evolSOM：一个利用SOM进行进化保守性分析的R包
evolSOM: an R Package for evolutionary conservation analysis with SOMs
AI总结：
本文介绍了R包evolSOM，利用自组织映射（SOMs）分析生物变量在物种或条件间的位移，揭示保守性和潜在基因驱动因素，成功应用于草叶表型分化研究。
摘要：
动机：揭示基因与性状之间的联系对于解决许多生物学难题至关重要。基因提供了构建细胞机制的指令，指导维持生命的各项过程。源自这些遗传指令的RNA分子和蛋白质在塑造细胞结构、影响反应和引导行为方面发挥着关键作用。这一基本生物学原理将基因组成与可观察性状联系起来，但从这种复杂的多模态数据中整合并提取有意义的关联是一项重大挑战。结果：我们介绍了evolSOM，一个新颖的R包，它利用自组织映射（SOMs）来探索和可视化生物变量的保守性，简化了表型和基因型属性的整合。通过构建捕获非冗余模式的物种特异性或条件特异性SOM，evolSOM允许分析生物变量在物种或条件间的位移。一起位移的变量表明它们属于同一个调控网络，位移的性质可能具有生物学意义。该包自动计算并以图形方式展示这些位移，实现高效比较并揭示保守和位移的变量。该包促进了多种表型数据类型的整合，从而能够探索观察到的表型变化背后的潜在基因驱动因素。其用户友好的界面和可视化功能增强了复杂网络分析的可及性。作为示例，我们使用evolSOM研究了基因和表型性状的位移，成功识别了草叶表型分化的潜在驱动因素。可用性：该包是开源的，可在https://github.com/sanprochetto/evolSOM获取。
英文摘要：
Motivation: Unraveling the connection between genes and traits is crucial for solving many biological puzzles. Genes provide instructions for building cellular machinery, directing the processes that sustain life. RNA molecules and proteins, derived from these genetic instructions, play crucial roles in shaping cell structures, influencing reactions, and guiding behavior. This fundamental biological principle links genetic makeup to observable traits, but integrating and extracting meaningful relationships from this complex, multimodal data presents a significant challenge. Results: We introduce evolSOM, a novel R package that utilizes Self-Organizing Maps (SOMs) to explore and visualize the conservation of biological variables, easing the integration of phenotypic and genotypic attributes. By constructing species-specific or condition-specific SOMs that capture non-redundant patterns, evolSOM allows the analysis of displacement of biological variables between species or conditions. Variables displaced together suggest membership in the same regulatory network, and the nature of the displacement may hold biological significance. The package automatically calculates and graphically presents these displacements, enabling efficient comparison and revealing conserved and displaced variables. The package facilitates the integration of diverse phenotypic data types, enabling the exploration of potential gene drivers underlying observed phenotypic changes. Its user-friendly interface and visualization capabilities enhance the accessibility of complex network analyses. Illustratively, we employed evolSOM to study the displacement of genes and phenotypic traits, successfully identifying potential drivers of phenotypic differentiation in grass leaves. Availability: The package is open-source and is available at https://github.com/sanprochetto/evolSOM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
