MetDAT: a modular and workflow-based free online pipeline for mass spectrometry data processing, analysis and interpretation

Biswas, Ambarish; Mynampati, Kalyan C.; Umashankar, Shivshankar; Reuben, Sheela; Parab, Gauri; Rao, Raghuraj; Kannan, Velayutham S.; Swarup, Sanjay

doi:10.1093/bioinformatics/btq436

Cited by 32 publications

(11 citation statements)

References 9 publications

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“…These online pipelines include the MetaboAnalyst 28, the Metabolomics Workbench 47, the MetaDB 48, the MetDAT 49, the MSPrep 50, the Workflow4Metabolomics 51 and the XCMS online 52. Based on a comprehensive review, the number of normalization algorithms provided by the above pipelines varies significantly from 2 (the Workflow4Metabolomics ) to 13 (the MetaboAnalyst ).…”

mentioning

confidence: 99%

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

Tang

Yang

et al. 2016

Sci Rep

137

124

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In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

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mentioning

confidence: 99%

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

Tang

Yang

et al. 2016

Sci Rep

137

124

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“…Several general purpose platforms for managing MS data have been developed [33][34][35][36]. In [33], a system based on the integration of different platforms for standalone applications is presented to compare healthy and diseased patients by using a statistical based approach based on the Wilcoxon-Mann-Whitney (WNM) algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…The graphical composition of workflows involves an ontology-based algorithm. Biswas et al [36] describes an integrated platform for preprocessing and mining MS data. The tools provided by that platform are: (i) preprocessing and data preparation, including peak identification, (ii) database querying for comparing the detected peaks to ones stored in publicly available databases (i.e., expressions of known proteins), and (iii) MS clustering and classification.…”

Section: Related Workmentioning

confidence: 99%

A time series approach for clustering mass spectrometry data

Gullo

Ponti

Tagarelli

et al. 2012

Journal of Computational Science

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“…There has been research effort directed towards this end and various applications have been developed for addressing a range of data types [3][4][5][6][7][8]. However most of those approaches adhere to a narrow focus dictated by discipline-specific requirements.…”

Section: Related Workmentioning

confidence: 99%

OmicsMiner: A Biological Data Mining Framework

Zhou

Netzer

Lee

et al. 2012

J Data Mining in Genom Proteomics

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OmicsMiner is a computational platform providing systematic access to state-of-the-art data processing and mining methods with the goal of facilitating the design of customized pipelines for processing a diverse range of biological data sets. Many built-in methods are provided for preprocessing, feature selection, clustering and classification of complex datasets. The platform supports convenient integration of additional algorithms that can further expand its functionality. OmicsMiner also provides convenient and concise interactive graphical user interfaces for data processing. OmicsMiner is a Java program that is platform-independent and does not require installation. It is available at http://www.bcf.ku.edu/ software.shtml Journal of Data Mining in Genomics & Proteomics J o u rn al of Da ta M in in g in Gen o m ic s & Proteom ic s

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MetDAT: a modular and workflow-based free online pipeline for mass spectrometry data processing, analysis and interpretation

Abstract: sanjay@nus.edu.sg

Cited by 32 publications

References 9 publications

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

A time series approach for clustering mass spectrometry data

OmicsMiner: A Biological Data Mining Framework

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