Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK.
Bioinformatics. 2018 Jun 15;34(12):2096-2102. doi: 10.1093/bioinformatics/bty080.
High-resolution mass spectrometry permits simultaneous detection of thousands of different metabolites in biological samples; however, their automated annotation still presents a challenge due to the limited number of tailored computational solutions freely available to the scientific community.
Here, we introduce ChemDistiller, a customizable engine that combines automated large-scale annotation of metabolites using tandem MS data with a compiled database containing tens of millions of compounds with pre-calculated 'fingerprints' and fragmentation patterns. Our tests using publicly and commercially available tandem MS spectra for reference compounds show retrievals rates comparable to or exceeding the ones obtainable by the current state-of-the-art solutions in the field while offering higher throughput, scalability and processing speed.
Source code freely available for download at https://bitbucket.org/iAnalytica/chemdistillerpython.
Supplementary data are available at Bioinformatics online.
高分辨率质谱允许同时检测生物样本中数千种不同的代谢物；然而，由于科学界可自由获得的定制计算解决方案数量有限，其自动化注释仍然是一个挑战。
在这里，我们介绍了 ChemDistiller，这是一个可定制的引擎，它将使用串联质谱数据对代谢物进行自动大规模注释与一个包含数千万种化合物的编译数据库相结合，这些化合物具有预先计算的“指纹”和碎片模式。我们使用公开和商业上可用的串联质谱谱图对参考化合物进行测试，结果表明，我们的检索率与该领域当前最先进的解决方案相当，甚至更高，同时提供更高的吞吐量、可扩展性和处理速度。
可在 https://bitbucket.org/iAnalytica/chemdistillerpython 下载源代码。
补充数据可在 Bioinformatics 在线获得。
