In its fourth year, the CASMI 2016 contest was organized to evaluate current chemical structure identification strategies for 19 natural products using high-resolution LC-MS and LC-MS/MS challenge datasets using automated methods with or without the combination of other tools. These natural products originate from plants, fungi, marine sponges, algae, or micro-algae. Every compound annotation workflow must start with determination of elemental compositions. Of these 19 challenges, one was excluded by the organizers after submission. For the remaining 18 challenges, three software programs were used. MS-FINDER version 1.62 was able to correctly identify 89% of the molecular formulas using an internal database that comprised of 13 metabolomics repositories with 45,181 formulas. SIRIUS correctly identified 61% compositions using PubChem formulas and Seven Golden Rules correctly identified 83% by using the Dictionary of Natural Products as a targeted database. Next, we performed structural dereplication for which we used the consensus formula from the three software programs. We submitted two solution sets for these challenges. In the first solution set, avaniya001, we only used the internal MS-FINDER functions for predicting and ranking structures, correctly identifying 53% of the structures as top-hit, 72% within the top-3 structures, and 78% within the top-10 hits. For our second set, avaniya002, we used both MS-FINDER predictions as well as MS/MS queries against the commercial NIST 14, METLIN, and the public MassBank of North America libraries. Here we correctly identified 78% of the structures as top-hit and 83% within the top-3 hits. Three challenge spectra remained unidentified in either of our submissions within the top-10 hits.

中文翻译：

---

使用MS-FINDER在CASMI 2016竞赛中识别19种天然产品。

在第四届CASMI 2016竞赛中，使用高分辨率LC-MS和LC-MS / MS挑战数据集（使用自动方法，结合或不结合其他工具），对19种天然产品的当前化学结构鉴定策略进行了评估。这些天然产物来自植物，真菌，海洋海绵，藻类或微藻。每个化合物注释工作流程都必须从确定元素组成开始。在这19个挑战中，组织者在提交后排除了一个挑战。对于其余的18个挑战，使用了三个软件程序。MS-FINDER版本1.62能够使用内部数据库正确识别89％的分子式，该内部数据库包含13个代谢组学资料库和45,181个分子式。SIRIUS使用PubChem公式正确识别了61％的成分，而七个黄金法则通过使用天然产物词典作为目标数据库正确识别了83％。接下来，我们执行了结构重复，使用了三个软件程序中的共识公式。针对这些挑战，我们提交了两种解决方案。在第一个解决方案集中avaniya001中，我们仅使用内部MS-FINDER函数预测结构并对其进行排名，正确地将53％的结构确定为最高命中率，将72％的结构确定为前3个结构以及将78％的结构确定为顶部-10次 对于第二组avaniya002，我们同时使用了MS-FINDER预测以及针对商业NIST 14，METLIN和北美洲MassBank公共图书馆的MS / MS查询。在这里，我们正确地确定了78％的结构为最高命中，而83％的结构则位于前3名命中。在前十名的命中率中，我们提交的任何一份报告都没有确定三个挑战谱。