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Network-based methods for predicting essential genes or proteins: a survey.基于网络的方法预测必需基因或蛋白质:综述。
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APID database: redefining protein-protein interaction experimental evidences and binary interactomes.APID 数据库:重新定义蛋白质-蛋白质相互作用的实验证据和二进制相互作用组。
Database (Oxford). 2019 Jan 1;2019:baz005. doi: 10.1093/database/baz005.
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STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.STRING v11:具有增强覆盖范围的蛋白质-蛋白质相互作用网络,支持在全基因组实验数据集的功能发现。
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The BioGRID interaction database: 2019 update.生物相互作用数据库(BioGRID):2019 年更新版。
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InterPro in 2019: improving coverage, classification and access to protein sequence annotations.InterPro 在 2019 年:提高蛋白质序列注释的覆盖范围、分类和访问。
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实时生成的 IHP-PING 综合人类蛋白质-蛋白质相互作用网络。

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

机构信息

Computational Biology Division, Department of Integrative Biomedical Sciences, IDM, CIDRI-Africa WT Centre, University of Cape Town, Health Sciences Campus. Anzio Rd, Observatory, 7925, South Africa.

African Institute for Mathematical Sciences, 5-7 Melrose Road, Muizenberg, 7945, Cape Town, South Africa.

出版信息

Brief Bioinform. 2021 Jul 20;22(4). doi: 10.1093/bib/bbaa277.

DOI:10.1093/bib/bbaa277
PMID:33129201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8293832/
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 As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated.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.

摘要

高通量测序技术的进步使得可公开获取的生物数据集呈指数级增长。在“大数据”驱动的“后基因组”背景下,人们正在进行大量工作来探索人类蛋白质-蛋白质相互作用 (PPI),以便进行基于系统水平的分析,从而揭示有用的信号,并获得更多的见解,以推进现有知识并回答特定的生物和健康问题。这些 PPI 是通过实验或计算预测的,存储在不同的在线数据库中,并且一些 PPI 资源会定期更新。与许多生物数据集一样,这些定期更新会使旧的 PPI 数据集变得过时。此外,虽然这些在线资源之间共享了许多相互作用,但每个资源都包含自己识别的 PPI,并且这些数据库都没有包含所有现有的人类 PPI 图谱。在这种情况下,必须能够整合或组合来自不同资源的交互数据集,以生成具有更高覆盖率和置信度的 PPI 图谱。为了使研究人员能够实时生成综合的人类 PPI 数据集,我们引入了综合人类蛋白质-蛋白质相互作用网络生成器 (IHP-PING) 工具。IHP-PING 是一个灵活的 Python 包,它可以从免费提供的在线资源中生成人类 PPI 网络。该工具提取和整合异构 PPI 数据集,以生成统一的 PPI 网络,并将其存储在本地,以用于进一步的应用。