- Hosking, Christopher, Driguez, Patrick, McWilliam, Hamish, Ilag, Leodevico, Gladman, Simon, Li, Yuesheng, Piedrafita, David, McManus, Donald, Meeusen, Els, De Veer, Michael
- Authors: Hosking, Christopher , Driguez, Patrick , McWilliam, Hamish , Ilag, Leodevico , Gladman, Simon , Li, Yuesheng , Piedrafita, David , McManus, Donald , Meeusen, Els , De Veer, Michael
- Date: 2015
- Type: Text , Journal article
- Relation: International Journal for Parasitology Vol. 45, no. 11 (2015), p. 729-740
- Full Text: false
- Reviewed:
- Description: Antibodies isolated from the local draining inguinal lymph node of field exposed-water buffaloes following challenge with Schistosoma japonicum cercariae showed high reactivity towards S. japonicum antigen preparations and bound specifically to formaldehyde-fixed S. japonicum schistosomules. Using this specific local immune response we produced a series of single-chain antibody Fv domain libraries from the same lymph nodes. Removal of phage that cross reacted with epitopes on adult parasites yielded a single-chain antibody Fv domain-phage library that specifically bound to whole formaldehyde-fixed and live S. japonicum schistosomules. DNA sequencing indicated clear enrichment of the single-chain antibody Fv domain library for buffalo B-cell complementarity determining regions post-selection for schistosomule binding. This study also revealed that long heavy chain complementarity determining regions appear to be an important factor when selecting for antibody binding fragments against schistosomule proteins. The selected single-chain antibody Fv domain-phage were used to probe a schistosome-specific protein microarray, which resulted in the recognition of many proteins expressed across all schistosome life-cycle stages. Following absorption to adult worms, the single-chain antibody Fv domain-phage library showed significantly reduced binding to most proteins, whilst two proteins (NCBI GenBank accession numbers AY915878 and AY815196) showed increased binding. We have thus developed a unique set of host derived single-chain antibody Fv domains comprising buffalo B-cell variable regions that specifically bind to early S. japonicum life-stages. © 2015 Australian Society for Parasitology Inc..
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.
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