01386

[…] -Brucella interactions is still missing.Currently, there is very limited information regarding host-Brucella interactions in the host–pathogen interaction databases such as PHIDIAS (), PHISTO (), and HPIDB (). Most of the relevant information is only available in a textual format in the published scientific articles. In this study, our goal is to utilize text mining methods to extract host-Brucell […]


Application of Functional Genomics for Bovine Respiratory Disease Diagnostics

Bioinform Biol Insights
PMCID: 4620937
PMID: 26526746
DOI: 10.4137/BBI.S30525

[…] iewer. For a set of bovine genes that lack functional annotation, GOAnna at AgBase can add GOs based on sequence homology. Pathways resources such as Reactome,, KEGG, Unipathways, InnateDB, TFDB, and HPIDB can each add additional bits of relevant information to the list of genes, such as pathways represented, involvement in host innate immune responses, activity as TFs, etc. Resources such as the […]


Integrated inference and evaluation of host–fungi interaction networks

Front Microbiol
PMCID: 4523839
PMID: 26300851
DOI: 10.3389/fmicb.2015.00764

[…] To get a set of genes of H. sapiens and M. musculus that are known to be involved in host–pathogen interactions, the PPI information were downloaded from the HPIDB version 5/22/2014 and the PATRIC database version Mar2013. Further, all interspecies interactions that involved viral pathogens or the interaction types which are not related to a direct PPI suc […]


A review on computational systems biology of pathogen–host interactions

Front Microbiol
PMCID: 4391036
PMID: 25914674
DOI: 10.3389/fmicb.2015.00235

[…] er range of specific pathogens are VirHostNet (), VirusMentha () and ViRBase () for viruses, PATRIC () for bacteria and PHI-base () for bacterial, fungal, and oomycete pathogens. Finally, PHIDIAS (), HPIDB (), and PHISTO () are PHI databases for all pathogen types with known interaction data.HCVPro (HCV interaction database) is dedicated to only HCV, cataloging the characterized protein interactio […]


Prediction of host pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs

BMC Bioinformatics
PMCID: 4456996
PMID: 25887594
DOI: 10.1186/s12859-015-0535-y

[…] c local alignment search tool) [] was employed to compute sequence similarities. Query protein sequences were aligned against all sequences with known interactions stored in the databases BIPS [] and HPIDB []. BIPS and HPIDB are integrated databases including several data sources such as DIP [] and IntAct [], and both of the databases allow the users to set the parameters freely. The e-value and i […]


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HPIDB institution(s)
School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, USA; Institute for Genomics, Biocomputing and Biotechnology, College of Veterinary Medicine, Institute for Genomics, Mississippi State University, Mississippi State, MS, USA; College of Veterinary Medicine, Mississippi State University, Mississippi State, MS, USA
HPIDB funding source(s)
This work was supported by Agriculture and Food Research Initiative (2015-67015-23271) from the United States Department of Agriculture, National Institute of Food and Agriculture with partial support from National Institutes of Health (P20GM103646).

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