Literature DB >> 30443919

Tools and resources for metabolomics research community: A 2017-2018 update.

Biswapriya B Misra1, Subhashree Mohapatra2.   

Abstract

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Data; Network; Software; Statistics; Visualization; Workflow

Mesh:

Year:  2018        PMID: 30443919     DOI: 10.1002/elps.201800428

Source DB:  PubMed          Journal:  Electrophoresis        ISSN: 0173-0835            Impact factor:   3.535


  9 in total

Review 1.  Nutritional Metabolomics in Cancer Epidemiology: Current Trends, Challenges, and Future Directions.

Authors:  Emma E McGee; Rama Kiblawi; Mary C Playdon; A Heather Eliassen
Journal:  Curr Nutr Rep       Date:  2019-09

Review 2.  Software tools, databases and resources in metabolomics: updates from 2018 to 2019.

Authors:  Keiron O'Shea; Biswapriya B Misra
Journal:  Metabolomics       Date:  2020-03-07       Impact factor: 4.290

Review 3.  Interpreting the lipidome: bioinformatic approaches to embrace the complexity.

Authors:  Jennifer E Kyle; Lucila Aimo; Alan J Bridge; Geremy Clair; Maria Fedorova; J Bernd Helms; Martijn R Molenaar; Zhixu Ni; Matej Orešič; Denise Slenter; Egon Willighagen; Bobbie-Jo M Webb-Robertson
Journal:  Metabolomics       Date:  2021-06-06       Impact factor: 4.290

4.  A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics.

Authors:  Nichole A Reisdorph; Scott Walmsley; Rick Reisdorph
Journal:  Metabolites       Date:  2019-12-21

5.  patRoon: open source software platform for environmental mass spectrometry based non-target screening.

Authors:  Rick Helmus; Thomas L Ter Laak; Annemarie P van Wezel; Pim de Voogt; Emma L Schymanski
Journal:  J Cheminform       Date:  2021-01-06       Impact factor: 5.514

6.  "notame": Workflow for Non-Targeted LC-MS Metabolic Profiling.

Authors:  Anton Klåvus; Marietta Kokla; Stefania Noerman; Ville M Koistinen; Marjo Tuomainen; Iman Zarei; Topi Meuronen; Merja R Häkkinen; Soile Rummukainen; Ambrin Farizah Babu; Taisa Sallinen; Olli Kärkkäinen; Jussi Paananen; David Broadhurst; Carl Brunius; Kati Hanhineva
Journal:  Metabolites       Date:  2020-03-31

Review 7.  Machine Learning Applications for Mass Spectrometry-Based Metabolomics.

Authors:  Ulf W Liebal; An N T Phan; Malvika Sudhakar; Karthik Raman; Lars M Blank
Journal:  Metabolites       Date:  2020-06-13

8.  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
Journal:  Metabolomics       Date:  2020-09-11       Impact factor: 4.290

Review 9.  Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity.

Authors:  Marina Creydt; Markus Fischer
Journal:  Molecules       Date:  2020-08-31       Impact factor: 4.411

  9 in total

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