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.

中文翻译：

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部署大数据破解基因型到表型代码

从机制上将基因型与表型联系起来是生物学的一项长期而核心的任务。破译这些联系将统一从分子到生态系统的所有尺度的问题和数据集。尽管高通量测序为开展这项工作提供了一个丰富的平台，但用于破译基因组到现象组管道中的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。这份愿景文件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，以调查破解基因型到表型代码的挑战和机遇，从而生成跨生物规模的预测框架。主要建议包括：促进为实验设计和数据收集制定最低限度的“最佳实践”；培养持续和长期的数据存储库；促进招聘、培训和留住各种人才的计划，并提供资金以有效支持这些高度跨学科的工作。我们通过强调这些努力将推动的一些特定的变革性研究机会来关注这一讨论。