An Ultra-Fast Metabolite Prediction Algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.

小分子是所有生物过程的核心，而代谢组学(metabolomics)正成为日益重要的研究发现工具。稳健、精准且高效的实验方法，对于支撑和验证后基因组学研究的预测结果至关重要。为精准预测代谢变化与动态过程，实验设计需设置多组生物学重复，通常还需开展多种处理。每次质谱检测所得的质谱数据均需经过处理，并提取代谢物特征。受仪器分辨率限制及重复样本间的变异影响，同一代谢物在不同质谱数据中的保留时间和质荷比可能存在不同的数值。有效利用非靶向代谢组学数据的一项主要障碍，在于确保精准的质谱对齐，以实现跨质谱数据的特征（代谢物）精准识别。现有的对齐算法通常采用全局合并策略或局部合并策略：前者可实现精准对齐，但效率欠佳；后者虽运算速度快，但对齐结果往往精度不足。本研究报道了一种采用快速排序（quicksort）技术的新型对齐算法。基于模拟数据与真实数据的测试结果表明，该算法不仅大幅提升了对齐速度，同时也优化了对齐精度。