Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA.
High-Throughput Crystallization Screening Center, Hauptman-Woodward Medical Research Institute, Buffalo, NY 14203, USA.
Integr Comp Biol. 2020 Aug 1;60(2):385-396. doi: 10.1093/icb/icaa055.
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.
将基因型与表型联系起来是生物学的一个长期而核心的任务。破译这些联系将把从分子到生态系统各个尺度的问题和数据集统一起来。尽管高通量测序为开展这项工作提供了一个丰富的平台，但破译基因组到表型管道中更深入机制的工具仍然有限。机器学习方法和其他新兴的计算工具有望增强人类克服这些障碍的努力。本文是一次整合生物学研讨会的成果，汇集了综合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，从而在生物尺度上生成预测框架。主要建议包括促进制定实验设计和数据收集的最低“最佳实践”；支持长期和可持续的数据存储库；促进招聘、培训和留住多样化人才的计划；并为这些高度跨学科的努力提供资金支持。我们在这一讨论之后，强调了这些努力将推动的一些具体的变革性研究机会。
