Deploying Big Data to Crack the Genotype to Phenotype Code
Deploying Big Data to Crack the Genotype to Phenotype Code
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DOI：
10.1093/icb/icaa055
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发表时间：
2020-08-01
影响因子：
2.6
通讯作者：
Sanford，Christopher P. J.
中科院分区：
文献类型：
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作者：
Westerman，Erica L.;Bowman，Sarah E. J.;Sanford，Christopher P. J.
从机制上将基因型与表型联系起来是生物学的一项长期且核心的使命。破译这些联系将把从分子到生态系统的所有尺度的问题和数据集结合起来。尽管高通量测序为开展这项工作提供了丰富的平台，但用于进一步解读基因组到表型组管道机制的工具仍然有限。机器学习方法和其他新兴计算工具有望增强人类克服这些障碍的努力。这篇愿景论文是重新整合生物学研讨会的成果，汇集了综合生物学家和比较生物学家的观点，调查破解基因型到表型密码的挑战和机遇，从而生成跨生物尺度的预测框架。主要建议包括促进实验设计和数据收集的最低限度“最佳实践”的发展；培育持续和长期的数据存储库；促进招聘、培训和留住多元化人才的计划；并提供资金以有效支持这些高度跨学科的努力。在这次讨论之后，我们重点介绍了这些努力将推动的一些具体的变革性研究机会。
Mechanistically connecting genotypes to phenotypes is a longstanding and central mission of biology. Deciphering these connections will unite questions and datasets across all scales from molecules to ecosystems. Although high-throughput sequencing has provided a rich platform on which to launch this effort, tools for deciphering mechanisms further along the genome to phenome pipeline remain limited. Machine learning approaches and other emerging computational tools hold the promise of augmenting human efforts to overcome these obstacles. This vision paper is the result of a Reintegrating Biology Workshop, bringing together the perspectives of integrative and comparative biologists to survey challenges and opportunities in cracking the genotype to phenotype code and thereby generating predictive frameworks across biological scales. Key recommendations include promoting the development of minimum “best practices” for the experimental design and collection of data; fostering sustained and long-term data repositories; promoting programs that recruit, train, and retain a diversity of talent; and providing funding to effectively support these highly cross-disciplinary efforts. We follow this discussion by highlighting a few specific transformative research opportunities that will be advanced by these efforts.