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

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.

MetaboShiny – interactive processing, analysis and annotation of direct infusion metabolomics data

“…MetaboShiny performs multiple analysis steps and integrates many smaller tools into one toolbox. MetaboShiny should therefore be compared to toolboxes with a similar multi-level approach 5 . In this section we discuss four existing platforms and compare them to MetaboShiny.…”

“…A particularly pressing issue is that most current MS analysis tools are limited in the number of databases that are used for metabolite identification, thus limiting the number of metabolite identities that can be matched. For instance, many MS analysis tools rely on a custom built-in database, or a fixed integrated version of a publicly available database, with no opportunity to update to the most recent version 5 . Moreover, identification of the metabolite identity is only one step in the data analysis workflow and needs to be integrated into downstream analytics, such as data normalization, statistical testing, visualization, clustering or supervised machine learning.…”

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

“…In addition, while many tools have been developed specifically for plants, natural products, and industrial applications, these are not always applicable for human or translational studies. Therefore, this perspective article focuses on LC/MS-based clinical metabolomics and proposes a strategy to improve identification of compounds in these studies.Identifying compounds in LC/MS studies can be difficult and metabolomics researchers have developed an array of databases, spectral libraries, novel algorithms, and other tools to address the various challenges (for review, please see [8,9]). Generally speaking, compound annotation is conducted using metabolomics databases and/or libraries.…”

“…">Traditional Strategies in LC/MS-Based Metabolomics Compound IDIn general, the strategy used to perform compound ID proceeds, in its simplest form, as follows: obtaining and extracting LC/MS and/or LC/MS/MS data, searching of MS databases and/or MS/MS libraries, using additional tools to interpret MS/MS spectra, and finally using authentic standards to verify compound identities [4,12] (Figure 1). With the advent of new tools, this workflow has grown more complex and more informative, however, the basic steps are essentially the same [8,9]. Unfortunately, this process is often truncated where database and/or library searches fail to provide unambiguous evidence that an annotation is correct, making interpretation difficult.…”

“…Identifying compounds in LC/MS studies can be difficult and metabolomics researchers have developed an array of databases, spectral libraries, novel algorithms, and other tools to address the various challenges (for review, please see [8,9]). Generally speaking, compound annotation is conducted using metabolomics databases and/or libraries.…”