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Zhu L, Zhang J, Zhang Y, Lang J, Xiang J, Bai X, Yan N, Tian G, Zhang H, Yang J. NAIGO: An Improved Method to Align PPI Networks Based on Gene Ontology and Graphlets. Front Bioeng Biotechnol 2020; 8:547. [PMID: 32637398 PMCID: PMC7318716 DOI: 10.3389/fbioe.2020.00547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 05/06/2020] [Indexed: 11/24/2022] Open
Abstract
With the development of high throughput technologies, there are more and more protein–protein interaction (PPI) networks available, which provide a need for efficient computational tools for network alignment. Network alignment is widely used to predict functions of certain proteins, identify conserved network modules, and study the evolutionary relationship across species or biological entities. However, network alignment is an NP-complete problem, and previous algorithms are usually slow or less accurate in aligning big networks like human vs. yeast. In this study, we proposed a fast yet accurate algorithm called Network Alignment by Integrating Biological Process (NAIGO). Specifically, we first divided the networks into subnets taking the advantage of known prior knowledge, such as gene ontology. For each subnet pair, we then developed a novel method to align them by considering both protein orthologous information and their local structural information. After that, we expanded the obtained local network alignments in a greedy manner. Taking the aligned pairs as seeds, we formulated the global network alignment problem as an assignment problem based on similarity matrix, which was solved by the Hungarian method. We applied NAIGO to align human and Saccharomyces cerevisiae S288c PPI network and compared the results with other popular methods like IsoRank, GRAAL, SANA, and NABEECO. As a result, our method outperformed the competitors by aligning more orthologous proteins or matched interactions. In addition, we found a few potential functional orthologous proteins such as RRM2B in human and DNA2 in S. cerevisiae S288c, which are related to DNA repair. We also identified a conserved subnet with six orthologous proteins EXO1, MSH3, MSH2, MLH1, MLH3, and MSH6, and six aligned interactions. All these proteins are associated with mismatch repair. Finally, we predicted a few proteins of S. cerevisiae S288c potentially involving in certain biological processes like autophagosome assembly.
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Affiliation(s)
- Lijuan Zhu
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
| | - Ju Zhang
- Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, and Beijing Key Laboratory of Emerging Infectious Diseases, Beijing, China
| | - Yi Zhang
- Department of Mathematics, Hebei University of Science & Technology, Shijiazhuang, China
| | | | - Ju Xiang
- Neuroscience Research Center & Department of Basic Medical Sciences, Changsha Medical University, Changsha, China.,School of Computer Science and Engineering, Central South University, Changsha, China
| | - Xiaogang Bai
- Department of Mathematics, Hebei University of Science & Technology, Shijiazhuang, China
| | - Na Yan
- Geneis Beijing Co., Ltd., Beijing, China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing, China
| | - Huajun Zhang
- College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
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Liu Z, Li M, Yan P, Zhu Z, Liao L, Chen Q, Luo Y, Li H, Li J, Wang Q, Huang Y, Wu Y. Transcriptome analysis of the effects of Hericium erinaceus polysaccharide on the lymphocyte homing in Muscovy duck reovirus-infected ducklings. Int J Biol Macromol 2019; 140:697-708. [PMID: 31422190 DOI: 10.1016/j.ijbiomac.2019.08.130] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 08/11/2019] [Accepted: 08/14/2019] [Indexed: 01/15/2023]
Abstract
Hericium erinaceus polysaccharide (HEP) is a bioactive substance present in the fruiting bodies of H. erinaceus. Previously we have shown that HEP can repair the intestinal injury caused by Muscovy duck reovirus (MDRV) infection in Muscovy ducklings. To examine the effect of HEP on intestine mucosal MDRV immunity and explore its possible mechanisms, an MDRV contact-infection model in the Muscovy ducklings was established. Transcriptome sequencing analysis was then performed to investigate the mechanism of action of HEP on intestine mucosal MDRV immunity. During the infection, the expression levels of genes involved in cellular activities (protein translation and binding, cytokine interaction, and adhesion molecules activities) in the infected ducklings were increased. The expression levels of adhesion molecules (α4β7, LFA-1) and chemotaxis cytokine receptors (CCR7, CCR9, and CCR10) were also significantly upregulated. Following HEP treatment, cellular activities and cytokines upregulated to various degrees play crucial roles in the immune defenses and antiviral activities of Muscovy ducklings. ELISA analysis results were consistent with the results of the transcriptome analysis. Overall, our results provide a basis for further studying the underlying mechanisms of HEP in regulating mucosal immunity and for the clinical application of HEP in controlling MDRV infection in the Muscovy duck industry.
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Affiliation(s)
- Zhenni Liu
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China; Ganzhou Animal Husbandry Research Institute, Gannan Academy of Sciences, Ganzhou, 341000, People's Republic of China
| | - Minghui Li
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China
| | - Ping Yan
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China
| | - Zheng Zhu
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China
| | - Lvyan Liao
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China; Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, Fujian Agricultural and Forestry University, Fuzhou 350002, People's Republic of China
| | - Qiang Chen
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China
| | - Yu Luo
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China
| | - Hongwen Li
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China
| | - Jian Li
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China; Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, Fujian Agricultural and Forestry University, Fuzhou 350002, People's Republic of China
| | - Quanxi Wang
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China; Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, Fujian Agricultural and Forestry University, Fuzhou 350002, People's Republic of China
| | - Yifan Huang
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China; Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, Fujian Agricultural and Forestry University, Fuzhou 350002, People's Republic of China
| | - Yijian Wu
- College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou 350002, People's Republic of China; Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, Fujian Agricultural and Forestry University, Fuzhou 350002, People's Republic of China.
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Reyes PFL, Michoel T, Joshi A, Devailly G. Meta-analysis of Liver and Heart Transcriptomic Data for Functional Annotation Transfer in Mammalian Orthologs. Comput Struct Biotechnol J 2017; 15:425-432. [PMID: 29187960 PMCID: PMC5691612 DOI: 10.1016/j.csbj.2017.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 08/10/2017] [Accepted: 08/11/2017] [Indexed: 11/30/2022] Open
Abstract
Functional annotation transfer across multi-gene family
orthologs can lead to functional misannotations. We hypothesised that co-expression
network will help predict functional orthologs amongst complex homologous gene
families. To explore the use of transcriptomic data available in public domain to
identify functionally equivalent ones from all predicted orthologs, we collected
genome wide expression data in mouse and rat liver from over 1500 experiments with
varied treatments. We used a hyper-graph clustering method to identify clusters of
orthologous genes co-expressed in both mouse and rat. We validated these clusters by
analysing expression profiles in each species separately, and demonstrating a high
overlap. We then focused on genes in 18 homology groups with one-to-many or
many-to-many relationships between two species, to discriminate between functionally
equivalent and non-equivalent orthologs. Finally, we further applied our method by
collecting heart transcriptomic data (over 1400 experiments) in rat and mouse to
validate the method in an independent tissue.
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Affiliation(s)
| | - Tom Michoel
- The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, UK
| | - Anagha Joshi
- The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, UK
| | - Guillaume Devailly
- The Roslin Institute, The University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Scotland, UK
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Zahiri J, Mohammad-Noori M, Ebrahimpour R, Saadat S, Bozorgmehr JH, Goldberg T, Masoudi-Nejad A. LocFuse: human protein-protein interaction prediction via classifier fusion using protein localization information. Genomics 2014; 104:496-503. [PMID: 25458812 DOI: 10.1016/j.ygeno.2014.10.006] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2014] [Revised: 09/28/2014] [Accepted: 10/02/2014] [Indexed: 12/20/2022]
Abstract
UNLABELLED Protein-protein interaction (PPI) detection is one of the central goals of functional genomics and systems biology. Knowledge about the nature of PPIs can help fill the widening gap between sequence information and functional annotations. Although experimental methods have produced valuable PPI data, they also suffer from significant limitations. Computational PPI prediction methods have attracted tremendous attentions. Despite considerable efforts, PPI prediction is still in its infancy in complex multicellular organisms such as humans. Here, we propose a novel ensemble learning method, LocFuse, which is useful in human PPI prediction. This method uses eight different genomic and proteomic features along with four types of different classifiers. The prediction performance of this classifier selection method was found to be considerably better than methods employed hitherto. This confirms the complex nature of the PPI prediction problem and also the necessity of using biological information for classifier fusion. The LocFuse is available at: http://lbb.ut.ac.ir/Download/LBBsoft/LocFuse. BIOLOGICAL SIGNIFICANCE The results revealed that if we divide proteome space according to the cellular localization of proteins, then the utility of some classifiers in PPI prediction can be improved. Therefore, to predict the interaction for any given protein pair, we can select the most accurate classifier with regard to the cellular localization information. Based on the results, we can say that the importance of different features for PPI prediction varies between differently localized proteins; however in general, our novel features, which were extracted from position-specific scoring matrices (PSSMs), are the most important ones and the Random Forest (RF) classifier performs best in most cases. LocFuse was developed with a user-friendly graphic interface and it is freely available for Linux, Mac OSX and MS Windows operating systems.
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Affiliation(s)
- Javad Zahiri
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran; Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Morteza Mohammad-Noori
- School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Reza Ebrahimpour
- Brain and Intelligent Systems Research Lab, Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran
| | - Samaneh Saadat
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Joseph H Bozorgmehr
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Tatyana Goldberg
- Department for Bioinformatics and Computational Biology, Faculty of Informatics, TUM, Garching 85748, Germany
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Yang J, Li J, Grünewald S, Wan XF. BinAligner: a heuristic method to align biological networks. BMC Bioinformatics 2013; 14 Suppl 14:S8. [PMID: 24266981 PMCID: PMC3851320 DOI: 10.1186/1471-2105-14-s14-s8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the evolutionary relationships of the compared species. However, such analyses are not trivial due to the complexity of network and high computational cost. Here we develop a mixture of global and local algorithm, BinAligner, for network alignments. Based on the hypotheses that the similarity between two vertices across networks would be context dependent and that the information from the edges and the structures of subnetworks can be more informative than vertices alone, two scoring schema, 1-neighborhood subnetwork and graphlet, were introduced to derive the scoring matrices between networks, besides the commonly used scoring scheme from vertices. Then the alignment problem is formulated as an assignment problem, which is solved by the combinatorial optimization algorithm, such as the Hungarian method. The proposed algorithm was applied and validated in aligning the protein-protein interaction network of Kaposi's sarcoma associated herpesvirus (KSHV) and that of varicella zoster virus (VZV). Interestingly, we identified several putative functional orthologous proteins with similar functions but very low sequence similarity between the two viruses. For example, KSHV open reading frame 56 (ORF56) and VZV ORF55 are helicase-primase subunits with sequence identity 14.6%, and KSHV ORF75 and VZV ORF44 are tegument proteins with sequence identity 15.3%. These functional pairs can not be identified if one restricts the alignment into orthologous protein pairs. In addition, BinAligner identified a conserved pathway between two viruses, which consists of 7 orthologous protein pairs and these proteins are connected by conserved links. This pathway might be crucial for virus packing and infection.
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Kolář M, Meier J, Mustonen V, Lässig M, Berg J. GraphAlignment: Bayesian pairwise alignment of biological networks. BMC SYSTEMS BIOLOGY 2012; 6:144. [PMID: 23171476 PMCID: PMC3573967 DOI: 10.1186/1752-0509-6-144] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2012] [Accepted: 11/07/2012] [Indexed: 11/10/2022]
Abstract
BACKGROUND With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. RESULTS We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0.On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6).On empirical bacterial protein-protein interaction networks (PIN) and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment. CONCLUSIONS The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.
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Affiliation(s)
- Michal Kolář
- Institut für Theoretische Physik, Universität zu Köln, Zülpicher Straße 77, D-50937 Köln, Germany
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