New tools and resources in metabolomics: 2016–2017

Misra, Biswapriya B.

doi:10.1002/elps.201700441

Cited by 57 publications

(37 citation statements)

References 118 publications

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“…A recent review enlists genome-based systems biology tools and applications available for network analysis, pathway construction, genome alignments, assemblies, tree viewers and phylogenies, microarray and RNA-Seq viewers, genome browsers, visualization tools for comparative genomics, and tools for building visual prototypes (Pavlopoulos et al 2015). Similarly, tools, resources, databases and software for analysis and visualization of proteomics (Oveland et al 2015) and metabolomics data (Misra & van der Hooft 2016, Misra et al 2017, Misra 2018) are reviewed on a yearly basis. However, none of these recent publications provide a comprehensive overview of approaches for integrating three or more omics datasets.…”

Section: Introductionmentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

332

214

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With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

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Section: Introductionmentioning

confidence: 99%

Integrated omics: tools, advances and future approaches

Misra

Langefeld

Olivier

et al. 2019

Journal of Molecular Endocrinology

332

214

View full text Add to dashboard Cite

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“…The actual statistics and computation are much too complicated to be described in this review. The interested reader can refer to various brilliant reviews available on these aspects …”

Section: Metabolomics Versus Other ‘Omics’mentioning

confidence: 99%

“…Machine learning is a promising approach to evaluate such data. A review is available for the update on technical aspects of metabolomics over the last 2 years …”

Section: Role Of Metabolomics Studies In Rheumatologymentioning

confidence: 99%

Emerging role of metabolomics in rheumatology

2018

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The pursuit for understanding disease pathogenesis, in this age of rapid laboratory diagnostics and fast-paced research, has led scientists worldwide to take recourse in hypothesis-free approaches for molecular diagnosis. Metabolomics is one such powerful tool that explores comprehensibly the metabolic alternations in human diseases. It involves study of small molecules of less than 1 kD in size by either LSMS or nuclear magnetic resonance. Unlike genomics, which tells us what may have happened, metabolomics reflects what did happen. The NMR technique has an advantage of analyzing metabolites without sample preparation, thereby diminishing artifacts, is less cumbersome and with the latest database on Metabolome; about 30 000 metabolites can be identified. The study of metabolomics for several rheumatic diseases, including rheumatoid arthritis, lupus, osteoarthritis and vasculitis, has revealed distinctive metabolic signatures. Thus, metabolomics is a technique that promises precision medicine with better biomarkers, robust predictors of drug response and of disease outcome, discovery of newer metabolites and pathways in disease pathogenesis, and finally, targeted drug development. This review intends to decipher its relevance in common rheumatic diseases.

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“… provided a list of freely available open source software tools for metabolomics analysis, and their database is online and continues to be updated—a list that is classified and searchable—the updates do not come with a description and do not describe the usefulness of the tools. In this effort, we have summarized the software and tools available from every year starting 2015 onward . Building on the previously established review structure , this overview of major tools and resources in metabolomics is organized into the following 11 sections: (1) NMR tools, (2) MS tools, (3) tools for isotope techniques, (4) data preprocessing tools, (5) annotation tools, (6) multifunctional tools, (7) lipidomics tools, (8) tools for pathway and network analysis, (9) statistical tools, (10) tools for omics integration, and (11) miscellaneous tools of interest.…”

Section: Introductionmentioning

confidence: 99%

“…In this effort, we have summarized the software and tools available from every year starting 2015 onward . Building on the previously established review structure , this overview of major tools and resources in metabolomics is organized into the following 11 sections: (1) NMR tools, (2) MS tools, (3) tools for isotope techniques, (4) data preprocessing tools, (5) annotation tools, (6) multifunctional tools, (7) lipidomics tools, (8) tools for pathway and network analysis, (9) statistical tools, (10) tools for omics integration, and (11) miscellaneous tools of interest. Table shows a summary of all the reviewed tools and resources.…”

Section: Introductionmentioning

confidence: 99%

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

Misra¹,

Mohapatra

2018

Electrophoresis

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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.

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New tools and resources in metabolomics: 2016–2017

Cited by 57 publications

References 118 publications

Integrated omics: tools, advances and future approaches

Integrated omics: tools, advances and future approaches

Emerging role of metabolomics in rheumatology

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

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