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Golin N, Barreto LS, Esquivel L, Souza TLD, Nazário MG, Oliveira AP, Martins CC, Oliveira Ribeiro CAD. Organic and inorganic pollutants in Jordão and Iguaçu rivers southern Brazil impact early phases of Rhamdia quelen and represent a risk for population. CHEMOSPHERE 2022; 303:134989. [PMID: 35595115 DOI: 10.1016/j.chemosphere.2022.134989] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/13/2022] [Indexed: 06/15/2023]
Abstract
The Iguaçu River basin presents high ecological importance due to its expressive endemic ichthyofauna rate, but chemical pollution may threat this biodiversity. Jordão River is one of the main tributaries of Iguaçu River and contribute to this pollution status, since it drains large agricultural areas receiving domestic and industrial effluents before flowing into the Iguaçu River. The objective of the current study was to evaluate the toxic effects of the Iguaçu, Jordão, and the combination of their waters to the embryo-larval phase of R. quelen, investigating the consequences to the population by means of mathematical modelling. R. quelen fertilized eggs were exposed for 96 h to water samples from Iguaçu River upstream (IR), Jordão River (JR), and downstream of both rivers (MR). The analysis of micropollutants in the water showed that JR presented the most complex mixture of substances and elements, followed by IR, while MR showed the lower number of micropollutants detected. Survival rate was not a sensitive endpoint, while the deformity indices were higher in individuals exposed to water from the three studied sites. Superoxide dismutase activity was increased in MR, while non-protein thiol levels were reduced in MR and JR showing the antioxidant mechanism activation. The mathematical modelling revealed that fish exposed to JR would lead to the greater population reduction (46.19%), followed by IR (40.48%) and MR (33.33%). Although the results showed toxicity in all studied sites, the JR site is the most impacted by micropollutants but decrease its toxicity after dilution with Iguaçu River.
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Affiliation(s)
- Natália Golin
- Laboratório de Toxicologia Celular, Departamento de Biologia Celular, Universidade Federal do Paraná, CEP, 81531-980, Curitiba, PR, Brazil; Programa de Pós-Graduação em Ecologia e Conservação, Setor de Ciências Biológicas, Universidade Federal do Paraná, CEP, 81531-980, Curitiba, Paraná, Brazil
| | - Luiza Santos Barreto
- Laboratório de Toxicologia Celular, Departamento de Biologia Celular, Universidade Federal do Paraná, CEP, 81531-980, Curitiba, PR, Brazil; Programa de Pós-Graduação em Ecologia e Conservação, Setor de Ciências Biológicas, Universidade Federal do Paraná, CEP, 81531-980, Curitiba, Paraná, Brazil
| | - Luíse Esquivel
- Estação de Piscicultura Panamá, Est. Geral Bom Retiro, CEP, 88490-000, Paulo Lopes, SC, Brazil
| | - Tugstênio Lima de Souza
- Laboratório de Toxicologia Celular, Departamento de Biologia Celular, Universidade Federal do Paraná, CEP, 81531-980, Curitiba, PR, Brazil; Programa de Pós-Graduação em Biologia Celular e Molecular, Setor de Ciências Biológicas, Universidade Federal do Paraná, CEP, 81531-980, Curitiba, Paraná, Brazil
| | - Mariana Gallucci Nazário
- Laboratório de Análises Ambientais, Setor Litoral, Universidade Federal do Paraná, CEP, 83.260-000, Matinhos, PR, Brazil
| | - Andrea Pinto Oliveira
- Departamento de Química, Setor de Ciências Exatas, Centro Politécnico, Universidade Federal do Paraná, CEP, 81531-980, Curitiba, Paraná, Brazil
| | - César Castro Martins
- Centro de Estudos do Mar, Campus Pontal do Paraná, Universidade Federal do Paraná, CEP, 83255-000, Pontal do Paraná, Paraná, Brazil
| | - Ciro Alberto de Oliveira Ribeiro
- Laboratório de Toxicologia Celular, Departamento de Biologia Celular, Universidade Federal do Paraná, CEP, 81531-980, Curitiba, PR, Brazil.
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2
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Serral F, Castello FA, Sosa EJ, Pardo AM, Palumbo MC, Modenutti C, Palomino MM, Lazarowski A, Auzmendi J, Ramos PIP, Nicolás MF, Turjanski AG, Martí MA, Fernández Do Porto D. From Genome to Drugs: New Approaches in Antimicrobial Discovery. Front Pharmacol 2021; 12:647060. [PMID: 34177572 PMCID: PMC8219968 DOI: 10.3389/fphar.2021.647060] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 05/17/2021] [Indexed: 01/31/2023] Open
Abstract
Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a "big-data era" that improves target selection and lead compound identification in a cost-effective and shortened timeline.
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Affiliation(s)
- Federico Serral
- Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Florencia A Castello
- Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Ezequiel J Sosa
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Ciudad Universitaria, Buenos Aires, Argentina
| | - Agustín M Pardo
- Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Ciudad Universitaria, Buenos Aires, Argentina
| | - Miranda Clara Palumbo
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Carlos Modenutti
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Ciudad Universitaria, Buenos Aires, Argentina
| | - María Mercedes Palomino
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Ciudad Universitaria, Buenos Aires, Argentina
| | - Alberto Lazarowski
- Departamento de Bioquímica Clínica, Instituto de Investigaciones en Fisiopatología y Bioquímica Clínica (INFIBIOC), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Jerónimo Auzmendi
- Departamento de Bioquímica Clínica, Instituto de Investigaciones en Fisiopatología y Bioquímica Clínica (INFIBIOC), Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Pablo Ivan P Ramos
- Centro de Integração de Dados e Conhecimentos para Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (FIOCRUZ), Salvador, Brazil
| | - Marisa F Nicolás
- Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Brazil
| | - Adrián G Turjanski
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Ciudad Universitaria, Buenos Aires, Argentina
| | - Marcelo A Martí
- Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales (IQUIBICEN) CONICET, Ciudad Universitaria, Buenos Aires, Argentina
| | - Darío Fernández Do Porto
- Instituto de Cálculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.,Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
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Amoako DG, Somboro AM, Abia ALK, Allam M, Ismail A, Bester LA, Essack SY. Genome Mining and Comparative Pathogenomic Analysis of An Endemic Methicillin-Resistant Staphylococcus Aureus (MRSA) Clone, ST612-CC8-t1257-SCCmec_IVd(2B), Isolated in South Africa. Pathogens 2019; 8:E166. [PMID: 31569754 PMCID: PMC6963616 DOI: 10.3390/pathogens8040166] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/16/2019] [Accepted: 09/17/2019] [Indexed: 12/19/2022] Open
Abstract
This study undertook genome mining and comparative genomics to gain genetic insights into the dominance of the methicillin-resistant Staphylococcus aureus (MRSA) endemic clone ST612-CC8-t1257-SCCmec_IVd(2B), obtained from the poultry food chain in South Africa. Functional annotation of the genome revealed a vast array of similar central metabolic, cellular and biochemical networks within the endemic clone crucial for its survival in the microbial community. In-silico analysis of the clone revealed the possession of uniform defense systems, restriction-modification system (type I and IV), accessory gene regulator (type I), arginine catabolic mobile element (type II), and type 1 clustered, regularly interspaced, short palindromic repeat (CRISPR)Cas array (N = 7 ± 1), which offer protection against exogenous attacks. The estimated pathogenic potential predicted a higher probability (average Pscore ≈ 0.927) of the clone being pathogenic to its host. The clone carried a battery of putative virulence determinants whose expression are critical for establishing infection. However, there was a slight difference in their possession of adherence factors (biofilm operon system) and toxins (hemolysins and enterotoxins). Further analysis revealed a conserved environmental tolerance and persistence mechanisms related to stress (oxidative and osmotic), heat shock, sporulation, bacteriocins, and detoxification, which enable it to withstand lethal threats and contribute to its success in diverse ecological niches. Phylogenomic analysis with close sister lineages revealed that the clone was closely related to the MRSA isolate SHV713 from Australia. The results of this bioinformatic analysis provide valuable insights into the biology of this endemic clone.
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Affiliation(s)
- Daniel Gyamfi Amoako
- Infection Genomics and Applied Bioinformatics Division, Antimicrobial Research Unit, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa.
- Biomedical Resource Unit, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal; Durban 4000, South Africa.
| | - Anou M Somboro
- Biomedical Resource Unit, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal; Durban 4000, South Africa.
- Antimicrobial Research Unit, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa.
| | - Akebe Luther King Abia
- Antimicrobial Research Unit, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa.
| | - Mushal Allam
- Sequencing Core Facility, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg 2131, South Africa.
| | - Arshad Ismail
- Sequencing Core Facility, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg 2131, South Africa.
| | - Linda A Bester
- Biomedical Resource Unit, School of Laboratory Medicine and Medical Sciences, College of Health Sciences, University of KwaZulu-Natal; Durban 4000, South Africa.
| | - Sabiha Y Essack
- Antimicrobial Research Unit, College of Health Sciences, University of KwaZulu-Natal, Durban 4000, South Africa.
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Wang LL, Thomas Hayman G, Smith JR, Tutaj M, Shimoyama ME, Gennari JH. Predicting instances of pathway ontology classes for pathway integration. J Biomed Semantics 2019; 10:11. [PMID: 31196182 PMCID: PMC6567466 DOI: 10.1186/s13326-019-0202-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 05/22/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND To improve the outcomes of biological pathway analysis, a better way of integrating pathway data is needed. Ontologies can be used to organize data from disparate sources, and we leverage the Pathway Ontology as a unifying ontology for organizing pathway data. We aim to associate pathway instances from different databases to the appropriate class in the Pathway Ontology. RESULTS Using a supervised machine learning approach, we trained neural networks to predict mappings between Reactome pathways and Pathway Ontology (PW) classes. For 2222 Reactome classes, the neural network (NN) model generated 10,952 class recommendations. We compared against a baseline bag-of-words (BOW) model for predicting correct PW classes. A 5% subset of Reactome pathways (111 pathways) was randomly selected, and the corresponding class recommendations from both models were evaluated by two curators. The precision of the BOW model was higher (0.49 for BOW and 0.39 for NN), but the recall was lower (0.42 for BOW and 0.78 for NN). Around 78% of Reactome pathways received pertinent recommendations from the NN model. CONCLUSIONS The neural predictive model produced meaningful class recommendations that assisted PW curators in selecting appropriate class mappings for Reactome pathways. Our methods can be used to reduce the manual effort associated with ontology curation, and more broadly, for augmenting the curators' ability to organize and integrate data from pathway databases using the Pathway Ontology.
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Affiliation(s)
- Lucy Lu Wang
- Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican St, Seattle, 98109, WA, USA.
| | - G Thomas Hayman
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, 53226, WI, USA
| | - Jennifer R Smith
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, 53226, WI, USA
| | - Monika Tutaj
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, 53226, WI, USA
| | - Mary E Shimoyama
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, 53226, WI, USA
| | - John H Gennari
- Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican St, Seattle, 98109, WA, USA
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5
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Metabolomics applied to diabetes-lessons from human population studies. Int J Biochem Cell Biol 2017; 93:136-147. [PMID: 29074437 DOI: 10.1016/j.biocel.2017.10.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 09/30/2017] [Accepted: 10/20/2017] [Indexed: 02/08/2023]
Abstract
The 'classical' distribution of type 2 diabetes (T2D) across the globe is rapidly changing and it is no longer predominantly a disease of middle-aged/elderly adults of western countries, but it is becoming more common through Asia and the Middle East, as well as increasingly found in younger individuals. This global altered incidence of T2D is most likely associated with the spread of western diets and sedentary lifestyles, although there is still much debate as to whether the increased incidence rates are due to an overconsumption of fats, sugars or more generally high-calorie foods. In this context, understanding the interactions between genes of risk and diet and how they influence the incidence of T2D will help define the causative pathways of the disease. This review focuses on the use of metabolomics in large cohort studies to follow the incidence of type 2 diabetes in different populations. Such approaches have been used to identify new biomarkers of pre-diabetes, such as branch chain amino acids, and associate metabolomic profiles with genes of known risk in T2D from large scale GWAS studies. As the field develops, there are also examples of meta-analysis across metabolomics cohort studies and cross-comparisons with different populations to allow us to understand how genes and diet contribute to disease risk. Such approaches demonstrate that insulin resistance and T2D have far reaching metabolic effects beyond raised blood glucose and how the disease impacts systemic metabolism.
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Minkiewicz P, Darewicz M, Iwaniak A, Bucholska J, Starowicz P, Czyrko E. Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science. Int J Mol Sci 2016; 17:ijms17122039. [PMID: 27929431 PMCID: PMC5187839 DOI: 10.3390/ijms17122039] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Revised: 11/17/2016] [Accepted: 11/29/2016] [Indexed: 01/02/2023] Open
Abstract
Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.
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Affiliation(s)
- Piotr Minkiewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Małgorzata Darewicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Anna Iwaniak
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Justyna Bucholska
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Piotr Starowicz
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
| | - Emilia Czyrko
- Department of Food Biochemistry, University of Warmia and Mazury in Olsztyn, Plac Cieszyński 1, 10-726 Olsztyn-Kortowo, Poland.
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Abstract
Developing improved approaches for diagnosis, treatment, and prevention of diseases is a major goal of biomedical research. Therefore, the discovery of biomarker signatures from high-throughput "omics" data is an active research topic in the field of bioinformatics and systems medicine. A major issue is the low reproducibility and the limited biological interpretability of candidate biomarker signatures identified from high-throughput data. This impedes the use of discovered biomarker signatures into clinical applications. Currently, much focus is placed on developing strategies to improve reproducibility and interpretability. Researchers have fruitfully started to incorporate prior knowledge derived from pathways and molecular networks into the process of biomarker identification. In this chapter, after giving a general introduction to the problem of disease classification and biomarker discovery, we will review two types of network-assisted approaches: (1) approaches inferring activity scores for specific pathways which are subsequently used for classification and (2) approaches identifying subnetworks or modules of molecular networks by differential network analysis which can serve as biomarker signatures.
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8
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Abstract
The exponential growth of the Internet of Things and the global popularity and remarkable decline in cost of the mobile phone is driving the digital transformation of medical practice. The rapidly maturing digital, non-medical world of mobile (wireless) devices, cloud computing and social networking is coalescing with the emerging digital medical world of omics data, biosensors and advanced imaging which offers the increasingly realistic prospect of personalized medicine. Described as a potential “seismic” shift from the current “healthcare” model to a “wellness” paradigm that is predictive, preventative, personalized and participatory, this change is based on the development of increasingly sophisticated biosensors which can track and measure key biochemical variables in people. Additional key drivers in this shift are metabolomic and proteomic signatures, which are increasingly being reported as pre-symptomatic, diagnostic and prognostic of toxicity and disease. These advancements also have profound implications for toxicological evaluation and safety assessment of pharmaceuticals and environmental chemicals. An approach based primarily on human in vivo and high-throughput in vitro human cell-line data is a distinct possibility. This would transform current chemical safety assessment practice which operates in a human “data poor” to a human “data rich” environment. This could also lead to a seismic shift from the current animal-based to an animal-free chemical safety assessment paradigm.
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Affiliation(s)
- George D Loizou
- Health Risks, Health and Safety Laboratory, Health and Safety Executive Buxton, UK
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Lapatas V, Stefanidakis M, Jimenez RC, Via A, Schneider MV. Data integration in biological research: an overview. JOURNAL OF BIOLOGICAL RESEARCH (THESSALONIKE, GREECE) 2015; 22:9. [PMID: 26336651 PMCID: PMC4557916 DOI: 10.1186/s40709-015-0032-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 08/10/2015] [Indexed: 11/16/2022]
Abstract
Data sharing, integration and annotation are essential to ensure the reproducibility of the analysis and interpretation of the experimental findings. Often these activities are perceived as a role that bioinformaticians and computer scientists have to take with no or little input from the experimental biologist. On the contrary, biological researchers, being the producers and often the end users of such data, have a big role in enabling biological data integration. The quality and usefulness of data integration depend on the existence and adoption of standards, shared formats, and mechanisms that are suitable for biological researchers to submit and annotate the data, so it can be easily searchable, conveniently linked and consequently used for further biological analysis and discovery. Here, we provide background on what is data integration from a computational science point of view, how it has been applied to biological research, which key aspects contributed to its success and future directions.
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Affiliation(s)
- Vasileios Lapatas
- />Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu, 49100 Greece
| | - Michalis Stefanidakis
- />Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu, 49100 Greece
| | | | - Allegra Via
- />Biocomputing Group, Sapienza University, Piazzale Aldo Moro 5, Rome, 00185 Italy
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Chen YA, Tripathi LP, Dessailly BH, Nyström-Persson J, Ahmad S, Mizuguchi K. Integrated pathway clusters with coherent biological themes for target prioritisation. PLoS One 2014; 9:e99030. [PMID: 24918583 PMCID: PMC4053319 DOI: 10.1371/journal.pone.0099030] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Accepted: 05/07/2014] [Indexed: 12/15/2022] Open
Abstract
Prioritising candidate genes for further experimental characterisation is an essential, yet challenging task in biomedical research. One way of achieving this goal is to identify specific biological themes that are enriched within the gene set of interest to obtain insights into the biological phenomena under study. Biological pathway data have been particularly useful in identifying functional associations of genes and/or gene sets. However, biological pathway information as compiled in varied repositories often differs in scope and content, preventing a more effective and comprehensive characterisation of gene sets. Here we describe a new approach to constructing biologically coherent gene sets from pathway data in major public repositories and employing them for functional analysis of large gene sets. We first revealed significant overlaps in gene content between different pathways and then defined a clustering method based on the shared gene content and the similarity of gene overlap patterns. We established the biological relevance of the constructed pathway clusters using independent quantitative measures and we finally demonstrated the effectiveness of the constructed pathway clusters in comparative functional enrichment analysis of gene sets associated with diverse human diseases gathered from the literature. The pathway clusters and gene mappings have been integrated into the TargetMine data warehouse and are likely to provide a concise, manageable and biologically relevant means of functional analysis of gene sets and to facilitate candidate gene prioritisation.
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Affiliation(s)
- Yi-An Chen
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
| | | | | | | | - Shandar Ahmad
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
| | - Kenji Mizuguchi
- National Institute of Biomedical Innovation, Ibaraki, Osaka, Japan
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Semantic particularity measure for functional characterization of gene sets using gene ontology. PLoS One 2014; 9:e86525. [PMID: 24489737 PMCID: PMC3904913 DOI: 10.1371/journal.pone.0086525] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Accepted: 12/11/2013] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity. RESULTS We propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term's distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure. CONCLUSION Semantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.
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