背景:使用唯一标识符的生物数据标准化对于无缝数据集成至关重要,全面解读,和研究结果的可重复性,促进生物信息学和系统生物学的进步。尽管被广泛接受为通用标识符,生物物种的科学名称具有固有的局限性,包括缺乏稳定性,独特性,和可兑换,阻碍了它们在数据库中作为标识符的有效使用,特别是在天然产物(NP)发生数据库中,对利用这些有价值的数据进行大规模研究应用构成了重大障碍。
结果:为了应对这些挑战并促进涉及科学名称的生物学数据的高通量分析,我们开发了PhyloSophos,一个Python包,它考虑了科学名称和分类系统的属性,以将名称输入准确地映射到所选参考数据库中的条目。我们以NP发生数据库为例,说明了评估多个分类数据库并考虑基于分类语法的预处理的重要性,最终目标是将异构信息集成到一个单一的,统一数据集。
结论:我们预计PhyloSophos将大大有助于系统处理数字化和策划不良的生物数据,如生物多样性信息和民族药理学资源,利用这些宝贵的数据资源进行全面的生物信息学分析。
BACKGROUND: The standardization of biological data using unique identifiers is vital for seamless data integration, comprehensive interpretation, and reproducibility of research findings, contributing to advancements in bioinformatics and systems biology. Despite being widely accepted as a universal identifier, scientific names for biological species have inherent limitations, including lack of stability, uniqueness, and convertibility, hindering their effective use as identifiers in databases, particularly in natural product (NP) occurrence databases, posing a substantial obstacle to utilizing this valuable data for large-scale research applications.
RESULTS: To address these challenges and facilitate high-throughput analysis of biological data involving scientific names, we developed PhyloSophos, a Python package that considers the properties of scientific names and taxonomic systems to accurately map name inputs to entries within a chosen reference database. We illustrate the importance of assessing multiple taxonomic databases and considering taxonomic syntax-based pre-processing using NP occurrence databases as an example, with the ultimate goal of integrating heterogeneous information into a single, unified dataset.
CONCLUSIONS: We anticipate PhyloSophos to significantly aid in the systematic processing of poorly digitized and curated biological data, such as biodiversity information and ethnopharmacological resources, enabling full-scale bioinformatics analysis using these valuable data resources.