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Special Issue on “Biological Network Approaches and Applications”. Processes (Basel) 2023. [DOI: 10.3390/pr11020307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Biological phenomena comprise various interactions between genes and molecules [...]
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Das T, Kaur H, Gour P, Prasad K, Lynn AM, Prakash A, Kumar V. Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review. Brief Bioinform 2022; 23:6780269. [PMID: 36411673 DOI: 10.1093/bib/bbac442] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/12/2022] [Accepted: 09/13/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. OBJECTIVE This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. METHODS The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria. RESULTS We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.
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Affiliation(s)
- Trishala Das
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Harbinder Kaur
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Pratibha Gour
- Dept. of Plant Molecular Biology, University of Delhi, South Campus, New Delhi-110021, India
| | - Kartikay Prasad
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, UP-201303, India
| | - Andrew M Lynn
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi-110067, India
| | - Amresh Prakash
- Amity Institute of Integrative Sciences and Health, Amity University Haryana, Gurgaon-122413, India
| | - Vijay Kumar
- Amity Institute of Neuropsychology & Neurosciences (AINN), Amity University, Noida, UP-201303, India
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Basics on network theory to analyze biological systems: a hands-on outlook. Funct Integr Genomics 2022; 22:1433-1448. [PMID: 36227427 DOI: 10.1007/s10142-022-00907-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 10/03/2022] [Accepted: 10/04/2022] [Indexed: 11/04/2022]
Abstract
Biological processes result from interactions among molecules and cell-to-cell communications. In the last 50 years, network theory has empowered advances in understanding molecular networks' structure and dynamics that regulate biological systems. Adopting a network data analysis point of view at more laboratories might enrich their research capacity to generate forward working hypotheses. This work briefly describes network theory origins and provides basic graph analysis principles in biological systems, specific centrality measurements, and the main models for network structures. Also, we describe a workflow employing user-friendly free platforms to process, construct, and analyze transcriptome data from a network perspective. With this assay, we expect to encourage the implementation of network theory analysis on biological data in everyday laboratory research.
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Kumar D, Kashyap MK, Yu Z, Spaanderman I, Villa R, Kipps TJ, La Clair JJ, Burkart MD, Castro JE. Modulation of RNA splicing associated with Wnt signaling pathway using FD-895 and pladienolide B. Aging (Albany NY) 2022; 14:2081-2100. [PMID: 35230971 PMCID: PMC8954975 DOI: 10.18632/aging.203924] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 02/22/2022] [Indexed: 02/07/2023]
Abstract
Alterations in RNA splicing are associated with different malignancies, including leukemia, lymphoma, and solid tumors. The RNA splicing modulators such as FD-895 and pladienolide B have been investigated in different malignancies to target/modulate spliceosome for therapeutic purpose. Different cell lines were screened using an RNA splicing modulator to test in vitro cytotoxicity and the ability to modulate RNA splicing capability via induction of intron retention (using RT-PCR and qPCR). The Cignal Finder Reporter Array evaluated [pathways affected by the splice modulators in HeLa cells. Further, the candidates associated with the pathways were validated at protein level using western blot assay, and gene-gene interaction studies were carried out using GeneMANIA. We show that FD-895 and pladienolide B induces higher apoptosis levels than conventional chemotherapy in different solid tumors. In addition, both agents modulate Wnt signaling pathways and mRNA splicing. Specifically, FD-895 and pladienolide B significantly downregulates Wnt signaling pathway-associated transcripts (GSK3β and LRP5) and both transcript and proteins including LEF1, CCND1, LRP6, and pLRP6 at the transcript, total protein, and protein phosphorylation's levels. These results indicate FD-895 and pladienolide B inhibit Wnt signaling by decreasing LRP6 phosphorylation and modulating mRNA splicing through induction of intron retention in solid tumors.
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Affiliation(s)
- Deepak Kumar
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
- ThermoFisher Scientific, Carlsbad, CA 92008, USA
| | - Manoj K. Kashyap
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
- Amity Stem Cell Institute, Amity Medical School, Amity University Haryana, Panchgaon (Manesar), Haryana 122413, India
| | - Zhe Yu
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Ide Spaanderman
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Reymundo Villa
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
| | - Thomas J. Kipps
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
- CLL Research Consortium and Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - James J. La Clair
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
| | - Michael D. Burkart
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
| | - Januario E. Castro
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA
- CLL Research Consortium and Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
- Hematology-Oncology Division, Mayo Clinic, Phoenix, AZ 85054, USA
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5
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Trapotsi MA, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data, methods and integration. RSC Chem Biol 2022; 3:170-200. [PMID: 35360890 PMCID: PMC8827085 DOI: 10.1039/d1cb00069a] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022] Open
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
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Affiliation(s)
- Maria-Anna Trapotsi
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
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6
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Kaur H, van der Feltz C, Sun Y, Hoskins AA. Network theory reveals principles of spliceosome structure and dynamics. Structure 2022; 30:190-200.e2. [PMID: 34592160 PMCID: PMC8741635 DOI: 10.1016/j.str.2021.09.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 06/30/2021] [Accepted: 09/08/2021] [Indexed: 02/07/2023]
Abstract
Cryoelectron microscopy has revolutionized spliceosome structural biology, and structures representing much of the splicing process have been determined. Comparison of these structures is challenging due to extreme dynamics of the splicing machinery and the thousands of changing interactions during splicing. We have used network theory to analyze splicing factor interactions by constructing structure-based networks from protein-protein, protein-RNA, and RNA-RNA interactions found in eight different spliceosome structures. Our analyses reveal that connectivity dynamics result in step-specific impacts of factors on network topology. The spliceosome's connectivity is focused on the active site, in part due to contributions from nonglobular proteins. Many essential factors exhibit large shifts in centralities during splicing. Others show transiently high betweenness centralities at certain stages, thereby suggesting mechanisms for regulating splicing by briefly bridging otherwise poorly connected network nodes. These observations provide insights into organizing principles of the spliceosome and provide frameworks for comparative analysis of other macromolecular machines.
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Affiliation(s)
- Harpreet Kaur
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706 USA,These authors contributed equally
| | - Clarisse van der Feltz
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706 USA,College of Arts and Sciences, Northwest University, Kirkland, Washington, 98033 USA,These authors contributed equally
| | - Yichen Sun
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706 USA
| | - Aaron A. Hoskins
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706 USA,Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706 USA,Correspondence:
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7
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Redhu N, Thakur Z. Network biology and applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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8
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Porras P, Orchard S, Licata L. IMEx Databases: Displaying Molecular Interactions into a Single, Standards-Compliant Dataset. Methods Mol Biol 2022; 2449:27-42. [PMID: 35507258 DOI: 10.1007/978-1-0716-2095-3_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Molecular interaction databases aim to systematically capture and organize the experimental interaction information described in the scientific literature. These data can then be used to perform network analysis, to assign putative roles to uncharacterized proteins and to investigate their involvement in cellular pathways.This chapter gives a brief overview of publicly available molecular interaction databases and focuses on the members of the IMEx Consortium, on their curation policies and standard data formats. All of the goals achieved by IMEx databases over the last 15 years, the data types provided and the many different ways in which such data can be utilized by the research community, are described in detail. The IMEx databases curate molecular interaction data to the highest caliber, following a detailed curation model and supplying rich metadata by employing common curation rules and harmonized standards. The IMEx Consortium provides comprehensively annotated molecular interaction data integrated into a single, non-redundant, open access dataset.
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Affiliation(s)
- Pablo Porras
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Sandra Orchard
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, UK
| | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Rome, Italy.
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9
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Nakazawa MA, Tamada Y, Tanaka Y, Ikeguchi M, Higashihara K, Okuno Y. Novel cancer subtyping method based on patient-specific gene regulatory network. Sci Rep 2021; 11:23653. [PMID: 34880275 PMCID: PMC8654869 DOI: 10.1038/s41598-021-02394-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 11/12/2021] [Indexed: 12/11/2022] Open
Abstract
The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.
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Affiliation(s)
| | - Yoshinori Tamada
- Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.
- Innovation Center for Health Promotion, Hirosaki University, Hirosaki, 036-8562, Japan.
| | - Yoshihisa Tanaka
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, 606-8507, Japan
- Biomedical Computational Intelligence Unit, HPC- and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, Kobe, 650-0047, Japan
| | - Marie Ikeguchi
- Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Kako Higashihara
- Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan
| | - Yasushi Okuno
- Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.
- Biomedical Computational Intelligence Unit, HPC- and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, Kobe, 650-0047, Japan.
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10
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Hao M, Zhang H, Hu Z, Jiang X, Song Q, Wang X, Wang J, Liu Z, Wang X, Li Y, Jin L. Phenotype correlations reveal the relationships of physiological systems underlying human ageing. Aging Cell 2021; 20:e13519. [PMID: 34825761 PMCID: PMC8672793 DOI: 10.1111/acel.13519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/18/2021] [Accepted: 11/03/2021] [Indexed: 01/02/2023] Open
Abstract
Ageing is characterized by degeneration and loss of function across multiple physiological systems. To study the mechanisms and consequences of ageing, several metrics have been proposed in a hierarchical model, including biological, phenotypic and functional ageing. In particular, phenotypic ageing and interconnected changes in multiple physiological systems occur in all ageing individuals over time. Recently, phenotypic age, a new ageing measure, was proposed to capture morbidity and mortality risk across diverse subpopulations in US cohort studies. Although phenotypic age has been widely used, it may overlook the complex relationships among phenotypic biomarkers. Considering the correlation structure of these phenotypic biomarkers, we proposed a composite phenotype analysis (CPA) strategy to analyse 71 biomarkers from 2074 individuals in the Rugao Longitudinal Ageing Study. CPA grouped these biomarkers into 18 composite phenotypes according to their internal correlation, and these composite phenotypes were mostly consistent with prior findings. In addition, compared with prior findings, this strategy exhibited some different yet important implications. For example, the indicators of kidney and cardiovascular functions were tightly connected, implying internal interactions. The composite phenotypes were further verified through associations with functional metrics of ageing, including disability, depression, cognitive function and frailty. Compared to age alone, these composite phenotypes had better predictive performances for functional metrics of ageing. In summary, CPA could reveal the hidden relationships of physiological systems and identify the links between physiological systems and functional ageing metrics, thereby providing novel insights into potential mechanisms underlying human ageing.
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Affiliation(s)
- Meng Hao
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
| | - Hui Zhang
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
- National Clinical Research Center for Ageing and MedicineHuashan HospitalFudan UniversityShanghaiChina
| | - Zixin Hu
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
| | - Xiaoyan Jiang
- Key Laboratory of Arrhythmias of the Ministry of Education of ChinaTongji University School of MedicineShanghaiChina
| | - Qi Song
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
| | - Xi Wang
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
| | - Jiucun Wang
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058)Chinese Academy of Medical SciencesBeijingChina
| | - Zuyun Liu
- Center for Clinical Big Data and AnalyticsSecond Affiliated Hospital and Department of Big Data in Health ScienceSchool of Public HealthZhejiang University School of MedicineHangzhouZhejiangChina
| | - Xiaofeng Wang
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
- National Clinical Research Center for Ageing and MedicineHuashan HospitalFudan UniversityShanghaiChina
| | - Yi Li
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058)Chinese Academy of Medical SciencesBeijingChina
| | - Li Jin
- State Key Laboratory of Genetic EngineeringCollaborative Innovation Center for Genetics and DevelopmentSchool of Life Sciences and Human Phenome InstituteFudan UniversityShanghaiChina
- Research Unit of Dissecting the Population Genetics and Developing New Technologies for Treatment and Prevention of Skin Phenotypes and Dermatological Diseases (2019RU058)Chinese Academy of Medical SciencesBeijingChina
- International Human Phenome InstitutesShanghaiChina
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11
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Zhang X, Wang W, Ren CX, Dai DQ. Learning representation for multiple biological networks via a robust graph regularized integration approach. Brief Bioinform 2021; 23:6381251. [PMID: 34607360 DOI: 10.1093/bib/bbab409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 08/23/2021] [Accepted: 09/06/2021] [Indexed: 01/18/2023] Open
Abstract
Learning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network-based inference tasks, including drug-target interaction prediction, gene function identification and fine-grained species categorization, are conducted using representation features learned from our method. Biological networks at different scales and levels of sparsity are involved. Experimental results on real-world data show that the proposed method has robust performance compared with alternatives. Overall, by eliminating noise and integrating effectively, the proposed method is able to learn useful representations from multiple biological networks.
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Affiliation(s)
- Xiwen Zhang
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China
| | - Weiwen Wang
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China
| | - Chuan-Xian Ren
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China
| | - Dao-Qing Dai
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China
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Oliveira de Biagi CA, Nociti RP, Brotto DB, Funicheli BO, Cássia Ruy PD, Bianchi Ximenez JP, Alves Figueiredo DL, Araújo Silva W. CeTF: an R/Bioconductor package for transcription factor co-expression networks using regulatory impact factors (RIF) and partial correlation and information (PCIT) analysis. BMC Genomics 2021; 22:624. [PMID: 34416858 PMCID: PMC8379792 DOI: 10.1186/s12864-021-07918-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 07/30/2021] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Finding meaningful gene-gene interaction and the main Transcription Factors (TFs) in co-expression networks is one of the most important challenges in gene expression data mining. RESULTS Here, we developed the R package "CeTF" that integrates the Partial Correlation with Information Theory (PCIT) and Regulatory Impact Factors (RIF) algorithms applied to gene expression data from microarray, RNA-seq, or single-cell RNA-seq platforms. This approach allows identifying the transcription factors most likely to regulate a given network in different biological systems - for example, regulation of gene pathways in tumor stromal cells and tumor cells of the same tumor. This pipeline can be easily integrated into the high-throughput analysis. To demonstrate the CeTF package application, we analyzed gastric cancer RNA-seq data obtained from TCGA (The Cancer Genome Atlas) and found the HOXB3 gene as the second most relevant TFs with a high regulatory impact (TFs-HRi) regulating gene pathways in the cell cycle. CONCLUSION This preliminary finding shows the potential of CeTF to list master regulators of gene networks. CeTF was designed as a user-friendly tool that provides many highly automated functions without requiring the user to perform many complicated processes. It is available on Bioconductor ( http://bioconductor.org/packages/CeTF ) and GitHub ( http://github.com/cbiagii/CeTF ).
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Affiliation(s)
- Carlos Alberto Oliveira de Biagi
- Department of Genetics at Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.,Center for Cell-Based Therapy (CEPID/FAPESP), National Institute of Science and Technology in Stem Cell and Cell Therapy (INCTC/CNPq), Regional Blood Center of Ribeirão Preto, Ribeirão Preto, Brazil.,Institute for Cancer Research, IPEC, Guarapuava, Brazil
| | - Ricardo Perecin Nociti
- Center for Cell-Based Therapy (CEPID/FAPESP), National Institute of Science and Technology in Stem Cell and Cell Therapy (INCTC/CNPq), Regional Blood Center of Ribeirão Preto, Ribeirão Preto, Brazil.,Laboratory of Molecular Morphophysiology and Development, Department of Veterinary Medicine, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, Brazil
| | - Danielle Barbosa Brotto
- Department of Genetics at Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.,Center for Cell-Based Therapy (CEPID/FAPESP), National Institute of Science and Technology in Stem Cell and Cell Therapy (INCTC/CNPq), Regional Blood Center of Ribeirão Preto, Ribeirão Preto, Brazil
| | - Breno Osvaldo Funicheli
- Center for Cell-Based Therapy (CEPID/FAPESP), National Institute of Science and Technology in Stem Cell and Cell Therapy (INCTC/CNPq), Regional Blood Center of Ribeirão Preto, Ribeirão Preto, Brazil
| | - Patrícia de Cássia Ruy
- Center for Cell-Based Therapy (CEPID/FAPESP), National Institute of Science and Technology in Stem Cell and Cell Therapy (INCTC/CNPq), Regional Blood Center of Ribeirão Preto, Ribeirão Preto, Brazil.,Center for Medical Genomics, HCFMRP/USP, Ribeirão Preto, Brazil
| | - João Paulo Bianchi Ximenez
- Center for Cell-Based Therapy (CEPID/FAPESP), National Institute of Science and Technology in Stem Cell and Cell Therapy (INCTC/CNPq), Regional Blood Center of Ribeirão Preto, Ribeirão Preto, Brazil
| | - David Livingstone Alves Figueiredo
- Institute for Cancer Research, IPEC, Guarapuava, Brazil.,Department of Medicine, Midwest State University of Paraná-UNICENTRO, Guarapuava, Brazil
| | - Wilson Araújo Silva
- Department of Genetics at Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil. .,Center for Cell-Based Therapy (CEPID/FAPESP), National Institute of Science and Technology in Stem Cell and Cell Therapy (INCTC/CNPq), Regional Blood Center of Ribeirão Preto, Ribeirão Preto, Brazil. .,Institute for Cancer Research, IPEC, Guarapuava, Brazil. .,Center for Integrative Systems Biology (CISBi) - NAP/USP, University of São Paulo, Ribeirão Preto, Brazil.
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13
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Okinaga Y, Kyogoku D, Kondo S, Nagano AJ, Hirose K. Relationship between gene regulation network structure and prediction accuracy in high dimensional regression. Sci Rep 2021; 11:11483. [PMID: 34075095 PMCID: PMC8169869 DOI: 10.1038/s41598-021-90791-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 05/17/2021] [Indexed: 11/09/2022] Open
Abstract
The least absolute shrinkage and selection operator (lasso) and principal component regression (PCR) are popular methods of estimating traits from high-dimensional omics data, such as transcriptomes. The prediction accuracy of these estimation methods is highly dependent on the covariance structure, which is characterized by gene regulation networks. However, the manner in which the structure of a gene regulation network together with the sample size affects prediction accuracy has not yet been sufficiently investigated. In this study, Monte Carlo simulations are conducted to investigate the prediction accuracy for several network structures under various sample sizes. When the gene regulation network is a random graph, a sufficiently large number of observations are required to ensure good prediction accuracy with the lasso. The PCR provided poor prediction accuracy regardless of the sample size. However, a real gene regulation network is likely to exhibit a scale-free structure. In such cases, the simulation indicates that a relatively small number of observations, such as [Formula: see text], is sufficient to allow the accurate prediction of traits from a transcriptome with the lasso.
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Affiliation(s)
- Yuichi Okinaga
- Graduate School of Mathematics, Kyushu University, 744 Motooka, Fukuoka, 819-0395, Japan
| | - Daisuke Kyogoku
- The Museum of Nature and Human Activities, 6 Yayoigaoka, Sanda, Hyogo, 669-1546, Japan
| | - Satoshi Kondo
- Agriculture and Biotechnology Business Division, Toyota Motor Corporation, Miyoshi, Aichi, 470-0201, Japan
| | - Atsushi J Nagano
- Faculty of Agriculture, Ryukoku University, Otsu, Shiga, 520-2194, Japan. .,Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0017, Japan.
| | - Kei Hirose
- Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Fukuoka, 819-0395, Japan. .,RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
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14
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Srinivas Bharadhwaj V, Ali M, Birkenbihl C, Mubeen S, Lehmann J, Hofmann-Apitius M, Tapley Hoyt C, Domingo-Fernández D. CLEP: A Hybrid Data- and Knowledge- Driven Framework for Generating Patient Representations. Bioinformatics 2021; 37:3311-3318. [PMID: 33964127 PMCID: PMC8504642 DOI: 10.1093/bioinformatics/btab340] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/29/2021] [Accepted: 05/03/2021] [Indexed: 12/29/2022] Open
Abstract
Summary As machine learning and artificial intelligence increasingly attain a larger number of applications in the biomedical domain, at their core, their utility depends on the data used to train them. Due to the complexity and high dimensionality of biomedical data, there is a need for approaches that combine prior knowledge around known biological interactions with patient data. Here, we present CLinical Embedding of Patients (CLEP), a novel approach that generates new patient representations by leveraging both prior knowledge and patient-level data. First, given a patient-level dataset and a knowledge graph containing relations across features that can be mapped to the dataset, CLEP incorporates patients into the knowledge graph as new nodes connected to their most characteristic features. Next, CLEP employs knowledge graph embedding models to generate new patient representations that can ultimately be used for a variety of downstream tasks, ranging from clustering to classification. We demonstrate how using new patient representations generated by CLEP significantly improves performance in classifying between patients and healthy controls for a variety of machine learning models, as compared to the use of the original transcriptomics data. Furthermore, we also show how incorporating patients into a knowledge graph can foster the interpretation and identification of biological features characteristic of a specific disease or patient subgroup. Finally, we released CLEP as an open source Python package together with examples and documentation. Availability and implementation CLEP is available to the bioinformatics community as an open source Python package at https://github.com/hybrid-kg/clep under the Apache 2.0 License. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vinay Srinivas Bharadhwaj
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany
| | - Mehdi Ali
- Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany.,Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Dresden and Sankt Augustin, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany.,Fraunhofer Center for Machine Learning, Germany
| | - Jens Lehmann
- Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany.,Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Dresden and Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany.,Fraunhofer Center for Machine Learning, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany.,Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn 53113, Germany.,Fraunhofer Center for Machine Learning, Germany
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15
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Zhang L, Wang M, Castan A, Hjalmarsson H, Chotteau V. Probabilistic model by Bayesian network for the prediction of antibody glycosylation in perfusion and fed-batch cell cultures. Biotechnol Bioeng 2021; 118:3447-3459. [PMID: 33788254 DOI: 10.1002/bit.27769] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 03/05/2021] [Accepted: 03/12/2021] [Indexed: 01/01/2023]
Abstract
Glycosylation is a critical quality attribute of therapeutic monoclonal antibodies (mAbs). The glycan pattern can have a large impact on the immunological functions, serum half-life and stability. The medium components and cultivation parameters are known to potentially influence the glycosylation profile. Mathematical modelling provides a strategy for rational design and control of the upstream bioprocess. However, the kinetic models usually contain a very large number of unknown parameters, which limit their practical applications. In this article, we consider the metabolic network of N-linked glycosylation as a Bayesian network (BN) and calculate the fluxes of the glycosylation process as joint probability using the culture parameters as inputs. The modelling approach is validated with data of different Chinese hamster ovary cell cultures in pseudo perfusion, perfusion, and fed batch cultures, all showing very good predictive capacities. In cases where a large number of cultivation parameters is available, it is shown here that principal components analysis can efficiently be employed for a dimension reduction of the inputs compared to Pearson correlation analysis and feature importance by decision tree. The present study demonstrates that BN model can be a powerful tool in upstream process and medium development for glycoprotein productions.
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Affiliation(s)
- Liang Zhang
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden
| | - MingLiang Wang
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Stockholm, Sweden
| | | | - Håkan Hjalmarsson
- AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Division of Decision and Control System, School of Electrical Engineering and Computer Science, KTH-Royal Institute of Technology, Stockholm, Sweden.,Digital Futures - KTH Royal Institute of Technology, Stockholm, Sweden
| | - Veronique Chotteau
- Department of Industrial Biotechnology, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, KTH Royal Institute of Technology, Stockholm, Sweden.,Digital Futures - KTH Royal Institute of Technology, Stockholm, Sweden
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16
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Katz S, Song J, Webb KP, Lounsbury NW, Bryant CE, Fraser IDC. SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays. Cell Syst 2021; 12:338-352.e5. [PMID: 33894945 DOI: 10.1016/j.cels.2021.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 11/25/2020] [Accepted: 03/03/2021] [Indexed: 01/13/2023]
Abstract
Hit selection from high-throughput assays remains a critical bottleneck in realizing the potential of omic-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs. The specific limitations of these individual approaches and the lack of a systematic way by which to integrate their rankings have contributed to limited overlap in the reported results from comparable genome-wide studies and costly inefficiencies in secondary validation efforts. Using comparative analysis of parallel independent studies as a benchmark, we characterize the specific complementary contributions of each approach and demonstrate an optimal framework to integrate these methods. We describe selection by iterative pathway group and network analysis looping (SIGNAL), an integrated, iterative approach that uses both pathway and network methods to optimize gene prioritization. SIGNAL is accessible as a rapid user-friendly web-based application (https://signal.niaid.nih.gov). A record of this paper's transparent peer review is included in the Supplemental information.
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Affiliation(s)
- Samuel Katz
- NIAID, National Institutes of Health, Laboratory of Immune System Biology, Bethesda, MD 20892, USA; University of Cambridge, Department of Veterinary Medicine, Cambridge, UK
| | - Jian Song
- NIAID, National Institutes of Health, Laboratory of Immune System Biology, Bethesda, MD 20892, USA
| | - Kyle P Webb
- NIAID, National Institutes of Health, Laboratory of Immune System Biology, Bethesda, MD 20892, USA
| | - Nicolas W Lounsbury
- NIAID, National Institutes of Health, Laboratory of Immune System Biology, Bethesda, MD 20892, USA
| | - Clare E Bryant
- University of Cambridge, Department of Veterinary Medicine, Cambridge, UK
| | - Iain D C Fraser
- NIAID, National Institutes of Health, Laboratory of Immune System Biology, Bethesda, MD 20892, USA.
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17
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Abstract
INTRODUCTION Traumatic brain injury (TBI) is associated with secondary injury to the central nervous system (CNS) via inflammatory mechanisms. The combination of polytrauma and TBI further exacerbates the inflammatory response to injury; however, combined injury phenomena have not been thoroughly studied. In this study, we examined the inflammatory differences between patients with TBI versus patients with polytrauma, but no TBI (polytrauma). We hypothesize that patients with TBI have a heightened early inflammatory response compared with polytrauma. METHODS We conducted a single-center retrospective study of a cohort of patients with polytrauma, who were enrolled in the PROPPR study. These patients had blood samples prospectively collected at eight time points in the first 3 days of admission. Using radiological data to determine TBI, our polytrauma cohort was dichotomized into TBI (n = 30) or polytrauma (n = 54). Inflammatory biomarkers were measured using ELISA. Data across time were compared for TBI versus polytrauma groups using Wilcoxon rank-sum test. Network analysis techniques were used to systematically characterize the inflammatory responses at admission. RESULTS Patients with TBI (51.6%) had a higher 30-day mortality compared with polytrauma (16.9%) (P <0.001). Expression levels of IL6, IL8, and CCL2 were elevated from the 2-h through 24-h time points, becoming significant at the 6-h time point (IL6, IL8, and CCL2; P <0.05) (). CSF3 showed a similar pattern, but did not attain significance. TBI and polytrauma networks underwent diverging trends from admission to the 6-h time point. CONCLUSION Patients with TBI demonstrated upregulations in proinflammatory cytokines IL6, IL8, and CCL2. Utilizing informatics methods, we were able to identify temporal differences in network trends, as well as uncharacterized cytokines and chemokines in TBI. These data suggest TBI induces a distinct inflammatory response and pathologically heightened inflammatory response in the presence of polytrauma and may propagate worsened patient outcomes including mortality.
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18
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Wei T, Fa B, Luo C, Johnston L, Zhang Y, Yu Z. An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery. Front Genet 2021; 11:613033. [PMID: 33488678 PMCID: PMC7820902 DOI: 10.3389/fgene.2020.613033] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 11/25/2020] [Indexed: 12/25/2022] Open
Abstract
Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.
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Affiliation(s)
- Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Botao Fa
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Chengwen Luo
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Luke Johnston
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.,SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
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19
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Vrahatis AG, Kotsireas IS, Vlamos P. Detecting Common Pathways and Key Molecules of Neurodegenerative Diseases from the Topology of Molecular Networks. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1194:409-421. [PMID: 32468556 DOI: 10.1007/978-3-030-32622-7_38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
MotivationNeurodegenerative diseases (NDs), including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease, occur as a result of neurodegenerative processes. Thus, it has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels. However, traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Discovering this overlap offers hope for therapeutic advances that could ameliorate many ND simultaneously. In parallel, in the last decade, systems biology approaches have become a reliable choice in complex disease analysis for gaining more delicate biological insights and have enabled the comprehension of the higher order functions of the biological systems.ResultsToward this orientation, we developed a systems biology approach for the identification of common links and pathways of ND, based on well-established and novel topological and functional measures. For this purpose, a molecular pathway network was constructed, using molecular interactions and relations of four main neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease). Our analysis captured the overlapped subregions forming molecular subpathways fully enriched in these four NDs. Also, it exported molecules that act as bridges, hubs, and key players for neurodegeneration concerning either their topology or their functional role.ConclusionUnderstanding these common links and central topologies under the perspective of systems biology and network theory and greater insights are provided to uncover the complex neurodegeneration processes.
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Affiliation(s)
| | - Ilias S Kotsireas
- Department of Physics and Computer Science, Wilfrid Laurier University, Waterloo, Canada
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20
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Burke PEP, Campos CBDL, Costa LDF, Quiles MG. A biochemical network modeling of a whole-cell. Sci Rep 2020; 10:13303. [PMID: 32764598 PMCID: PMC7411072 DOI: 10.1038/s41598-020-70145-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 07/23/2020] [Indexed: 01/18/2023] Open
Abstract
All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.
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Affiliation(s)
- Paulo E P Burke
- University of São Paulo, Bioinformatics Graduate Program, São Carlos, SP, Brazil.
| | - Claudia B de L Campos
- Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil
| | - Luciano da F Costa
- São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, Brazil
| | - Marcos G Quiles
- Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, SP, Brazil
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21
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Lau A, So HC. Turning genome-wide association study findings into opportunities for drug repositioning. Comput Struct Biotechnol J 2020; 18:1639-1650. [PMID: 32670504 PMCID: PMC7334463 DOI: 10.1016/j.csbj.2020.06.015] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 02/02/2023] Open
Abstract
Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored. The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations. Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.
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Affiliation(s)
- Alexandria Lau
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hon-Cheong So
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Zoology Institute of Zoology and The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong SAR, China
- Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong SAR, China
- Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China
- Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Corresponding author at: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
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22
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Silverman EK, Schmidt HHHW, Anastasiadou E, Altucci L, Angelini M, Badimon L, Balligand JL, Benincasa G, Capasso G, Conte F, Di Costanzo A, Farina L, Fiscon G, Gatto L, Gentili M, Loscalzo J, Marchese C, Napoli C, Paci P, Petti M, Quackenbush J, Tieri P, Viggiano D, Vilahur G, Glass K, Baumbach J. Molecular networks in Network Medicine: Development and applications. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2020; 12:e1489. [PMID: 32307915 DOI: 10.1002/wsbm.1489] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/29/2020] [Accepted: 03/20/2020] [Indexed: 12/14/2022]
Abstract
Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Translational, Genomic, and Systems Medicine > Translational Medicine Analytical and Computational Methods > Analytical Methods Analytical and Computational Methods > Computational Methods.
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Affiliation(s)
- Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Harald H H W Schmidt
- Department of Pharmacology and Personalized Medicine, School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
| | - Eleni Anastasiadou
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Marco Angelini
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Lina Badimon
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Jean-Luc Balligand
- Pole of Pharmacology and Therapeutics (FATH), Institute for Clinical and Experimental Research (IREC), UCLouvain, Brussels, Belgium
| | - Giuditta Benincasa
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Giovambattista Capasso
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", Naples, Italy.,BIOGEM, Ariano Irpino, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Antonella Di Costanzo
- Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Laurent Gatto
- de Duve Institute, Brussels, Belgium.,Institute for Experimental and Clinical Research (IREC), UCLouvain, Brussels, Belgium
| | - Michele Gentili
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Cinzia Marchese
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Claudio Napoli
- Department of Advanced Clinical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Manuela Petti
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - John Quackenbush
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paolo Tieri
- CNR National Research Council of Italy, IAC Institute for Applied Computing, Rome, Italy
| | - Davide Viggiano
- BIOGEM, Ariano Irpino, Italy.,Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Gemma Vilahur
- Cardiovascular Program-ICCC, IR-Hospital de la Santa Creu i Sant Pau, CiberCV, IIB-Sant Pau, Autonomous University of Barcelona, Barcelona, Spain
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jan Baumbach
- Department of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Maximus-von-Imhof-Forum 3, Freising, Germany.,Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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23
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Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells. Molecules 2020; 25:molecules25081883. [PMID: 32325755 PMCID: PMC7221756 DOI: 10.3390/molecules25081883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 04/10/2020] [Accepted: 04/14/2020] [Indexed: 12/28/2022] Open
Abstract
Drug target prediction is an important method for drug discovery and design, can disclose the potential inhibitory effect of active compounds, and is particularly relevant to many diseases that have the potential to kill, such as dengue, but lack any healing agent. An antiviral drug is urgently required for dengue treatment. Some potential antiviral agents are still in the process of drug discovery, but the development of more effective active molecules is in critical demand. Herein, we aimed to provide an efficient technique for target prediction using homopharma and network-based methods, which is reliable and expeditious to hunt for the possible human targets of three phenolic lipids (anarcardic acid, cardol, and cardanol) related to dengue viral (DENV) infection as a case study. Using several databases, the similarity search and network-based analyses were applied on the three phenolic lipids resulting in the identification of seven possible targets as follows. Based on protein annotation, three phenolic lipids may interrupt or disturb the human proteins, namely KAT5, GAPDH, ACTB, and HSP90AA1, whose biological functions have been previously reported to be involved with viruses in the family Flaviviridae. In addition, these phenolic lipids might inhibit the mechanism of the viral proteins: NS3, NS5, and E proteins. The DENV and human proteins obtained from this study could be potential targets for further molecular optimization on compounds with a phenolic lipid core structure in anti-dengue drug discovery. As such, this pipeline could be a valuable tool to identify possible targets of active compounds.
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Hallin J, Cisneros AF, Hénault M, Fijarczyk A, Dandage R, Bautista C, Landry CR. Similarities in biological processes can be used to bridge ecology and molecular biology. Evol Appl 2020; 13:1335-1350. [PMID: 32684962 PMCID: PMC7359829 DOI: 10.1111/eva.12961] [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: 10/28/2019] [Revised: 02/17/2020] [Accepted: 03/16/2020] [Indexed: 01/10/2023] Open
Abstract
Much of the research in biology aims to understand the origin of diversity. Naturally, ecological diversity was the first object of study, but we now have the necessary tools to probe diversity at molecular scales. The inherent differences in how we study diversity at different scales caused the disciplines of biology to be organized around these levels, from molecular biology to ecology. Here, we illustrate that there are key properties of each scale that emerge from the interactions of simpler components and that these properties are often shared across different levels of organization. This means that ideas from one level of organization can be an inspiration for novel hypotheses to study phenomena at another level. We illustrate this concept with examples of events at the molecular level that have analogs at the organismal or ecological level and vice versa. Through these examples, we illustrate that biological processes at different organization levels are governed by general rules. The study of the same phenomena at different scales could enrich our work through a multidisciplinary approach, which should be a staple in the training of future scientists.
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Affiliation(s)
- Johan Hallin
- Département de biochimie de microbiologie et de bio-informatique Faculté des sciences et de génie Université Laval Québec Canada.,Département de biologie Faculté des sciences et de génie Université Laval Québec Canada.,Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec Canada.,PROTEO Le réseau québécois de recherche sur la fonction la structure et l'ingénierie des protéines Université Laval Québec Canada.,Centre de Recherche en Données Massives (CRDM) Université Laval Québec Canada
| | - Angel F Cisneros
- Département de biochimie de microbiologie et de bio-informatique Faculté des sciences et de génie Université Laval Québec Canada.,Département de biologie Faculté des sciences et de génie Université Laval Québec Canada.,Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec Canada.,PROTEO Le réseau québécois de recherche sur la fonction la structure et l'ingénierie des protéines Université Laval Québec Canada.,Centre de Recherche en Données Massives (CRDM) Université Laval Québec Canada
| | - Mathieu Hénault
- Département de biochimie de microbiologie et de bio-informatique Faculté des sciences et de génie Université Laval Québec Canada.,Département de biologie Faculté des sciences et de génie Université Laval Québec Canada.,Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec Canada.,PROTEO Le réseau québécois de recherche sur la fonction la structure et l'ingénierie des protéines Université Laval Québec Canada.,Centre de Recherche en Données Massives (CRDM) Université Laval Québec Canada
| | - Anna Fijarczyk
- Département de biochimie de microbiologie et de bio-informatique Faculté des sciences et de génie Université Laval Québec Canada.,Département de biologie Faculté des sciences et de génie Université Laval Québec Canada.,Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec Canada.,PROTEO Le réseau québécois de recherche sur la fonction la structure et l'ingénierie des protéines Université Laval Québec Canada.,Centre de Recherche en Données Massives (CRDM) Université Laval Québec Canada
| | - Rohan Dandage
- Département de biochimie de microbiologie et de bio-informatique Faculté des sciences et de génie Université Laval Québec Canada.,Département de biologie Faculté des sciences et de génie Université Laval Québec Canada.,Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec Canada.,PROTEO Le réseau québécois de recherche sur la fonction la structure et l'ingénierie des protéines Université Laval Québec Canada.,Centre de Recherche en Données Massives (CRDM) Université Laval Québec Canada
| | - Carla Bautista
- Département de biochimie de microbiologie et de bio-informatique Faculté des sciences et de génie Université Laval Québec Canada.,Département de biologie Faculté des sciences et de génie Université Laval Québec Canada.,Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec Canada.,PROTEO Le réseau québécois de recherche sur la fonction la structure et l'ingénierie des protéines Université Laval Québec Canada.,Centre de Recherche en Données Massives (CRDM) Université Laval Québec Canada
| | - Christian R Landry
- Département de biochimie de microbiologie et de bio-informatique Faculté des sciences et de génie Université Laval Québec Canada.,Département de biologie Faculté des sciences et de génie Université Laval Québec Canada.,Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec Canada.,PROTEO Le réseau québécois de recherche sur la fonction la structure et l'ingénierie des protéines Université Laval Québec Canada.,Centre de Recherche en Données Massives (CRDM) Université Laval Québec Canada
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25
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Vibala B, Praseetha P, Vijayakumar S. Evaluating new strategies for anticancer molecules from ethnic medicinal plants through in silico and biological approach - A review. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2019.100553] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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26
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Ye W, Ji G, Ye P, Long Y, Xiao X, Li S, Su Y, Wu X. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data. BMC Genomics 2019; 20:347. [PMID: 31068142 PMCID: PMC6505295 DOI: 10.1186/s12864-019-5747-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 04/29/2019] [Indexed: 12/15/2022] Open
Abstract
Background Single-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data. Results We presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools. Conclusions scNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization. Electronic supplementary material The online version of this article (10.1186/s12864-019-5747-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wenbin Ye
- Department of Automation, Xiamen University, Xiamen, 361005, China.,Xiamen Research Institute of National Center of Healthcare Big Data, Xiamen, China
| | - Guoli Ji
- Department of Automation, Xiamen University, Xiamen, 361005, China.,Xiamen Research Institute of National Center of Healthcare Big Data, Xiamen, China.,Innovation Center for Cell Biology, Xiamen University, Xiamen, 361005, China
| | - Pengchao Ye
- Department of Automation, Xiamen University, Xiamen, 361005, China.,Xiamen Research Institute of National Center of Healthcare Big Data, Xiamen, China
| | - Yuqi Long
- Software Quality Testing Engineering Research Center, China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, 510610, China
| | - Xuesong Xiao
- Department of Automation, Xiamen University, Xiamen, 361005, China.,Xiamen Research Institute of National Center of Healthcare Big Data, Xiamen, China
| | - Shuchao Li
- Department of Automation, Xiamen University, Xiamen, 361005, China.,Xiamen Research Institute of National Center of Healthcare Big Data, Xiamen, China
| | - Yaru Su
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
| | - Xiaohui Wu
- Department of Automation, Xiamen University, Xiamen, 361005, China. .,Xiamen Research Institute of National Center of Healthcare Big Data, Xiamen, China. .,Innovation Center for Cell Biology, Xiamen University, Xiamen, 361005, China.
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27
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Fast Subgraph Matching Strategies Based on Pattern-Only Heuristics. Interdiscip Sci 2019; 11:21-32. [PMID: 30790228 DOI: 10.1007/s12539-019-00323-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 01/28/2019] [Accepted: 02/02/2019] [Indexed: 12/22/2022]
Abstract
Many scientific applications entail solving the subgraph isomorphism problem, i.e., given an input pattern graph, find all the subgraphs of a (usually much larger) target graph that are structurally equivalent to that input. Because subgraph isomorphism is NP-complete, methods to solve it have to use heuristics. This work evaluates subgraph isomorphism methods to assess their computational behavior on a wide range of synthetic and real graphs. Surprisingly, our experiments show that, among the leading algorithms, certain heuristics based only on pattern graphs are the most efficient.
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28
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Integrating Multiple Interaction Networks for Gene Function Inference. Molecules 2018; 24:molecules24010030. [PMID: 30577643 PMCID: PMC6337127 DOI: 10.3390/molecules24010030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 12/19/2018] [Accepted: 12/20/2018] [Indexed: 01/17/2023] Open
Abstract
In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.
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29
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Barbosa S, Niebel B, Wolf S, Mauch K, Takors R. A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints. Biosystems 2018; 174:37-48. [PMID: 30312740 DOI: 10.1016/j.biosystems.2018.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 10/05/2018] [Accepted: 10/08/2018] [Indexed: 02/07/2023]
Abstract
The study of biological systems at a system level has become a reality due to the increasing powerful computational approaches able to handle increasingly larger datasets. Uncovering the dynamic nature of gene regulatory networks in order to attain a system level understanding and improve the predictive power of biological models is an important research field in systems biology. The task itself presents several challenges, since the problem is of combinatorial nature and highly depends on several biological constraints and also the intended application. Given the intrinsic interdisciplinary nature of gene regulatory network inference, we present a review on the currently available approaches, their challenges and limitations. We propose guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.
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Affiliation(s)
- Sara Barbosa
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany.
| | - Bastian Niebel
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Sebastian Wolf
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Klaus Mauch
- Insilico Biotechnology AG, Meitnerstrasse 9, 70563 Stuttgart, Germany
| | - Ralf Takors
- Institute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany
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30
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Chung M, Cho SY, Lee YS. Construction of a Transcriptome-Driven Network at the Early Stage of Infection with Influenza A H1N1 in Human Lung Alveolar Epithelial Cells. Biomol Ther (Seoul) 2018; 26:290-297. [PMID: 29401570 PMCID: PMC5933896 DOI: 10.4062/biomolther.2017.240] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 12/29/2017] [Accepted: 01/02/2018] [Indexed: 12/30/2022] Open
Abstract
We aimed to understand the molecular changes in host cells that accompany infection by the seasonal influenza A H1N1 virus because the initial response rapidly changes owing to the fact that the virus has a robust initial propagation phase. Human epithelial alveolar A549 cells were infected and total RNA was extracted at 30 min, 1 h, 2 h, 4 h, 8 h, 24 h, and 48 h post infection (h.p.i.). The differentially expressed host genes were clustered into two distinct sets of genes as the infection progressed over time. The patterns of expression were significantly different at the early stages of infection. One of the responses showed roles similar to those associated with the enrichment gene sets to known 'gp120 pathway in HIV.' This gene set contains genes known to play roles in preventing the progress of apoptosis, which infected cells undergo as a response to viral infection. The other gene set showed enrichment of 'Drug Metabolism Enzymes (DMEs).' The identification of two distinct gene sets indicates that the virus regulates the cell's mechanisms to create a favorable environment for its stable replication and protection of gene metabolites within 8 h.
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Affiliation(s)
- Myungguen Chung
- Division of Molecular and Life Sciences, Hanyang University, Ansan 15588, Republic of Korea
| | - Soo Young Cho
- National Cancer Center, Goyang 10408, Republic of Korea
| | - Young Seek Lee
- Division of Molecular and Life Sciences, Hanyang University, Ansan 15588, Republic of Korea
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31
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Pavlopoulos GA, Kontou PI, Pavlopoulou A, Bouyioukos C, Markou E, Bagos PG. Bipartite graphs in systems biology and medicine: a survey of methods and applications. Gigascience 2018; 7:1-31. [PMID: 29648623 PMCID: PMC6333914 DOI: 10.1093/gigascience/giy014] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2017] [Revised: 01/15/2018] [Accepted: 02/13/2018] [Indexed: 11/14/2022] Open
Abstract
The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.
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Affiliation(s)
- Georgios A Pavlopoulos
- Lawrence Berkeley Labs, DOE Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA
| | - Panagiota I Kontou
- University of Thessaly, Department of Computer Science and Biomedical Informatics, Papasiopoulou 2–4, Lamia, 35100, Greece
| | - Athanasia Pavlopoulou
- Izmir International Biomedicine and Genome Institute (iBG-Izmir), Dokuz Eylül University, 35340, Turkey
| | - Costas Bouyioukos
- Université Paris Diderot, Sorbonne Paris Cité, Epigenetics and Cell Fate, UMR7216, CNRS, France
| | - Evripides Markou
- University of Thessaly, Department of Computer Science and Biomedical Informatics, Papasiopoulou 2–4, Lamia, 35100, Greece
| | - Pantelis G Bagos
- University of Thessaly, Department of Computer Science and Biomedical Informatics, Papasiopoulou 2–4, Lamia, 35100, Greece
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32
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Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 2018; 6:484-495.e5. [PMID: 29605183 DOI: 10.1016/j.cels.2018.03.001] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 12/19/2017] [Accepted: 02/28/2018] [Indexed: 12/27/2022]
Abstract
Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.
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33
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Burke PEP, Comin CH, Silva FN, Costa LDF. Biological network border detection. Integr Biol (Camb) 2017; 9:947-955. [PMID: 29138780 DOI: 10.1039/c7ib00161d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Complex networks have been widely used to model biological systems. The concept of accessibility has been proposed recently as a means to organize the nodes of complex networks as belonging to its border or center. Such an approach paves the way to investigating how the functional and structural properties of nodes vary with their respective position in the networks. In this work, we approach such a problem in a biological context applying border detection to Protein-Protein Interaction networks from four organisms of the Mycoplasma genus. We found evidence that the borderness of proteins bears a relation with their spatial organization and molecular function specificity.
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Affiliation(s)
- Paulo E P Burke
- University of São Paulo - Bioinformatics Graduate Program, São Carlos, SP, Brazil.
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34
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NFP: An R Package for Characterizing and Comparing of Annotated Biological Networks. BIOMED RESEARCH INTERNATIONAL 2017; 2017:7457131. [PMID: 28280740 PMCID: PMC5322572 DOI: 10.1155/2017/7457131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 11/27/2016] [Indexed: 11/17/2022]
Abstract
Large amounts of various biological networks exist for representing different types of interaction data, such as genetic, metabolic, gene regulatory, and protein-protein relationships. Recent approaches on biological network study are based on different mathematical concepts. It is necessary to construct a uniform framework to judge the functionality of biological networks. We recently introduced a knowledge-based computational framework that reliably characterized biological networks in system level. The method worked by making systematic comparisons to a set of well-studied "basic networks," measuring both the functional and topological similarities. A biological network could be characterized as a spectrum-like vector consisting of similarities to basic networks. Here, to facilitate the application, development, and adoption of this framework, we present an R package called NFP. This package extends our previous pipeline, offering a powerful set of functions for Network Fingerprint analysis. The software shows great potential in biological network study. The open source NFP R package is freely available under the GNU General Public License v2.0 at CRAN along with the vignette.
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35
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Gladilin E. Graph-theoretical model of global human interactome reveals enhanced long-range communicability in cancer networks. PLoS One 2017; 12:e0170953. [PMID: 28141819 PMCID: PMC5283687 DOI: 10.1371/journal.pone.0170953] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 01/13/2017] [Indexed: 12/22/2022] Open
Abstract
Malignant transformation is known to involve substantial rearrangement of the molecular genetic landscape of the cell. A common approach to analysis of these alterations is a reductionist one and consists of finding a compact set of differentially expressed genes or associated signaling pathways. However, due to intrinsic tumor heterogeneity and tissue specificity, biomarkers defined by a small number of genes/pathways exhibit substantial variability. As an alternative to compact differential signatures, global features of genetic cell machinery are conceivable. Global network descriptors suggested in previous works are, however, known to potentially be biased by overrepresentation of interactions between frequently studied genes-proteins. Here, we construct a cellular network of 74538 directional and differential gene expression weighted protein-protein and gene regulatory interactions, and perform graph-theoretical analysis of global human interactome using a novel, degree-independent feature—the normalized total communicability (NTC). We apply this framework to assess differences in total information flow between different cancer (BRCA/COAD/GBM) and non-cancer interactomes. Our experimental results reveal that different cancer interactomes are characterized by significant enhancement of long-range NTC, which arises from circulation of information flow within robustly organized gene subnetworks. Although enhancement of NTC emerges in different cancer types from different genomic profiles, we identified a subset of 90 common genes that are related to elevated NTC in all studied tumors. Our ontological analysis shows that these genes are associated with enhanced cell division, DNA replication, stress response, and other cellular functions and processes typically upregulated in cancer. We conclude that enhancement of long-range NTC manifested in the correlated activity of genes whose tight coordination is required for survival and proliferation of all tumor cells, and, thus, can be seen as a graph-theoretical equivalent to some hallmarks of cancer. The computational framework for differential network analysis presented herein is of potential interest for a wide range of network perturbation problems given by single or multiple gene-protein activation-inhibition.
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Affiliation(s)
- Evgeny Gladilin
- Division of Theoretical Bioinformatics, German Cancer Research Center, Berliner Str. 41, 69120 Heidelberg, Germany
- BioQuant and IPMB, University Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
- * E-mail:
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36
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Seasonal Dynamics and Metagenomic Characterization of Marine Viruses in Goseong Bay, Korea. PLoS One 2017; 12:e0169841. [PMID: 28122030 PMCID: PMC5266330 DOI: 10.1371/journal.pone.0169841] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 12/21/2016] [Indexed: 12/01/2022] Open
Abstract
Viruses are the most abundant biological entities in the oceans, and account for a significant amount of the genetic diversity of marine ecosystems. However, there is little detailed information about the biodiversity of viruses in marine environments. Rapid advances in metagenomics have enabled the identification of previously unknown marine viruses. We performed metagenomic profiling of seawater samples collected at 6 sites in Goseong Bay (South Sea, Korea) during the spring, summer, autumn, and winter of 2014. The results indicated the presence of highly diverse virus communities. The DNA libraries from samples collected during four seasons were sequenced using Illumina HiSeq 2000. The number of viral reads was 136,850 during March, 70,651 during June, 66,165 during September, and 111,778 during December. Species identification indicated that Pelagibacter phage HTVC010P, Ostreococcus lucimarinus OIV5 and OIV1, and Roseobacter phage SIO1 were the most common species in all samples. For viruses with at least 10 reads, there were 204 species during March, 189 during June, 170 during September, and 173 during December. Analysis of virus families indicated that the Myoviridae was the most common during all four seasons, and viruses in the Polyomaviridae were only present during March. Viruses in the Iridoviridae were only present during three seasons. Additionally, viruses in the Iridoviridae, Herpesviridae, and Poxviridae, which may affect fish and marine animals, appeared during different seasons. These results suggest that seasonal changes in temperature contribute to the dynamic structure of the viral community in the study area. The information presented here will be useful for comparative analyses with other marine viral communities.
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Abstract
Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches.
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Affiliation(s)
- Domenico Cozzetto
- Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - David T Jones
- Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.
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38
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Bonnici V, Giugno R. On the Variable Ordering in Subgraph Isomorphism Algorithms. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:193-203. [PMID: 26761859 DOI: 10.1109/tcbb.2016.2515595] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Graphs are mathematical structures to model several biological data. Applications to analyze them require to apply solutions for the subgraph isomorphism problem, which is NP-complete. Here, we investigate the existing strategies to reduce the subgraph isomorphism algorithm running time with emphasis on the importance of the order with which the graph vertices are taken into account during the search, called variable ordering, and its incidence on the total running time of the algorithms. We focus on two recent solutions, which are based on an effective variable ordering strategy. We discuss their comparison both with the variable ordering strategies reviewed in the paper and the other algorithms present in the ICPR2014 contest on graph matching algorithms for pattern search in biological databases.
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39
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Cho H, Berger B, Peng J. Compact Integration of Multi-Network Topology for Functional Analysis of Genes. Cell Syst 2016; 3:540-548.e5. [PMID: 27889536 DOI: 10.1016/j.cels.2016.10.017] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2016] [Revised: 08/14/2016] [Accepted: 10/19/2016] [Indexed: 01/18/2023]
Abstract
The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the structure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains.
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Affiliation(s)
- Hyunghoon Cho
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA; Department of Mathematics, MIT, Cambridge, MA 02139, USA.
| | - Jian Peng
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA; Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA.
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Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism. BIOMED RESEARCH INTERNATIONAL 2016; 2016:8313272. [PMID: 27840831 PMCID: PMC5090126 DOI: 10.1155/2016/8313272] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 08/03/2016] [Accepted: 08/18/2016] [Indexed: 02/02/2023]
Abstract
In the last decade vast data sets are being generated in biological and medical studies. The challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specific knowledge that often lays beyond classical biology and includes many computational tools and software. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available software. The pipeline allows the user to control each step of the network generation and provides flexibility in selection of correlation methods and thresholds. The pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. The analysis revealed significant differences between the metabolic networks in response to the tested conditions. The network under hypoxia had 1.7 times more significant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identified which could lead to the identification of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth.
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Kori M, Gov E, Arga KY. Molecular signatures of ovarian diseases: Insights from network medicine perspective. Syst Biol Reprod Med 2016; 62:266-82. [PMID: 27341345 DOI: 10.1080/19396368.2016.1197982] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Dysfunctions and disorders in the ovary lead to a host of diseases including ovarian cancer, ovarian endometriosis, and polycystic ovarian syndrome (PCOS). Understanding the molecular mechanisms behind ovarian diseases is a great challenge. In the present study, we performed a meta-analysis of transcriptome data for ovarian cancer, ovarian endometriosis, and PCOS, and integrated the information gained from statistical analysis with genome-scale biological networks (protein-protein interaction, transcriptional regulatory, and metabolic). Comparative and integrative analyses yielded reporter biomolecules (genes, proteins, metabolites, transcription factors, and micro-RNAs), and unique or common signatures at protein, metabolism, and transcription regulation levels, which might be beneficial to uncovering the underlying biological mechanisms behind the diseases. These signatures were mostly associated with formation or initiation of cancer development, and pointed out the potential tendency of PCOS and endometriosis to tumorigenesis. Molecules and pathways related to MAPK signaling, cell cycle, and apoptosis were the mutual determinants in the pathogenesis of all three diseases. To our knowledge, this is the first report that screens these diseases from a network medicine perspective. This study provides signatures which could be considered as potential therapeutic targets and/or as medical prognostic biomarkers in further experimental and clinical studies. Abbreviations DAVID: Database for Annotation, Visualization and Integrated Discovery; DEGs: differentially expressed genes; GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; LIMMA: Linear Models for Microarray Data; MBRole: Metabolite Biological Role; miRNA: micro-RNA; PCOS: polycystic ovarian syndrome; PPI: protein-protein interaction; RMA: Robust Multi-Array Average; TF: transcription factor.
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Affiliation(s)
- Medi Kori
- a Department of Bioengineering , Marmara University , Istanbul , Turkey
| | - Esra Gov
- a Department of Bioengineering , Marmara University , Istanbul , Turkey
| | - Kazim Yalcin Arga
- a Department of Bioengineering , Marmara University , Istanbul , Turkey
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Bonnici V, Busato F, Micale G, Bombieri N, Pulvirenti A, Giugno R. APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks. Bioinformatics 2016; 32:2159-66. [DOI: 10.1093/bioinformatics/btw223] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 04/10/2016] [Indexed: 02/02/2023] Open
Affiliation(s)
- Vincenzo Bonnici
- Department of Computer Science, University of Verona, Strada Le Grazie, Verona
| | - Federico Busato
- Department of Computer Science, University of Verona, Strada Le Grazie, Verona
| | - Giovanni Micale
- Department of Math and Computer Science, University of Catania, Viale a. Doria, Catania
| | - Nicola Bombieri
- Department of Computer Science, University of Verona, Strada Le Grazie, Verona
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, via Palermo, Catania
| | - Rosalba Giugno
- Department of Computer Science, University of Verona, Strada Le Grazie, Verona
- Department of Clinical and Experimental Medicine, University of Catania, via Palermo, Catania
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Politano G, Orso F, Raimo M, Benso A, Savino A, Taverna D, Di Carlo S. CyTRANSFINDER: a Cytoscape 3.3 plugin for three-component (TF, gene, miRNA) signal transduction pathway construction. BMC Bioinformatics 2016; 17:157. [PMID: 27059647 PMCID: PMC4826505 DOI: 10.1186/s12859-016-0964-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 02/19/2016] [Indexed: 12/02/2022] Open
Abstract
Background Biological research increasingly relies on network models to study complex phenomena. Signal Transduction Pathways are molecular circuits that model how cells receive, process, and respond to information from the environment providing snapshots of the overall cell dynamics. Most of the attempts to reconstruct signal transduction pathways are limited to single regulator networks including only genes/proteins. However, networks involving a single type of regulator and neglecting transcriptional and post-transcriptional regulations mediated by transcription factors and microRNAs, respectively, may not fully reveal the complex regulatory mechanisms of a cell. We observed a lack of computational instruments supporting explorative analysis on this type of three-component signal transduction pathways. Results We have developed CyTRANSFINDER, a new Cytoscape plugin able to infer three-component signal transduction pathways based on user defined regulatory patterns and including miRNAs, TFs and genes. Since CyTRANSFINDER has been designed to support exploratory analysis, it does not rely on expression data. To show the potential of the plugin we have applied it in a study of two miRNAs that are particularly relevant in human melanoma progression, miR-146a and miR-214. Conclusions CyTRANSFINDER supports the reconstruction of small signal transduction pathways among groups of genes. Results obtained from its use in a real case study have been analyzed and validated through both literature data and preliminary wet-lab experiments, showing the potential of this tool when performing exploratory analysis. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0964-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gianfranco Politano
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy
| | - Francesca Orso
- Molecular Biotechnology Center (MBC), Via Nizza, 52, Torino, 10126, Italy.,Dept. Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza, 52, Torino, 10126, Italy.,Center for Complex Systems in Molecular Biology and Medicine, Via Accademia Albertina, 13, Torino, 10123, Italy
| | - Monica Raimo
- Molecular Biotechnology Center (MBC), Via Nizza, 52, Torino, 10126, Italy.,Dept. Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza, 52, Torino, 10126, Italy
| | - Alfredo Benso
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy
| | - Alessandro Savino
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy
| | - Daniela Taverna
- Molecular Biotechnology Center (MBC), Via Nizza, 52, Torino, 10126, Italy.,Dept. Molecular Biotechnology and Health Sciences, University of Torino, Via Nizza, 52, Torino, 10126, Italy.,Center for Complex Systems in Molecular Biology and Medicine, Via Accademia Albertina, 13, Torino, 10123, Italy
| | - Stefano Di Carlo
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy.
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Tumor suppressor genes and their underlying interactions in paclitaxel resistance in cancer therapy. Cancer Cell Int 2016; 16:13. [PMID: 26900348 PMCID: PMC4761208 DOI: 10.1186/s12935-016-0290-9] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 02/12/2016] [Indexed: 01/01/2023] Open
Abstract
Objectives Paclitaxel (PTX) is frequently used in the clinical treatment of solid tumors. But the PTX-resistance is a great obstacle in cancer treatment. Exploration of the mechanisms of drug resistance suggests that tumor suppressor genes (TSGs) play a key role in the response of chemotherapeutic drugs. TSGs, a set of genes that are often inactivated in cancers, can regulate various biological processes. In this study, an overview of the contribution of TSGs to PTX resistance and their underlying relationship in cancers are reported by using GeneMANIA, a web-based tool for gene/protein function prediction. Methods Using PubMed online database and Google web site, the terms “paclitaxel resistance” or “taxol resistance” or “drug resistance” or “chemotherapy resistance”, and “cancer” or “carcinoma”, and “tumor suppressor genes” or “TSGs” or “negative regulated protein” or “antioncogenes” were searched and analyzed. GeneMANIA data base was used to predict gene/protein interactions and functions. Results We identified 22 TSGs involved in PTX resistance, including BRCA1, TP53, PTEN, APC, CDKN1A, CDKN2A, HIN-1, RASSF1, YAP, ING4, PLK2, FBW7, BLU, LZTS1, REST, FADD, PDCD4, TGFBI, ING1, Bax, PinX1 and hEx. The TSGs were found to have direct and indirect relationships with each other, and thus they could contribute to PTX resistance as a group. The varied expression status and regulation function of the TSGs on cell cycle in different cancers might play an important role in PTX resistance. Conclusion A further understanding of the roles of tumor suppressor genes in drug resistance is an important step to overcome chemotherapy tolerance. Tumor suppressor gene therapy targets the altered genes and signaling pathways and can be a new strategy to reverse chemotherapy resistance.
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Jafari M, Mirzaie M, Sadeghi M. Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms. BMC Bioinformatics 2015; 16:319. [PMID: 26437714 PMCID: PMC4595048 DOI: 10.1186/s12859-015-0755-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 09/29/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary. METHODS Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs). RESULTS The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome. CONCLUSION In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.
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Affiliation(s)
- Mohieddin Jafari
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, 69 Pasteur St, PO Box 13164, Tehran, Iran.
- School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Shahid Lavasani St, PO Box 19395-5746, Tehran, Iran.
| | - Mehdi Mirzaie
- Department of Computational Biology, Faculty of High Technologies, Tarbiat Modares University, Jalal Ale Ahmad Highway, PO Box 14115-111, Tehran, Iran.
| | - Mehdi Sadeghi
- National Institute of Genetic Engineering and Biotechnology (NIGEB), Pajoohesh Blvd, 17 Km Tehran-Karaj Highway, PO Box 161-14965, Tehran, Iran.
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Deciphering complex patterns of class-I HLA-peptide cross-reactivity via hierarchical grouping. Immunol Cell Biol 2015; 93:522-32. [PMID: 25708537 DOI: 10.1038/icb.2015.3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Revised: 12/20/2014] [Accepted: 12/22/2014] [Indexed: 11/08/2022]
Abstract
T-cell responses in humans are initiated by the binding of a peptide antigen to a human leukocyte antigen (HLA) molecule. The peptide-HLA complex then recruits an appropriate T cell, leading to cell-mediated immunity. More than 2000 HLA class-I alleles are known in humans, and they vary only in their peptide-binding grooves. The polymorphism they exhibit enables them to bind a wide range of peptide antigens from diverse sources. HLA molecules and peptides present a complex molecular recognition pattern, as many peptides bind to a given allele and a given peptide can be recognized by many alleles. A powerful grouping scheme that not only provides an insightful classification, but is also capable of dissecting the physicochemical basis of recognition specificity is necessary to address this complexity. We present a hierarchical classification of 2010 class-I alleles by using a systematic divisive clustering method. All-pair distances of alleles were obtained by comparing binding pockets in the structural models. By varying the similarity thresholds, a multilevel classification was obtained, with 7 supergroups, each further subclassifying to yield 72 groups. An independent clustering performed based only on similarities in their epitope pools correlated highly with pocket-based clustering. Physicochemical feature combinations that best explain the basis of clustering are identified. Mutual information calculated for the set of peptide ligands enables identification of binding site residues contributing to peptide specificity. The grouping of HLA molecules achieved here will be useful for rational vaccine design, understanding disease susceptibilities and predicting risk of organ transplants.
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Chowdhury A, Zomorrodi AR, Maranas CD. Bilevel optimization techniques in computational strain design. Comput Chem Eng 2015. [DOI: 10.1016/j.compchemeng.2014.06.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Caberlotto L, Lauria M. Systems biology meets -omic technologies: novel approaches to biomarker discovery and companion diagnostic development. Expert Rev Mol Diagn 2014; 15:255-65. [DOI: 10.1586/14737159.2015.975214] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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A genetically modified protein-based hydrogel for 3D culture of AD293 cells. PLoS One 2014; 9:e107949. [PMID: 25233088 PMCID: PMC4169439 DOI: 10.1371/journal.pone.0107949] [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] [Received: 07/27/2014] [Accepted: 08/18/2014] [Indexed: 11/19/2022] Open
Abstract
Hydrogels have strong application prospects for drug delivery, tissue engineering and cell therapy because of their excellent biocompatibility and abundant availability as scaffolds for drugs and cells. In this study, we created hybrid hydrogels based on a genetically modified tax interactive protein-1 (TIP1) by introducing two or four cysteine residues in the primary structure of TIP1. The introduced cysteine residues were crosslinked with a four-armed poly (ethylene glycol) having their arm ends capped with maleimide residues (4-armed-PEG-Mal) to form hydrogels. In one form of the genetically modification, we incorporated a peptide sequence ‘GRGDSP’ to introduce bioactivity to the protein, and the resultant hydrogel could provide an excellent environment for a three dimensional cell culture of AD293 cells. The AD293 cells continued to divide and displayed a polyhedron or spindle-shape during the 3-day culture period. Besides, AD293 cells could be easily separated from the cell-gel constructs for future large-scale culture after being cultured for 3 days and treating hydrogel with trypsinase. This work significantly expands the toolbox of recombinant proteins for hydrogel formation, and we believe that our hydrogel will be of considerable interest to those working in cell therapy and controlled drug delivery.
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Hernández-Prieto MA, Semeniuk TA, Futschik ME. Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria. Front Genet 2014; 5:191. [PMID: 25071821 PMCID: PMC4079066 DOI: 10.3389/fgene.2014.00191] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Accepted: 06/11/2014] [Indexed: 12/21/2022] Open
Abstract
Cyanobacteria are essential primary producers in marine ecosystems, playing an important role in both carbon and nitrogen cycles. In the last decade, various genome sequencing and metagenomic projects have generated large amounts of genetic data for cyanobacteria. This wealth of data provides researchers with a new basis for the study of molecular adaptation, ecology and evolution of cyanobacteria, as well as for developing biotechnological applications. It also facilitates the use of multiplex techniques, i.e., expression profiling by high-throughput technologies such as microarrays, RNA-seq, and proteomics. However, exploration and analysis of these data is challenging, and often requires advanced computational methods. Also, they need to be integrated into our existing framework of knowledge to use them to draw reliable biological conclusions. Here, systems biology provides important tools. Especially, the construction and analysis of molecular networks has emerged as a powerful systems-level framework, with which to integrate such data, and to better understand biological relevant processes in these organisms. In this review, we provide an overview of the advances and experimental approaches undertaken using multiplex data from genomic, transcriptomic, proteomic, and metabolomic studies in cyanobacteria. Furthermore, we summarize currently available web-based tools dedicated to cyanobacteria, i.e., CyanoBase, CyanoEXpress, ProPortal, Cyanorak, CyanoBIKE, and CINPER. Finally, we present a case study for the freshwater model cyanobacteria, Synechocystis sp. PCC6803, to show the power of meta-analysis, and the potential to extrapolate acquired knowledge to the ecologically important marine cyanobacteria genus, Prochlorococcus.
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Affiliation(s)
| | - Trudi A Semeniuk
- Systems Biology and Bioinformatics Laboratory, IBB-CBME, University of Algarve Faro, Portugal
| | - Matthias E Futschik
- Systems Biology and Bioinformatics Laboratory, IBB-CBME, University of Algarve Faro, Portugal ; Centre of Marine Sciences, University of Algarve Faro, Portugal
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