Best practice data life cycle approaches for the life sciences
- Griffin, Philippa, Khadake, Jyoti, LeMay, Kate, Lewis, Suzanna, Orchard, Sandra, Pask, Andrew, Pope, Bernard, Roessner, Ute, Russell, Keith, Seemann, Torsten, Treloar, Andrew, Tyagi, Sonika, Christiansen, Jeffrey, Dayalan, Saravanan, Gladman, Simon, Hangartner, Sandra, Hayden, Helen, Ho, William, Keeble-Gagnère, Gabriel, Korhonen, Pasi, Neish, Peter, Prestes, Priscilla, Richardson, Mark, Watson-Haigh, Nathan, Wyres, Kelly, Young, Neil, Schneider, Maria
- Authors: Griffin, Philippa , Khadake, Jyoti , LeMay, Kate , Lewis, Suzanna , Orchard, Sandra , Pask, Andrew , Pope, Bernard , Roessner, Ute , Russell, Keith , Seemann, Torsten , Treloar, Andrew , Tyagi, Sonika , Christiansen, Jeffrey , Dayalan, Saravanan , Gladman, Simon , Hangartner, Sandra , Hayden, Helen , Ho, William , Keeble-Gagnère, Gabriel , Korhonen, Pasi , Neish, Peter , Prestes, Priscilla , Richardson, Mark , Watson-Haigh, Nathan , Wyres, Kelly , Young, Neil , Schneider, Maria
- Date: 2018
- Type: Text , Journal article
- Relation: F1000 Research Vol. 6, no. (2018), p. 1-28
- Full Text:
- Reviewed:
- Description: Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices. © 2018 Griffin PC et al.
- Authors: Griffin, Philippa , Khadake, Jyoti , LeMay, Kate , Lewis, Suzanna , Orchard, Sandra , Pask, Andrew , Pope, Bernard , Roessner, Ute , Russell, Keith , Seemann, Torsten , Treloar, Andrew , Tyagi, Sonika , Christiansen, Jeffrey , Dayalan, Saravanan , Gladman, Simon , Hangartner, Sandra , Hayden, Helen , Ho, William , Keeble-Gagnère, Gabriel , Korhonen, Pasi , Neish, Peter , Prestes, Priscilla , Richardson, Mark , Watson-Haigh, Nathan , Wyres, Kelly , Young, Neil , Schneider, Maria
- Date: 2018
- Type: Text , Journal article
- Relation: F1000 Research Vol. 6, no. (2018), p. 1-28
- Full Text:
- Reviewed:
- Description: Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices. © 2018 Griffin PC et al.
A review of analytical techniques and their application in disease diagnosis in breathomics and salivaomics research
- Beale, David, Jones, Oliver, Karpe, Avinash, Dayalan, Saravanan, Oh, Ding, Kouremenos, Konstantinos, Ahmed, Warish, Palombo, Enzo
- Authors: Beale, David , Jones, Oliver , Karpe, Avinash , Dayalan, Saravanan , Oh, Ding , Kouremenos, Konstantinos , Ahmed, Warish , Palombo, Enzo
- Date: 2017
- Type: Text , Journal article
- Relation: International Journal of Molecular Sciences Vol. 18, no. 1 (2017), p. 1-26
- Full Text:
- Reviewed:
- Description: The application of metabolomics to biological samples has been a key focus in systems biology research, which is aimed at the development of rapid diagnostic methods and the creation of personalized medicine. More recently, there has been a strong focus towards this approach applied to non-invasively acquired samples, such as saliva and exhaled breath. The analysis of these biological samples, in conjunction with other sample types and traditional diagnostic tests, has resulted in faster and more reliable characterization of a range of health disorders and diseases. As the sampling process involved in collecting exhaled breath and saliva is non-intrusive as well as comparatively low-cost and uses a series of widely accepted methods, it provides researchers with easy access to the metabolites secreted by the human body. Owing to its accuracy and rapid nature, metabolomic analysis of saliva and breath (known as salivaomics and breathomics, respectively) is a rapidly growing field and has shown potential to be effective in detecting and diagnosing the early stages of numerous diseases and infections in preclinical studies. This review discusses the various collection and analyses methods currently applied in two of the least used non-invasive sample types in metabolomics, specifically their application in salivaomics and breathomics research. Some of the salient research completed in this field to date is also assessed and discussed in order to provide a basis to advocate their use and possible future scientific directions. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
- Authors: Beale, David , Jones, Oliver , Karpe, Avinash , Dayalan, Saravanan , Oh, Ding , Kouremenos, Konstantinos , Ahmed, Warish , Palombo, Enzo
- Date: 2017
- Type: Text , Journal article
- Relation: International Journal of Molecular Sciences Vol. 18, no. 1 (2017), p. 1-26
- Full Text:
- Reviewed:
- Description: The application of metabolomics to biological samples has been a key focus in systems biology research, which is aimed at the development of rapid diagnostic methods and the creation of personalized medicine. More recently, there has been a strong focus towards this approach applied to non-invasively acquired samples, such as saliva and exhaled breath. The analysis of these biological samples, in conjunction with other sample types and traditional diagnostic tests, has resulted in faster and more reliable characterization of a range of health disorders and diseases. As the sampling process involved in collecting exhaled breath and saliva is non-intrusive as well as comparatively low-cost and uses a series of widely accepted methods, it provides researchers with easy access to the metabolites secreted by the human body. Owing to its accuracy and rapid nature, metabolomic analysis of saliva and breath (known as salivaomics and breathomics, respectively) is a rapidly growing field and has shown potential to be effective in detecting and diagnosing the early stages of numerous diseases and infections in preclinical studies. This review discusses the various collection and analyses methods currently applied in two of the least used non-invasive sample types in metabolomics, specifically their application in salivaomics and breathomics research. Some of the salient research completed in this field to date is also assessed and discussed in order to provide a basis to advocate their use and possible future scientific directions. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
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