PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

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PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽

10.12688/f1000research.12947.2 ◽

2017 ◽

Vol 6 ◽

pp. 1969 ◽

Cited By ~ 2

Author(s):

Dongmin Jung ◽

Xijin Ge

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Biological Processes ◽

Bioconductor Package ◽

Biological Functions ◽

Ppi Network ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

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Computational Identification of Human Biological Processes and Protein Sequence Motifs Putatively Targeted by SARS-CoV-2 Proteins Using Protein–Protein Interaction Networks

Journal of Proteome Research ◽

10.1021/acs.jproteome.0c00422 ◽

2020 ◽

Vol 19 (11) ◽

pp. 4553-4566 ◽

Cited By ~ 1

Author(s):

Rachel Nadeau ◽

Soroush Shahryari Fard ◽

Amit Scheer ◽

Emily Hashimoto-Roth ◽

Dallas Nygard ◽

...

Keyword(s):

Protein Interaction ◽

Protein Sequence ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Biological Processes ◽

Sequence Motifs ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks ◽

Computational Identification ◽

Protein Sequence Motifs

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A Central Edge Selection Based Overlapping Community Detection Algorithm for the Detection of Overlapping Structures in Protein–Protein Interaction Networks

Molecules ◽

10.3390/molecules23102633 ◽

2018 ◽

Vol 23 (10) ◽

pp. 2633 ◽

Cited By ~ 6

Author(s):

Fang Zhang ◽

Anjun Ma ◽

Zhao Wang ◽

Qin Ma ◽

Bingqiang Liu ◽

...

Keyword(s):

Community Detection ◽

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Overlapping Community Detection ◽

Protein Protein Interaction ◽

Central Node ◽

Ppi Networks ◽

Overlapping Community ◽

Protein Protein Interaction Networks

Overlapping structures of protein–protein interaction networks are very prevalent in different biological processes, which reflect the sharing mechanism to common functional components. The overlapping community detection (OCD) algorithm based on central node selection (CNS) is a traditional and acceptable algorithm for OCD in networks. The main content of CNS is the central node selection and the clustering procedure. However, the original CNS does not consider the influence among the nodes and the importance of the division of the edges in networks. In this paper, an OCD algorithm based on a central edge selection (CES) algorithm for detection of overlapping communities of protein–protein interaction (PPI) networks is proposed. Different from the traditional CNS algorithms for OCD, the proposed algorithm uses community magnetic interference (CMI) to obtain more reasonable central edges in the process of CES, and employs a new distance between the non-central edge and the set of the central edges to divide the non-central edge into the correct cluster during the clustering procedure. In addition, the proposed CES improves the strategy of overlapping nodes pruning (ONP) to make the division more precisely. The experimental results on three benchmark networks and three biological PPI networks of Mus. musculus, Escherichia coli, and Cerevisiae show that the CES algorithm performs well.

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GASOLINE: a Cytoscape app for multiple local alignment of PPI networks

F1000Research ◽

10.12688/f1000research.4537.2 ◽

2014 ◽

Vol 3 ◽

pp. 140 ◽

Cited By ~ 7

Author(s):

Giovanni Micale ◽

Andrea Continella ◽

Alfredo Ferro ◽

Rosalba Giugno ◽

Alfredo Pulvirenti

Keyword(s):

Protein Interaction ◽

Sampling Strategy ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Local Alignment ◽

Stochastic Algorithm ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Alignment Task ◽

Protein Protein Interaction Networks

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithm. GASOLINE starts with the identification of sets of similar nodes, called seeds of the alignment. Alignments are then extended in a greedy manner and finally refined. Both the identification of seeds and the extension of alignments are performed through an iterative Gibbs sampling strategy. GASOLINE is a Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

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GASOLINE: a Cytoscape app for multiple local alignment of PPI networks

F1000Research ◽

10.12688/f1000research.4537.1 ◽

2014 ◽

Vol 3 ◽

pp. 140 ◽

Cited By ~ 1

Author(s):

Giovanni Micale ◽

Andrea Continella ◽

Alfredo Ferro ◽

Rosalba Giugno ◽

Alfredo Pulvirenti

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Local Alignment ◽

Stochastic Algorithms ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Alignment Task ◽

Protein Protein Interaction Networks ◽

Go Terms

Comparing protein interaction networks can reveal interesting patterns of interactions for a specific function or process in distantly related species. In this paper we present GASOLINE, a Cytoscape app for multiple local alignments of PPI (protein-protein interaction) networks. The app is based on the homonymous greedy and stochastic algorithms. To the authors knowledge, it is the first Cytoscape app for computing and visualizing local alignments, without requiring any post-processing operations. GO terms can be easily attached to the aligned proteins for further functional analysis of alignments. GASOLINE can perform the alignment task in few minutes, even for a large number of input networks.

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Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720015500262 ◽

2015 ◽

Vol 13 (05) ◽

pp. 1550026 ◽

Cited By ~ 4

Author(s):

Derui Wang ◽

Jingyu Hou

Keyword(s):

Protein Interaction ◽

Protein Function ◽

Prediction Performance ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Iterative Approach ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Protein Functions ◽

Protein Protein Interaction Networks

Protein–protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%–80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

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Predicting Cancer-Related Proteins in Protein-Protein Interaction Networks using Network Approach and SMO-SVM Algorithm

International Journal of Computer Applications ◽

10.5120/20129-2212 ◽

2015 ◽

Vol 115 (3) ◽

pp. 5-9 ◽

Cited By ~ 1

Author(s):

Richard Enyinnaya

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Network Approach ◽

Protein Protein Interaction ◽

Svm Algorithm ◽

Protein Protein Interaction Networks ◽

Related Proteins

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Identifying Protein Complexes from Dynamic Temporal Interval Protein-Protein Interaction Networks

BioMed Research International ◽

10.1155/2019/3726721 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Jinxiong Zhang ◽

Cheng Zhong ◽

Hai Xiang Lin ◽

Mian Wang

Keyword(s):

Gene Expression ◽

Protein Interaction ◽

Protein Complexes ◽

Biological Data ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Temporal Interval ◽

Protein Protein Interaction ◽

Identification Method ◽

Ppi Networks

Identification of protein complex is very important for revealing the underlying mechanism of biological processes. Many computational methods have been developed to identify protein complexes from static protein-protein interaction (PPI) networks. Recently, researchers are considering the dynamics of protein-protein interactions. Dynamic PPI networks are closer to reality in the cell system. It is expected that more protein complexes can be accurately identified from dynamic PPI networks. In this paper, we use the undulating degree above the base level of gene expression instead of the gene expression level to construct dynamic temporal PPI networks. Further we convert dynamic temporal PPI networks into dynamic Temporal Interval Protein Interaction Networks (TI-PINs) and propose a novel method to accurately identify more protein complexes from the constructed TI-PINs. Owing to preserving continuous interactions within temporal interval, the constructed TI-PINs contain more dynamical information for accurately identifying more protein complexes. Our proposed identification method uses multisource biological data to judge whether the joint colocalization condition, the joint coexpression condition, and the expanding cluster condition are satisfied; this is to ensure that the identified protein complexes have the features of colocalization, coexpression, and functional homogeneity. The experimental results on yeast data sets demonstrated that using the constructed TI-PINs can obtain better identification of protein complexes than five existing dynamic PPI networks, and our proposed identification method can find more protein complexes accurately than four other methods.

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Link clustering explains non-central essential genes in protein interaction networks

10.1101/532770 ◽

2019 ◽

Author(s):

Inhae Kim ◽

Heetak Lee ◽

Seong Kyu Han ◽

Kwanghwan Lee ◽

Sanguk Kim

Keyword(s):

Protein Interaction ◽

Human Cell ◽

Essential Genes ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Human Cell Lines ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Hierarchical Levels ◽

Human And Mouse

Essential genes (EGs) often form central nodes in protein-protein interaction (PPI) networks. However, many reports have shown that numerous EGs are non-central, suggesting that another principle governs gene essentiality. We propose link clustering as a distinct indicator of the essentiality for non-central nodes. Specifically, in various human and yeast PPI networks, we found that 29 to 47% of EGs were better characterized by link clustering than by centrality. Such non-central EGs with clustered links have significant impacts on communities at lower hierarchical levels, suggesting that their essentiality derives from functional dependency among relevant local neighbors, rather than their implication on global connectivity. Moreover, these non-central EGs exhibited several distinct characteristics: they tend to be younger and fast-evolving, and likely change their essentiality across different human cell lines and between human and mouse than central-EGs.

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