Literature DB >> 33129201

IHP-PING-generating integrated human protein-protein interaction networks on-the-fly.

Gaston K Mazandu1,2,3, Christopher Hooper1, Kenneth Opap1, Funmilayo Makinde1,2, Victoria Nembaware3, Nicholas E Thomford3,4, Emile R Chimusa3, Ambroise Wonkam3, Nicola J Mulder1.   

Abstract

Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the 'big data' driven 'post-genomic' context, much work is being done to explore human protein-protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein-protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  high-throughput technology; human proteome; network analysis; post-genomic analysis; protein–protein interaction

Year:  2021        PMID: 33129201      PMCID: PMC8293832          DOI: 10.1093/bib/bbaa277

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  51 in total

1.  The large-scale organization of metabolic networks.

Authors:  H Jeong; B Tombor; R Albert; Z N Oltvai; A L Barabási
Journal:  Nature       Date:  2000-10-05       Impact factor: 49.962

Review 2.  Interactome: gateway into systems biology.

Authors:  Michael E Cusick; Niels Klitgord; Marc Vidal; David E Hill
Journal:  Hum Mol Genet       Date:  2005-09-14       Impact factor: 6.150

Review 3.  The minimum information required for reporting a molecular interaction experiment (MIMIx).

Authors:  Sandra Orchard; Lukasz Salwinski; Samuel Kerrien; Luisa Montecchi-Palazzi; Matthias Oesterheld; Volker Stümpflen; Arnaud Ceol; Andrew Chatr-aryamontri; John Armstrong; Peter Woollard; John J Salama; Susan Moore; Jérôme Wojcik; Gary D Bader; Marc Vidal; Michael E Cusick; Mark Gerstein; Anne-Claude Gavin; Giulio Superti-Furga; Jack Greenblatt; Joel Bader; Peter Uetz; Mike Tyers; Pierre Legrain; Stan Fields; Nicola Mulder; Michael Gilson; Michael Niepmann; Lyle Burgoon; Javier De Las Rivas; Carlos Prieto; Victoria M Perreau; Chris Hogue; Hans-Werner Mewes; Rolf Apweiler; Ioannis Xenarios; David Eisenberg; Gianni Cesareni; Henning Hermjakob
Journal:  Nat Biotechnol       Date:  2007-08       Impact factor: 54.908

4.  Network-based methods for predicting essential genes or proteins: a survey.

Authors:  Xingyi Li; Wenkai Li; Min Zeng; Ruiqing Zheng; Min Li
Journal:  Brief Bioinform       Date:  2020-03-23       Impact factor: 11.622

5.  Using the underlying biological organization of the Mycobacterium tuberculosis functional network for protein function prediction.

Authors:  Gaston K Mazandu; Nicola J Mulder
Journal:  Infect Genet Evol       Date:  2011-11-07       Impact factor: 3.342

6.  Function prediction and analysis of mycobacterium tuberculosis hypothetical proteins.

Authors:  Gaston K Mazandu; Nicola J Mulder
Journal:  Int J Mol Sci       Date:  2012-06-13       Impact factor: 6.208

7.  HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks.

Authors:  Gregorio Alanis-Lobato; Miguel A Andrade-Navarro; Martin H Schaefer
Journal:  Nucleic Acids Res       Date:  2016-10-24       Impact factor: 16.971

8.  Literature-curated protein interaction datasets.

Authors:  Michael E Cusick; Haiyuan Yu; Alex Smolyar; Kavitha Venkatesan; Anne-Ruxandra Carvunis; Nicolas Simonis; Jean-François Rual; Heather Borick; Pascal Braun; Matija Dreze; Jean Vandenhaute; Mary Galli; Junshi Yazaki; David E Hill; Joseph R Ecker; Frederick P Roth; Marc Vidal
Journal:  Nat Methods       Date:  2009-01       Impact factor: 28.547

9.  IIS--Integrated Interactome System: a web-based platform for the annotation, analysis and visualization of protein-metabolite-gene-drug interactions by integrating a variety of data sources and tools.

Authors:  Marcelo Falsarella Carazzolle; Lucas Miguel de Carvalho; Hugo Henrique Slepicka; Ramon Oliveira Vidal; Gonçalo Amarante Guimarães Pereira; Jörg Kobarg; Gabriela Vaz Meirelles
Journal:  PLoS One       Date:  2014-06-20       Impact factor: 3.240

10.  PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology.

Authors:  Aris Gioutlakis; Maria I Klapa; Nicholas K Moschonas
Journal:  PLoS One       Date:  2017-10-12       Impact factor: 3.240

View more
  2 in total

Review 1.  Reviewing and assessing existing meta-analysis models and tools.

Authors:  Funmilayo L Makinde; Milaine S S Tchamga; James Jafali; Segun Fatumo; Emile R Chimusa; Nicola Mulder; Gaston K Mazandu
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 13.994

2.  Investigations of Kidney Dysfunction-Related Gene Variants in Sickle Cell Disease Patients in Cameroon (Sub-Saharan Africa).

Authors:  Valentina J Ngo-Bitoungui; Suzanne Belinga; Khuthala Mnika; Tshepiso Masekoameng; Victoria Nembaware; René G Essomba; Francoise Ngo-Sack; Gordon Awandare; Gaston K Mazandu; Ambroise Wonkam
Journal:  Front Genet       Date:  2021-03-15       Impact factor: 4.599

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.