metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets.
metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets.
复制标题
DOI：
10.1021/acs.analchem.0c03693
复制
发表时间：
2021-03-30
影响因子：
7.4
通讯作者：
Karnovsky, Alla
中科院分区：
文献类型：
--
作者：
Habra, Hani;Kachman, Maureen;Bullock, Kevin;Clish, Clary;Evans, Charles R.;Karnovsky, Alla
LC-HRMS实验检测了数千种化合物，其中只有一小部分在大多数研究中被鉴定出来。传统的数据处理管道包含一个对齐步骤，用于将样本中重叠特征的测量结果组装到统一的表中。然而，在不相同的条件下获得的数据集不适合这个过程，主要是由于色谱保留时间的显着变化。连续获得的LC-MS代谢组学数据之间的特征对齐可以帮助协作化合物鉴定工作，并实现扩展数据集的荟萃分析。在这里，我们描述了metabCombiner，一种新的计算管道，用于匹配一对非目标LC-MS数据集中的已知和未知特征，并将其丰度连接到交叉特征测量的组合表中。metabCombiner通过质荷比（m/z）值对特征进行分组以生成可能的特征对比对的搜索空间，通过一组选定的保留时间有序对拟合样条，并通过m/z、映射的保留时间和相对丰度相似性对比对进行排序。我们在用不同梯度洗脱方法获得的一对血浆代谢组学数据集上评估了该工作流程，实现了约0.06 min的平均绝对保留时间预测误差和约90%的加权每化合物匹配准确度。我们进一步证明了这种方法的实用性，通过全面映射功能，尿液和肌肉代谢组学数据集从不同的实验室。metabCombiner有可能弥合不兼容的代谢组学数据集之间的差距，并且可以在https://github.com/hhabra/metabCombiner和Bioconductor上作为R包提供。
LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.
登录
查看更多内容
影响因子：
3.6
作者：
Martin, Jean-Charles;Maillot, Matthieu;Mazerolles, Gerard;Verdu, Alexandre;Lyan, Bernard;Migne, Carole;Defoort, Catherine;Canlet, Cecile;Junot, Christophe;Guillou, Claude;Manach, Claudine;Jabob, Daniel;Bouveresse, Delphine Jouan-Rimbaud;Paris, Estelle;Pujos-Guillot, Estelle;Jourdan, Fabien;Giacomoni, Franck;Courant, Frederique;Fave, Gaelle;Le Gall, Gwenaelle;Chassaigne, Hubert;Tabet, Jean-Claude;Martin, Jean-Francois;Antignac, Jean-Philippe;Shintu, Laetitia;Defernez, Marianne;Philo, Mark;Alexandre-Gouaubau, Marie-Cecile;Amiot-Carlin, Marie-Josephe;Bossis, Mathilde;Triba, Mohamed N.;Stojilkovic, Natali;Banzet, Nathalie;Molinie, Roland;Bott, Romain;Goulitquer, Sophie;Caldarelli, Stefano;Rutledge, Douglas N.
通讯作者：
Rutledge, Douglas N.
影响因子：
5.7
作者：
Eilers, PHC;Marx, BD
通讯作者：
Marx, BD
影响因子：
2.5
作者：
BOGUSZ, M;ADERJAN, R
通讯作者：
ADERJAN, R
使用 iRT（一种标准化保留时间）可以更有针对性地测量肽。
影响因子：
3.4
作者：
Escher, Claudia;Reiter, Lukas;MacLean, Brendan;Ossola, Reto;Herzog, Franz;Chilton, John;MacCoss, Michael J.;Rinner, Oliver
通讯作者：
Rinner, Oliver
DOI：
10.1016/1044-0305(94)87009-8
发表时间：
1994-09-01
影响因子：
3.2
作者：
STEIN, SE;SCOTT, DR
通讯作者：
SCOTT, DR