Climate change is predicted to increase the severity and prevalence of harmful algal blooms (HABs). In the past twenty years, omics techniques such as genomics, transcriptomics, proteomics and metabolomics have transformed that data landscape of many fields including the study of HABs. Advances in technology have facilitated the creation of many publicly available omics datasets that are complementary and shed new light on the mechanisms of HAB formation and toxin production. Genomics have been used to reveal differences in toxicity and nutritional requirements, while transcriptomics and proteomics have been used to explore HAB species responses to environmental stressors, and metabolomics can reveal mechanisms of allelopathy and toxicity. In this review, we explore how omics data may be leveraged to improve predictions of how climate change will impact HAB dynamics. We also highlight important gaps in our knowledge of HAB prediction, which include swimming behaviors, microbial interactions and evolution that can be addressed by future studies with omics tools. Lastly, we discuss approaches to incorporate current omics datasets into predictive numerical models that may enhance HAB prediction in a changing world. With the ever-increasing omics databases, leveraging these data for understanding climate-driven HAB dynamics will be increasingly powerful.

预计气候变化会增加有害藻华（HABs）的严重性和流行率。在过去的二十年中，基因组学，转录组学，蛋白质组学和代谢组学等组学技术已经改变了包括HABs研究在内的许多领域的数据格局。技术的进步促进了许多公共组学数据集的创建，这些数据集是互补的，并为HAB形成和毒素产生的机理提供了新的思路。基因组学已被用于揭示毒性和营养需求方面的差异，而转录组学和蛋白质组学已被用于探索HAB物种对环境胁迫的反应，而代谢组学则可以揭示化感作用和毒性的机制。在这篇评论中，我们探讨了如何利用组学数据来改善对气候变化将如何影响HAB动态的预测。我们还强调了我们对HAB预测知识的重要差距，其中包括游泳行为，微生物相互作用和进化，这些可以通过未来使用组学工具进行研究来解决。最后，我们讨论了将当前组学数据集合并到预测数值模型中的方法，这些方法可能会增强在不断变化的世界中的HAB预测。随着组学数据库的不断增加，利用这些数据来了解气候驱动的HAB动力学将变得越来越强大。我们讨论了将当前组学数据集合并到预测数值模型中的方法，这些方法可能会增强在不断变化的世界中的HAB预测。随着组学数据库的不断增加，利用这些数据来了解气候驱动的HAB动力学将变得越来越强大。我们讨论了将当前组学数据集合并到预测数值模型中的方法，这些方法可能会增强在不断变化的世界中的HAB预测。随着组学数据库的不断增加，利用这些数据来了解气候驱动的HAB动力学将变得越来越强大。