{"title":"液相色谱-质谱分析中的工作流程自动化","authors":"R. Gentz, H. Martín, Edward Baidoo, S. Peisert","doi":"10.1109/eScience.2019.00095","DOIUrl":null,"url":null,"abstract":"We describe the fully automated workflow path developed for the ingest and analysis of liquid chromatography mass spectrometry (LCMS) data. With the help of this computational workflow, we were able to replace two human work days to analyze data with two hours of unsupervised computation time. In addition, this tool also can compute confidence intervals for all its results, based on the noise level present in the data. We leverage only open source tools and libraries in this workflow.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Workflow Automation in Liquid Chromatography Mass Spectrometry\",\"authors\":\"R. Gentz, H. Martín, Edward Baidoo, S. Peisert\",\"doi\":\"10.1109/eScience.2019.00095\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe the fully automated workflow path developed for the ingest and analysis of liquid chromatography mass spectrometry (LCMS) data. With the help of this computational workflow, we were able to replace two human work days to analyze data with two hours of unsupervised computation time. In addition, this tool also can compute confidence intervals for all its results, based on the noise level present in the data. We leverage only open source tools and libraries in this workflow.\",\"PeriodicalId\":142614,\"journal\":{\"name\":\"2019 15th International Conference on eScience (eScience)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 15th International Conference on eScience (eScience)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/eScience.2019.00095\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 15th International Conference on eScience (eScience)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/eScience.2019.00095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Workflow Automation in Liquid Chromatography Mass Spectrometry
We describe the fully automated workflow path developed for the ingest and analysis of liquid chromatography mass spectrometry (LCMS) data. With the help of this computational workflow, we were able to replace two human work days to analyze data with two hours of unsupervised computation time. In addition, this tool also can compute confidence intervals for all its results, based on the noise level present in the data. We leverage only open source tools and libraries in this workflow.
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