Literature DB >> 23515528

PHISTO: pathogen-host interaction search tool.

Saliha Durmuş Tekir1, Tunahan Çakır, Emre Ardiç, Ali Semih Sayılırbaş, Gökhan Konuk, Mithat Konuk, Hasret Sarıyer, Azat Uğurlu, İlknur Karadeniz, Arzucan Özgür, Fatih Erdoğan Sevilgen, Kutlu Ö Ülgen.   

Abstract

SUMMARY: Knowledge of pathogen-host protein interactions is required to better understand infection mechanisms. The pathogen-host interaction search tool (PHISTO) is a web-accessible platform that provides relevant information about pathogen-host interactions (PHIs). It enables access to the most up-to-date PHI data for all pathogen types for which experimentally verified protein interactions with human are available. The platform also offers integrated tools for visualization of PHI networks, graph-theoretical analysis of targeted human proteins, BLAST search and text mining for detecting missing experimental methods. PHISTO will facilitate PHI studies that provide potential therapeutic targets for infectious diseases. AVAILABILITY: http://www.phisto.org. CONTACT: saliha.durmus@boun.edu.tr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Year:  2013        PMID: 23515528     DOI: 10.1093/bioinformatics/btt137

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  40 in total

Review 1.  A review on host-pathogen interactions: classification and prediction.

Authors:  R Sen; L Nayak; R K De
Journal:  Eur J Clin Microbiol Infect Dis       Date:  2016-07-29       Impact factor: 3.267

2.  PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.

Authors:  Tuan-Dung Le; Phuong D Nguyen; Dmitry Korkin; Thanh Thieu
Journal:  Database (Oxford)       Date:  2022-06-30       Impact factor: 4.462

3.  Genome-scale detection of positive selection in nine primates predicts human-virus evolutionary conflicts.

Authors:  Robin van der Lee; Laurens Wiel; Teunis J P van Dam; Martijn A Huynen
Journal:  Nucleic Acids Res       Date:  2017-10-13       Impact factor: 16.971

4.  Interface-Based Structural Prediction of Novel Host-Pathogen Interactions.

Authors:  Emine Guven-Maiorov; Chung-Jung Tsai; Buyong Ma; Ruth Nussinov
Journal:  Methods Mol Biol       Date:  2019

5.  Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19.

Authors:  Marios Tomazou; Marilena M Bourdakou; George Minadakis; Margarita Zachariou; Anastasis Oulas; Evangelos Karatzas; Eleni M Loizidou; Andrea C Kakouri; Christiana C Christodoulou; Kyriaki Savva; Maria Zanti; Anna Onisiforou; Sotiroula Afxenti; Jan Richter; Christina G Christodoulou; Theodoros Kyprianou; George Kolios; Nikolas Dietis; George M Spyrou
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

6.  Computational prediction of molecular pathogen-host interactions based on dual transcriptome data.

Authors:  Sylvie Schulze; Sebastian G Henkel; Dominik Driesch; Reinhard Guthke; Jörg Linde
Journal:  Front Microbiol       Date:  2015-02-06       Impact factor: 5.640

7.  ViRBase: a resource for virus-host ncRNA-associated interactions.

Authors:  Yanhui Li; Changliang Wang; Zhengqiang Miao; Xiaoman Bi; Deng Wu; Nana Jin; Liqiang Wang; Hao Wu; Kun Qian; Chunhua Li; Ting Zhang; Chunrui Zhang; Ying Yi; Hongyan Lai; Yongfei Hu; Lixin Cheng; Kwong-Sak Leung; Xiaobo Li; Fengmin Zhang; Kongning Li; Xia Li; Dong Wang
Journal:  Nucleic Acids Res       Date:  2014-10-01       Impact factor: 16.971

8.  Integrative Genomics-Based Discovery of Novel Regulators of the Innate Antiviral Response.

Authors:  Robin van der Lee; Qian Feng; Martijn A Langereis; Rob Ter Horst; Radek Szklarczyk; Mihai G Netea; Arno C Andeweg; Frank J M van Kuppeveld; Martijn A Huynen
Journal:  PLoS Comput Biol       Date:  2015-10-20       Impact factor: 4.475

9.  Integrated inference and evaluation of host-fungi interaction networks.

Authors:  Christian W Remmele; Christian H Luther; Johannes Balkenhol; Thomas Dandekar; Tobias Müller; Marcus T Dittrich
Journal:  Front Microbiol       Date:  2015-08-04       Impact factor: 5.640

Review 10.  Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.

Authors:  Padhmanand Sudhakar; Kathleen Machiels; Bram Verstockt; Tamas Korcsmaros; Séverine Vermeire
Journal:  Front Microbiol       Date:  2021-05-11       Impact factor: 5.640

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