【摘要】 提出了一种基于¹³C同位素分布模型的多重液相色谱–质谱（LC–MS）实验数据的校准匹配方法,解决了肽链信号匹配不准确、覆盖率不高的问题。引入同位素分布模型对多次重复的LC–MS实验数据进行匹配校准。通过选取训练序列,生成同位素分布模型,并通过测试序列完成模型的测试。实验表明,结果匹配准确度达95%以上;多重实验数据覆盖率可达90%以上。同位素分布模型可以提升多次重复LC–MS实验数据中相关肽链信号匹配校准的准确性及覆盖率。更多还原

【Abstract】 An alignment and matching method based on isotope distribution model for multiple liquid chromatography–mass spectrometry(LC–MS) data was proposed to solve the problems of inaccurate and low coverage of peptide-matching. The isotope distribution model was introduced for matching and aligning the test datas of LC–MS. The model was generated by training sequence, and was tested by testing sequence. The results showed that the accuracy of matching was over 95%, and the coverage of multiple experimental datas could reach more than 90%. Isotope distribution model can improve the accuracy and coverage of peptide alignment in repeated LC–MS experiments.更多还原