CEA, LIST, Laboratory for Data Analysis and Systems' Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France.
CEA, DRF/JOLIOT/SPI, Laboratoire d'Etude du Métabolisme des Médicaments, MetaboHUB, Gif-Sur-Yvette F-91191, France.
Bioinformatics. 2017 Dec 1;33(23):3767-3775. doi: 10.1093/bioinformatics/btx458.
Flow Injection Analysis coupled to High-Resolution Mass Spectrometry (FIA-HRMS) is a promising approach for high-throughput metabolomics. FIA-HRMS data, however, cannot be preprocessed with current software tools which rely on liquid chromatography separation, or handle low resolution data only.
We thus developed the proFIA package, which implements a suite of innovative algorithms to preprocess FIA-HRMS raw files, and generates the table of peak intensities. The workflow consists of 3 steps: (i) noise estimation, peak detection and quantification, (ii) peak grouping across samples and (iii) missing value imputation. In addition, we have implemented a new indicator to quantify the potential alteration of the feature peak shape due to matrix effect. The preprocessing is fast (less than 15 s per file), and the value of the main parameters (ppm and dmz) can be easily inferred from the mass resolution of the instrument. Application to two metabolomics datasets (including spiked serum samples) showed high precision (96%) and recall (98%) compared with manual integration. These results demonstrate that proFIA achieves very efficient and robust detection and quantification of FIA-HRMS data, and opens new opportunities for high-throughput phenotyping.
The proFIA software (as well as the plasFIA dataset) is available as an R package on the Bioconductor repository (http://bioconductor.org/packages/proFIA), and as a Galaxy module on the Main Toolshed (https://toolshed.g2.bx.psu.edu), and on the Workflow4Metabolomics online infrastructure (http://workflow4metabolomics.org).
alexis.delabriere@cea.fr or etienne.thevenot@cea.fr.
Supplementary data are available at Bioinformatics online.
流动注射分析与高分辨率质谱联用（FIA-HRMS）是一种高通量代谢组学的有前途的方法。然而，FIA-HRMS 数据不能使用当前依赖于液相色谱分离的软件工具进行预处理，也不能处理低分辨率数据。
因此，我们开发了 proFIA 软件包，该软件包实现了一系列创新算法，用于预处理 FIA-HRMS 原始文件，并生成峰强度表。工作流程包括 3 个步骤：（i）噪声估计、峰检测和定量，（ii）跨样品的峰分组，（iii）缺失值插补。此外，我们还实现了一个新的指标来量化由于基质效应而导致特征峰形状潜在改变的程度。预处理速度很快（每个文件不到 15 秒），并且主要参数（ppm 和 dmz）的值可以从仪器的质量分辨率轻松推断出来。应用于两个代谢组学数据集（包括加标血清样本）的结果表明，与手动积分相比，具有较高的精度（96%）和召回率（98%）。这些结果表明，proFIA 实现了 FIA-HRMS 数据的高效、稳健检测和定量，为高通量表型分析开辟了新的机会。
proFIA 软件（以及 plasFIA 数据集）作为 R 包可在 Bioconductor 存储库（http://bioconductor.org/packages/proFIA）获得，也可以作为 Galaxy 模块在 Main Toolshed（https://toolshed.g2.bx.psu.edu）以及 Workflow4Metabolomics 在线基础设施（http://workflow4metabolomics.org）获得。
alexis.delabriere@cea.fr 或 etienne.thevenot@cea.fr。
补充数据可在《生物信息学》在线获取。
