Literature DB >> 30601938

MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery.

Xiaotao Shen1,2, Zheng-Jiang Zhu1.   

Abstract

SUMMARY: Mass spectrometry-based metabolomics aims to profile the metabolic changes in biological systems and identify differential metabolites related to physiological phenotypes and aberrant activities. However, many confounding factors during data acquisition complicate metabolomics data, which is characterized by high dimensionality, uncertain degrees of missing and zero values, nonlinearity, unwanted variations and non-normality. Therefore, prior to differential metabolite discovery analysis, various types of data cleaning such as batch alignment, missing value imputation, data normalization and scaling are essentially required for data post-processing. Here, we developed an interactive web server, namely, MetFlow, to provide an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery.
AVAILABILITY AND IMPLEMENTATION: The MetFlow is freely available on http://metflow.zhulab.cn/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Mesh:

Year:  2019        PMID: 30601938     DOI: 10.1093/bioinformatics/bty1066

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

Review 1.  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

2.  Identification of adulteration in botanical samples with untargeted metabolomics.

Authors:  E Diane Wallace; Daniel A Todd; James M Harnly; Nadja B Cech; Joshua J Kellogg
Journal:  Anal Bioanal Chem       Date:  2020-04-29       Impact factor: 4.142

3.  Botanical metabolite ions extraction from full electrospray ionization mass spectrometry using high-dimensional penalized regression.

Authors:  Bety Rostandy; Xiaoli Gao
Journal:  Metabolomics       Date:  2019-10-04       Impact factor: 4.290

4.  Predictive Modeling for Metabolomics Data.

Authors:  Tusharkanti Ghosh; Weiming Zhang; Debashis Ghosh; Katerina Kechris
Journal:  Methods Mol Biol       Date:  2020

Review 5.  Optimization of metabolomic data processing using NOREVA.

Authors:  Jianbo Fu; Ying Zhang; Yunxia Wang; Hongning Zhang; Jin Liu; Jing Tang; Qingxia Yang; Huaicheng Sun; Wenqi Qiu; Yinghui Ma; Zhaorong Li; Mingyue Zheng; Feng Zhu
Journal:  Nat Protoc       Date:  2021-12-24       Impact factor: 13.491

6.  NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.

Authors:  Qingxia Yang; Yunxia Wang; Ying Zhang; Fengcheng Li; Weiqi Xia; Ying Zhou; Yunqing Qiu; Honglin Li; Feng Zhu
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

7.  A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies.

Authors:  Qingxia Yang; Jiajun Hong; Yi Li; Weiwei Xue; Song Li; Hui Yang; Feng Zhu
Journal:  Brief Bioinform       Date:  2020-12-01       Impact factor: 11.622

8.  Identification of Dose-Dependent DNA Damage and Repair Responses From Subchronic Exposure to 1,4-Dioxane in Mice Using a Systems Analysis Approach.

Authors:  Georgia Charkoftaki; Jaya Prakash Golla; Alvaro Santos-Neto; David J Orlicky; Rolando Garcia-Milian; Ying Chen; Nicholas J W Rattray; Yuping Cai; Yewei Wang; Colin T Shearn; Varvara Mironova; Yensheng Wang; Caroline H Johnson; David C Thompson; Vasilis Vasiliou
Journal:  Toxicol Sci       Date:  2021-09-28       Impact factor: 4.849

9.  Elucidating the Antimycobacterial Mechanism of Action of Ciprofloxacin Using Metabolomics.

Authors:  Kirsten E Knoll; Zander Lindeque; Adetomiwa A Adeniji; Carel B Oosthuizen; Namrita Lall; Du Toit Loots
Journal:  Microorganisms       Date:  2021-05-28

10.  gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites.

Authors:  Fernando Perez-Sanz; Victoria Ruiz-Hernández; Marta I Terry; Sara Arce-Gallego; Julia Weiss; Pedro J Navarro; Marcos Egea-Cortines
Journal:  Metabolites       Date:  2021-03-31
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