1
|
Milano M, Agapito G, Cannataro M. Challenges and Limitations of Biological Network Analysis. BIOTECH 2022; 11:24. [PMID: 35892929 PMCID: PMC9326688 DOI: 10.3390/biotech11030024] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022] Open
Abstract
High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms' properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein-Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein-protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.
Collapse
Affiliation(s)
- Marianna Milano
- Department of Medical and Clinical Surgery, University Magna Græcia, 88100 Catanzaro, Italy; (M.M.); (M.C.)
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
| | - Giuseppe Agapito
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Law, Economics and Social Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Department of Medical and Clinical Surgery, University Magna Græcia, 88100 Catanzaro, Italy; (M.M.); (M.C.)
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
| |
Collapse
|
2
|
Pastrello C, Abovsky M, Lu R, Ahmed Z, Kotlyar M, Veillette C, Jurisica I. Osteoarthritis Data Integration Portal (OsteoDIP): A web-based gene and non-coding RNA expression database. OSTEOARTHRITIS AND CARTILAGE OPEN 2022; 4:100237. [DOI: 10.1016/j.ocarto.2022.100237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 01/19/2022] [Indexed: 10/19/2022] Open
|
3
|
Pinheiro M, Lupinacci FCS, Santiago KM, Drigo SA, Marchi FA, Fonseca-Alves CE, Andrade SCDS, Aagaard MM, Basso TR, dos Reis MB, Villacis RAR, Roffé M, Hajj GNM, Jurisica I, Kowalski LP, Achatz MI, Rogatto SR. Germline Mutation in MUS81 Resulting in Impaired Protein Stability is Associated with Familial Breast and Thyroid Cancer. Cancers (Basel) 2020; 12:cancers12051289. [PMID: 32443704 PMCID: PMC7281423 DOI: 10.3390/cancers12051289] [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: 03/25/2020] [Revised: 05/07/2020] [Accepted: 05/12/2020] [Indexed: 01/10/2023] Open
Abstract
Multiple primary thyroid cancer (TC) and breast cancer (BC) are commonly diagnosed, and the lifetime risk for these cancers is increased in patients with a positive family history of both TC and BC. Although this phenotype is partially explained by TP53 or PTEN mutations, a significant number of patients are negative for these alterations. We judiciously recruited patients diagnosed with BC and/or TC having a family history of these tumors and assessed their whole-exome sequencing. After variant prioritization, we selected MUS81 c.1292G>A (p.R431H) for further investigation. This variant was genotyped in a healthy population and sporadic BC/TC tissues and investigated at the protein level and cellular models. MUS81 c.1292G>A was the most frequent variant (25%) and the strongest candidate due to its function of double-strand break repair. This variant was confirmed in four relatives from two families. MUS81 p.R431H protein exhibited lower expression levels in tumors from patients positive for the germline variant, compared with wild-type BC, and normal breast and thyroid tissues. Using cell line models, we showed that c.1292G>A induced protein instability and affected DNA damage response. We suggest that MUS81 is a novel candidate involved in familial BC/TC based on its low frequency in healthy individuals and proven effect in protein stability.
Collapse
Affiliation(s)
- Maisa Pinheiro
- Faculty of Medicine, Sao Paulo State University, UNESP, Botucatu SP 18618-687, Brazil;
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Fernanda Cristina Sulla Lupinacci
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Karina Miranda Santiago
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Sandra Aparecida Drigo
- Department of Surgery and Orthopedics, Experimental Research Unity, Faculty of Medicine, São Paulo State University, UNESP, Botucatu SP 18618-687, Brazil;
| | - Fabio Albuquerque Marchi
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Carlos Eduardo Fonseca-Alves
- Department of Veterinary Surgery and Anesthesiology, São Paulo State University, UNESP, Botucatu SP 18618-681, Brazil;
| | | | - Mads Malik Aagaard
- Department of Clinical Genetics, Vejle University Hospital, 7100 Vejle, Denmark;
| | - Tatiane Ramos Basso
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Mariana Bisarro dos Reis
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Rolando André Rios Villacis
- Department of Genetics and Morphology, Institute of Biological Sciences, University of Brasília, UnB, Brasília DF 70910-900, Brazil;
| | - Martin Roffé
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Glaucia Noeli Maroso Hajj
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Igor Jurisica
- Krembil Research Institute, UHN, University of Toronto, Toronto, ON M5G 2C4, Canada;
- Institute of Neuroimmunology, Slovak Academy of Sciences, 845 10 Bratislava, Slovakia
| | - Luiz Paulo Kowalski
- International Research Center, A.C. Camargo Cancer Center, São Paulo SP 01508-010, Brazil; (F.C.S.L.); (K.M.S.); (F.A.M.); (T.R.B.); (M.B.d.R.); (M.R.); (G.N.M.H.); (L.P.K.)
| | - Maria Isabel Achatz
- Cancer Genetics Unit, Centro de Oncologia, Hospital Sirio Libanês, São Paulo SP 01308-050, Brazil;
| | - Silvia Regina Rogatto
- Department of Clinical Genetics, Vejle University Hospital, Institute of Regional Health Research, University of Southern Denmark, 5000 Odense, Denmark
- Correspondence:
| |
Collapse
|
4
|
Biological Network Approaches and Applications in Rare Disease Studies. Genes (Basel) 2019; 10:genes10100797. [PMID: 31614842 PMCID: PMC6827097 DOI: 10.3390/genes10100797] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 09/30/2019] [Accepted: 10/10/2019] [Indexed: 12/26/2022] Open
Abstract
Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases.
Collapse
|
5
|
Zhang P, Tao L, Zeng X, Qin C, Chen S, Zhu F, Li Z, Jiang Y, Chen W, Chen YZ. A protein network descriptor server and its use in studying protein, disease, metabolic and drug targeted networks. Brief Bioinform 2017; 18:1057-1070. [PMID: 27542402 PMCID: PMC5862332 DOI: 10.1093/bib/bbw071] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 06/14/2016] [Indexed: 02/06/2023] Open
Abstract
The genetic, proteomic, disease and pharmacological studies have generated rich data in protein interaction, disease regulation and drug activities useful for systems-level study of the biological, disease and drug therapeutic processes. These studies are facilitated by the established and the emerging computational methods. More recently, the network descriptors developed in other disciplines have become more increasingly used for studying the protein-protein, gene regulation, metabolic, disease networks. There is an inadequate coverage of these useful network features in the public web servers. We therefore introduced upto 313 literature-reported network descriptors in PROFEAT web server, for describing the topological, connectivity and complexity characteristics of undirected unweighted (uniform binding constants and molecular levels), undirected edge-weighted (varying binding constants), undirected node-weighted (varying molecular levels), undirected edge-node-weighted (varying binding constants and molecular levels) and directed unweighted (oriented process) networks. The usefulness of the PROFEAT computed network descriptors is illustrated by their literature-reported applications in studying the protein-protein, gene regulatory, gene co-expression, protein-drug and metabolic networks. PROFEAT is accessible free of charge at http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi.
Collapse
Affiliation(s)
- Peng Zhang
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore
- College of Science, Sichuan Agricultural University, Yaan, P. R. China
| | - Lin Tao
- College of Science, Sichuan Agricultural University, Yaan, P. R. China
| | - Xian Zeng
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore
| | - Chu Qin
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore
| | - Shangying Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore
| | - Feng Zhu
- College of Chemistry, Sichuan University, Chengdu, P. R. China
| | - Zerong Li
- Molecular Medicine Research Center, State Key Laboratory of Biotherapy, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, P. R. China
- Key Lab of Agricultural Products Processing and Quality Control of Nanchang City, Jiangxi Agricultural University, Nanchang, P. R. China
| | - Yuyang Jiang
- The Ministry-Province Jointly Constructed Base for State Key Lab, Shenzhen Technology and Engineering Lab for Personalized Cancer Diagnostics and Therapeutics, and Shenzhen Kivita Innovative Drug Discovery Institute, Tsinghua University Shenzhen Graduate School, Shenzhen, P.R. China
| | - Weiping Chen
- Key Lab of Agricultural Products Processing and Quality Control of Nanchang City, Jiangxi Agricultural University, Nanchang, P. R. China
| | - Yu-Zong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore
| |
Collapse
|
6
|
PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks. J Mol Biol 2016; 429:416-425. [PMID: 27742592 DOI: 10.1016/j.jmb.2016.10.013] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Revised: 09/25/2016] [Accepted: 10/06/2016] [Indexed: 02/05/2023]
Abstract
The studies of biological, disease, and pharmacological networks are facilitated by the systems-level investigations using computational tools. In particular, the network descriptors developed in other disciplines have found increasing applications in the study of the protein, gene regulatory, metabolic, disease, and drug-targeted networks. Facilities are provided by the public web servers for computing network descriptors, but many descriptors are not covered, including those used or useful for biological studies. We upgraded the PROFEAT web server http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi for computing up to 329 network descriptors and protein-protein interaction descriptors. PROFEAT network descriptors comprehensively describe the topological and connectivity characteristics of unweighted (uniform binding constants and molecular levels), edge-weighted (varying binding constants), node-weighted (varying molecular levels), edge-node-weighted (varying binding constants and molecular levels), and directed (oriented processes) networks. The usefulness of the network descriptors is illustrated by the literature-reported studies of the biological networks derived from the genome, interactome, transcriptome, metabolome, and diseasome profiles.
Collapse
|
7
|
Pavlopoulos GA, Malliarakis D, Papanikolaou N, Theodosiou T, Enright AJ, Iliopoulos I. Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future. Gigascience 2015; 4:38. [PMID: 26309733 PMCID: PMC4548842 DOI: 10.1186/s13742-015-0077-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Accepted: 08/03/2015] [Indexed: 01/31/2023] Open
Abstract
"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.
Collapse
Affiliation(s)
- Georgios A Pavlopoulos
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete, Medical School, 70013 Heraklion, Crete Greece
| | | | - Nikolas Papanikolaou
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete, Medical School, 70013 Heraklion, Crete Greece
| | - Theodosis Theodosiou
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete, Medical School, 70013 Heraklion, Crete Greece
| | - Anton J Enright
- EMBL - European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD UK
| | - Ioannis Iliopoulos
- Bioinformatics & Computational Biology Laboratory, Division of Basic Sciences, University of Crete, Medical School, 70013 Heraklion, Crete Greece
| |
Collapse
|
8
|
Pastrello C, Pasini E, Kotlyar M, Otasek D, Wong S, Sangrar W, Rahmati S, Jurisica I. Integration, visualization and analysis of human interactome. Biochem Biophys Res Commun 2014; 445:757-73. [DOI: 10.1016/j.bbrc.2014.01.151] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Accepted: 01/24/2014] [Indexed: 02/06/2023]
|