Department of Chemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA.
Morgridge Institute for Research, Madison, WI 53715, USA; Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI 53706, USA.
Cell Syst. 2018 May 23;6(5):621-625.e5. doi: 10.1016/j.cels.2018.03.011. Epub 2018 Apr 25.
State-of-the-art proteomics software routinely quantifies thousands of peptides per experiment with minimal need for manual validation or processing of data. For the emerging field of discovery lipidomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS), comparably mature informatics tools do not exist. Here, we introduce LipiDex, a freely available software suite that unifies and automates all stages of lipid identification, reducing hands-on processing time from hours to minutes for even the most expansive datasets. LipiDex utilizes flexible in silico fragmentation templates and lipid-optimized MS/MS spectral matching routines to confidently identify and track hundreds of lipid species and unknown compounds from diverse sample matrices. Unique spectral and chromatographic peak purity algorithms accurately quantify co-isolation and co-elution of isobaric lipids, generating identifications that match the structural resolution afforded by the LC-MS/MS experiment. During final data filtering, ionization artifacts are removed to significantly reduce dataset redundancy. LipiDex interfaces with several LC-MS/MS software packages, enabling robust lipid identification to be readily incorporated into pre-existing data workflows.
最先进的蛋白质组学软件通常可以在每个实验中定量数千种肽,而几乎不需要手动验证或处理数据。对于通过液相色谱-串联质谱 (LC-MS/MS) 进行新兴的发现脂质组学领域,不存在类似成熟的信息学工具。在这里,我们介绍了 LipiDex,这是一个免费的软件套件,它统一并自动化了脂质鉴定的所有阶段,即使对于最广泛的数据集,也将手动处理时间从数小时减少到数分钟。LipiDex 利用灵活的计算机模拟碎裂模板和针对脂质优化的 MS/MS 光谱匹配例程,从各种样本基质中自信地识别和跟踪数百种脂质种类和未知化合物。独特的光谱和色谱峰纯度算法可以准确地量化同分离和同洗脱的等质异位脂质,生成与 LC-MS/MS 实验提供的结构分辨率相匹配的鉴定结果。在最终的数据过滤过程中,去除了离子化伪影,从而大大减少了数据集的冗余。LipiDex 与多个 LC-MS/MS 软件包接口,能够轻松地将强大的脂质识别功能集成到现有的数据工作流程中。