Joanna C Wolthuis, Stefania Magnusdottir, Mia Pras-Raves, Maryam Moshiri, Judith J M Jans, Boudewijn Burgering, Saskia van Mil, Jeroen de Ridder
{"title":"MetaboShiny:基于质谱的代谢组学数据的交互分析和代谢物注释。","authors":"Joanna C Wolthuis, Stefania Magnusdottir, Mia Pras-Raves, Maryam Moshiri, Judith J M Jans, Boudewijn Burgering, Saskia van Mil, Jeroen de Ridder","doi":"10.1007/s11306-020-01717-8","DOIUrl":null,"url":null,"abstract":"<p><p>Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample's metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.</p>","PeriodicalId":144887,"journal":{"name":"Metabolomics : Official journal of the Metabolomic Society","volume":" ","pages":"99"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s11306-020-01717-8","citationCount":"10","resultStr":"{\"title\":\"MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data.\",\"authors\":\"Joanna C Wolthuis, Stefania Magnusdottir, Mia Pras-Raves, Maryam Moshiri, Judith J M Jans, Boudewijn Burgering, Saskia van Mil, Jeroen de Ridder\",\"doi\":\"10.1007/s11306-020-01717-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample's metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.</p>\",\"PeriodicalId\":144887,\"journal\":{\"name\":\"Metabolomics : Official journal of the Metabolomic Society\",\"volume\":\" \",\"pages\":\"99\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1007/s11306-020-01717-8\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metabolomics : Official journal of the Metabolomic Society\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11306-020-01717-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metabolomics : Official journal of the Metabolomic Society","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11306-020-01717-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MetaboShiny: interactive analysis and metabolite annotation of mass spectrometry-based metabolomics data.
Direct infusion untargeted mass spectrometry-based metabolomics allows for rapid insight into a sample's metabolic activity. However, analysis is often complicated by the large array of detected m/z values and the difficulty to prioritize important m/z and simultaneously annotate their putative identities. To address this challenge, we developed MetaboShiny, a novel R/RShiny-based metabolomics package featuring data analysis, database- and formula-prediction-based annotation and visualization. To demonstrate this, we reproduce and further explore a MetaboLights metabolomics bioinformatics study on lung cancer patient urine samples. MetaboShiny enables rapid and rigorous analysis and interpretation of direct infusion untargeted mass spectrometry-based metabolomics data.