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Integrating bioinformatic strategies in spatial life science research
As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677476/ https://www.ncbi.nlm.nih.gov/pubmed/36198665 http://dx.doi.org/10.1093/bib/bbac415 |
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author | Hao, Yangyang Lu, Liang Liu, Anna Lin, Xue Xiao, Li Kong, Xiaoyue Li, Kai Liang, Fengji Xiong, Jianghui Qu, Lina Li, Yinghui Li, Jian |
author_facet | Hao, Yangyang Lu, Liang Liu, Anna Lin, Xue Xiao, Li Kong, Xiaoyue Li, Kai Liang, Fengji Xiong, Jianghui Qu, Lina Li, Yinghui Li, Jian |
author_sort | Hao, Yangyang |
collection | PubMed |
description | As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools. |
format | Online Article Text |
id | pubmed-9677476 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-96774762022-11-21 Integrating bioinformatic strategies in spatial life science research Hao, Yangyang Lu, Liang Liu, Anna Lin, Xue Xiao, Li Kong, Xiaoyue Li, Kai Liang, Fengji Xiong, Jianghui Qu, Lina Li, Yinghui Li, Jian Brief Bioinform Review As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools. Oxford University Press 2022-10-05 /pmc/articles/PMC9677476/ /pubmed/36198665 http://dx.doi.org/10.1093/bib/bbac415 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Review Hao, Yangyang Lu, Liang Liu, Anna Lin, Xue Xiao, Li Kong, Xiaoyue Li, Kai Liang, Fengji Xiong, Jianghui Qu, Lina Li, Yinghui Li, Jian Integrating bioinformatic strategies in spatial life science research |
title | Integrating bioinformatic strategies in spatial life science research |
title_full | Integrating bioinformatic strategies in spatial life science research |
title_fullStr | Integrating bioinformatic strategies in spatial life science research |
title_full_unstemmed | Integrating bioinformatic strategies in spatial life science research |
title_short | Integrating bioinformatic strategies in spatial life science research |
title_sort | integrating bioinformatic strategies in spatial life science research |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677476/ https://www.ncbi.nlm.nih.gov/pubmed/36198665 http://dx.doi.org/10.1093/bib/bbac415 |
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