Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single‐tiered software application where the analytics tasks cannot be distributed, limiting the scalability and reproducibility of the data analysis. In this paper the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis, are summarized. The combination of software containers with workflows environments for large‐scale metabolomics and proteomics analysis is discussed. Finally, a new approach for reproducible and large‐scale data analysis based on BioContainers and two of the most popular workflow environments, Galaxy and Nextflow, is introduced to the proteomics and metabolomics communities.     

Plant metabolomics: large-scale phytochemistry in the functional genomics era

Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.     

Fungal metabolite analysis in genomics and phenomics

Metabolomics consists of strategies to quantitatively identify cellular metabolites and to understand how trafficking of these biochemical messengers through the metabolic network influences phenotype. The application of metabolomics to fungi has been strongly pursued because these organisms are widely used for the production of chemicals, are well known for their diverse metabolic landscape and serve as excellent eukaryotic model organisms for studying metabolism and systems biology. Within the context of fungal systems, recent progress has been made in the development of analytical tools and mathematical strategies used in metabolite analysis that have enhanced our ability to crack the code underpinning the cellular inventory, regulatory schemes and communication mechanisms that dictate cellular function. Metabolomics has played a key role in functional genomics and strain classification.     

Plant metabolomics: from holistic hope, to hype, to hot topic

In a short time, plant metabolomics has gone from being just an ambitious concept to being a rapidly growing, valuable technology applied in the stride to gain a more global picture of the molecular organization of multicellular organisms. The combination of improved analytical capabilities with newly designed, dedicated statistical, bioinformatics and data mining strategies, is beginning to broaden the horizons of our understanding of how plants are organized and how metabolism is both controlled but highly flexible. Metabolomics is predicted to play a significant, if not indispensable role in bridging the phenotype-genotype gap and thus in assisting us in our desire for full genome sequence annotation as part of the quest to link gene to function. Plants are a fabulously rich source of diverse functional biochemicals and metabolomics is also already proving valuable in an applied context. By creating unique opportunities for us to interrogate plant systems and characterize their biochemical composition, metabolomics will greatly assist in identifying and defining much of the still unexploited biodiversity available today.     

Current progress in computational metabolomics

Being a relatively new addition to the 'omics' field, metabolomics is still evolving its own computational infrastructure and assessing its own computational needs. Due to its strong emphasis on chemical information and because of the importance of linking that chemical data to biological consequences, metabolomics must combine elements of traditional bioinformatics with traditional cheminformatics. This is a significant challenge as these two fields have evolved quite separately and require very different computational tools and skill sets. This review is intended to familiarize readers with the field of metabolomics and to outline the needs, the challenges and the recent progress being made in four areas of computational metabolomics: (i) metabolomics databases; (ii) metabolomics LIMS; (iii) spectral analysis tools for metabolomics and (iv) metabolic modeling.     

代谢组学及其在微藻研究中的应用

代谢组学以完整的生物体为研究对象,运用合适的分析测试手段检测靶向或非靶向代谢物,结合统计模型进行分析解释。随着微藻研究的深入,微藻与代谢组学结合探究分子作用机理的研究日益增多。本文总结代谢组学的发展概况、研究流程及常用分析技术特点和代谢组学在微藻领域的研究进展,展望代谢组学在微藻研究的应用前景与发展趋势,并提出实际应用中所面临的困难与挑战。     

The MetabolomeExpress Project: enabling web-based processing,analysis and transparent dissemination of GC/MS metabolomics datasets

Background  

Standardization of analytical approaches and reporting methods via community-wide collaboration can work synergistically with web-tool development to result in rapid community-driven expansion of online data repositories suitable for data mining and meta-analysis. In metabolomics, the inter-laboratory reproducibility of gas-chromatography/mass-spectrometry (GC/MS) makes it an obvious target for such development. While a number of web-tools offer access to datasets and/or tools for raw data processing and statistical analysis, none of these systems are currently set up to act as a public repository by easily accepting, processing and presenting publicly submitted GC/MS metabolomics datasets for public re-analysis.     

CDK-Taverna: an open workflow environment for cheminformatics

Background  

Small molecules are of increasing interest for bioinformatics in areas such as metabolomics and drug discovery. The recent release of large open access chemistry databases generates a demand for flexible tools to process them and discover new knowledge. To freely support open science based on these data resources, it is desirable for the processing tools to be open source and available for everyone.     

Reflections on univariate and multivariate analysis of metabolomics data

Metabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like the t test, analysis of variance, principal component analysis, and partial least squares discriminant analysis constitute the backbone of the statistical part of the vast majority of metabolomics papers, it seems that many basic but rather fundamental questions are still often asked, like: Why do the results of univariate and multivariate analyses differ? Why apply univariate methods if you have already applied a multivariate method? Why if I do not see something univariately I see something multivariately? In the present paper we address some aspects of univariate and multivariate analysis, with the scope of clarifying in simple terms the main differences between the two approaches. Applications of the t test, analysis of variance, principal component analysis and partial least squares discriminant analysis will be shown on both real and simulated metabolomics data examples to provide an overview on fundamental aspects of univariate and multivariate methods.     

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided.     

Metabolomics for phytomedicine research and drug development

Metabolomics, including both targeted and global metabolite profiling strategies, is fast becoming the approach of choice across a broad range of sciences including systems biology, drug discovery, molecular and cell biology, and other medical and agricultural sciences. New analytical and bioinformatics technologies and techniques are continually being created or optimized, significantly increasing the crossdisciplinary capabilities of this new biology. The metabolomes of medicinal plants are particularly a valuable natural resource for the evidence-based development of new phytotherapeutics and nutraceuticals. Comparative metabolomics platforms are evolving into novel technologies for monitoring disease development, drug metabolism, and chemical toxicology. An efficient multidisciplinary marriage of these emerging metabolomics techniques with agricultural biotechnology will greatly benefit both basic and applied medical research.     

Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results

Informatics standards and controlled vocabularies are essentialfor allowing information technology to help exchange, manage,interpret and compare large data collections. In a rapidly evolvingfield, the challenge is to work out how best to describe, butnot prescribe, the use of these technologies and methods. AMetabolomics Standards Workshop was held by the US NationalInstitutes of Health (NIH) to bring together multiple ongoingstandards efforts in metabolomics with the NIH research community.The goals were to discuss metabolomics workflows (methods, technologiesand data treatments) and the needs, challenges and potentialapproaches to developing a Metabolomics Standards Initiativethat will help facilitate this rapidly growing field which hasbeen a focus of the NIH roadmap effort. This report highlightsspecific aspects of what was presented and discussed at the1st and 2nd August 2005 Metabolomics Standards Workshop.      

Quality control for plant metabolomics: reporting MSI-compliant studies

The Metabolomics Standards Initiative (MSI) has recently released documents describing minimum parameters for reporting metabolomics experiments, in order to validate metabolomic studies and to facilitate data exchange. The reporting parameters encompassed by MSI include the biological study design, sample preparation, data acquisition, data processing, data analysis and interpretation relative to the biological hypotheses being evaluated. Herein we exemplify how such metadata can be reported by using a small case study – the metabolite profiling by GC-TOF mass spectrometry of Arabidopsis thaliana leaves from a knockout allele of the gene At1g08510 in the Wassilewskija ecotype. Pitfalls in quality control are highlighted that can invalidate results even if MSI reporting standards are fulfilled, including reliable compound identification and integration of unknown metabolites. Standardized data processing methods are proposed for consistent data storage and dissemination via databases.     

Navigating freely-available software tools for metabolomics analysis

Introduction

The field of metabolomics has expanded greatly over the past two decades, both as an experimental science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools.

Objectives

To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics.

Methods

The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC–MS, GC–MS or NMR) and the functionality (i.e. pre- and post-processing, statistical analysis, workflow and other functions) they are designed for.

Results

A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/MetabolomicsTools which is classified and searchable via a simple controlled vocabulary.

Conclusion

This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct comparison of tools’ abilities to perform specific data analysis tasks e.g. peak picking.

     

MetaboLights: towards a new COSMOS of metabolomics data management

Exciting funding initiatives are emerging in Europe and the US for metabolomics data production, storage, dissemination and analysis. This is based on a rich ecosystem of resources around the world, which has been build during the past ten years, including but not limited to resources such as MassBank in Japan and the Human Metabolome Database in Canada. Now, the European Bioinformatics Institute has launched MetaboLights, a database for metabolomics experiments and the associated metadata (http://www.ebi.ac.uk/metabolights). It is the first comprehensive, cross-species, cross-platform metabolomics database maintained by one of the major open access data providers in molecular biology. In October, the European COSMOS consortium will start its work on Metabolomics data standardization, publication and dissemination workflows. The NIH in the US is establishing 6?C8 metabolomics services cores as well as a national metabolomics repository. This communication reports about MetaboLights as a new resource for Metabolomics research, summarises the related developments and outlines how they may consolidate the knowledge management in this third large omics field next to proteomics and genomics.