The metabolomics standards initiative (MSI)

In 2005, the Metabolomics Standards Initiative has been formed. An outline and general introduction is provided to inform about the history, structure, working plan and intentions of this initiative. Comments on any of the suggested minimal reporting standards are welcome to be sent to the open email list Msi-workgroups-feedback@lists.sourceforge.net     

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.     

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.     

Standard reporting requirements for biological samples in metabolomics experiments: microbial and in vitro biology experiments

With the increasing use of metabolomics as a means to study a large number of different biological research questions, there is a need for a minimal set of reporting standards that allow the scientific community to evaluate, understand, repeat, compare and re-investigate metabolomics studies. Here we propose, a first draft of minimal requirements to effectively describe the biological context of metabolomics studies that involve microbial or in vitro biological subjects. This recommendation has been produced by the microbiology and in vitro biology working subgroup of the Metabolomics Standards Initiative in collaboration with the yeast systems biology network as part of a wider standardization initiative led by the Metabolomics Society. Microbial and in vitro biology metabolomics is defined by this sub-working group as studies with any cell or organism that require a defined external medium to facilitate growth and propagation. Both a minimal set and a best practice set of reporting standards for metabolomics experiments have been defined. The minimal set of reporting standards for microbial or in vitro biology metabolomics experiments includes those factors that are specific for metabolomics experiments and that critically determine the outcome of the experiments. The best practice set of reporting standards contains both the factors that are specific for metabolomics experiments and general aspects that critically determine the outcome of any microbial or in vitro biological experiment.     

Whole genome transcriptome polymorphisms in Arabidopsis thaliana

ArrayPlex is a software package that centrally provides a large number of flexible toolsets useful for functional genomics, including microarray data storage, quality assessments, data visualization, gene annotation retrieval, statistical tests, genomic sequence retrieval and motif analysis. It uses a client-server architecture based on open source components, provides graphical, command-line, and programmatic access to all needed resources, and is extensible by virtue of a documented application programming interface. ArrayPlex is available at http://sourceforge.net/projects/arrayplex/.     

An ontology for a Robot Scientist

MOTIVATION: A Robot Scientist is a physically implemented robotic system that can automatically carry out cycles of scientific experimentation. We are commissioning a new Robot Scientist designed to investigate gene function in S. cerevisiae. This Robot Scientist will be capable of initiating >1,000 experiments, and making >200,000 observations a day. Robot Scientists provide a unique test bed for the development of methodologies for the curation and annotation of scientific experiments: because the experiments are conceived and executed automatically by computer, it is possible to completely capture and digitally curate all aspects of the scientific process. This new ability brings with it significant technical challenges. To meet these we apply an ontology driven approach to the representation of all the Robot Scientist's data and metadata. RESULTS: We demonstrate the utility of developing an ontology for our new Robot Scientist. This ontology is based on a general ontology of experiments. The ontology aids the curation and annotating of the experimental data and metadata, and the equipment metadata, and supports the design of database systems to hold the data and metadata. AVAILABILITY: EXPO in XML and OWL formats is at: http://sourceforge.net/projects/expo/. All materials about the Robot Scientist project are available at: http://www.aber.ac.uk/compsci/Research/bio/robotsci/.     

Mobster: accurate detection of mobile element insertions in next generation sequencing data

Mobile elements are major drivers in changing genomic architecture and can cause disease. The detection of mobile elements is hindered due to the low mappability of their highly repetitive sequences. We have developed an algorithm, called Mobster, to detect non-reference mobile element insertions in next generation sequencing data from both whole genome and whole exome studies. Mobster uses discordant read pairs and clipped reads in combination with consensus sequences of known active mobile elements. Mobster has a low false discovery rate and high recall rate for both L1 and Alu elements. Mobster is available at http://sourceforge.net/projects/mobster.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0488-x) contains supplementary material, which is available to authorized users.     

PHYSICO2: an UNIX based standalone procedure for computation of physicochemical,window-dependent and substitution based evolutionary properties of protein sequences along with automated block preparation tool,version 2

AvailabilityPHYSICO2: is freely available at http://sourceforge.net/projects/physico2/ along with its documentation at https://sourceforge.net/projects/physico2/files/Documentation.pdf/download for all users.     

PlantMetabolomics.org: A Web Portal for Plant Metabolomics Experiments

PlantMetabolomics.org (PM) is a web portal and database for exploring, visualizing, and downloading plant metabolomics data. Widespread public access to well-annotated metabolomics datasets is essential for establishing metabolomics as a functional genomics tool. PM integrates metabolomics data generated from different analytical platforms from multiple laboratories along with the key visualization tools such as ratio and error plots. Visualization tools can quickly show how one condition compares to another and which analytical platforms show the largest changes. The database tries to capture a complete annotation of the experiment metadata along with the metabolite abundance databased on the evolving Metabolomics Standards Initiative. PM can be used as a platform for deriving hypotheses by enabling metabolomic comparisons between genetically unique Arabidopsis (Arabidopsis thaliana) populations subjected to different environmental conditions. Each metabolite is linked to relevant experimental data and information from various annotation databases. The portal also provides detailed protocols and tutorials on conducting plant metabolomics experiments to promote metabolomics in the community. PM currently houses Arabidopsis metabolomics data generated by a consortium of laboratories utilizing metabolomics to help elucidate the functions of uncharacterized genes. PM is publicly available at http://www.plantmetabolomics.org.In the post genomics era, metabolomics is fast emerging as a vital source of information to aid in solving systems biology puzzles with an emphasis on metabolic solutions. Metabolomics is the science of measuring the pool sizes of metabolites (small molecules of M_r ≤ 1,000 D), which collectively define the metabolome of a biological sample (Fiehn et al., 2000; Hall et al., 2002). Coverage of the entire plant metabolome is a daunting task as it is estimated that there are over 200,000 different metabolites within the plant kingdom (Goodacre et al., 2004). Although technology is rapidly advancing, there are still large gaps in our knowledge of the plant metabolome.Despite this lack of complete knowledge and the immense metabolic diversity among plants, metabolomics has become a key analytical tool in the plant community (Hall et al., 2002). This has led to the emergence of multiple experimental and analytical platforms that collectively generate millions of metabolite data points. Because of this vast amount of data, the development of public databases to capture information from metabolomics experiments is vital to provide the scientific community with comprehensive knowledge about metabolite data generation, annotation, and integration with metabolic pathway data. Some examples of these public databases are given below. The Human Metabolome Project contains comprehensive data for more than 2,000 metabolites found within the human body (Wishart et al., 2007). The Golm Database is a repository that provides access to mass spectrometry (MS) libraries, metabolite profiling experiments, and related information from gas chromatography (GC)-MS experimental platforms, along with tools to integrate this information with other systems biology knowledge (Kopka et al., 2005). The Madison Metabolomics Consortium Database contains primarily NMR spectra for Arabidopsis (Arabidopsis thaliana) and features thorough NMR search tools (Cui et al., 2008). SetupX and Binbase provide a framework that combines MS data and biological metadata for steering laboratory work flows and employs automated metabolite annotation (Scholz and Fiehn, 2007).A single analytical technique cannot identify and quantify all the metabolites found in plants. Thus, PlantMetabolomics.org (PM) was developed to provide a portal for accessing publicly available MS-based plant metabolomics experimental results from multiple analytical and separation techniques. PM also follows the emerging metabolomics standards for experiment annotation. PM has extensive annotation links between the identified metabolites and metabolic pathways in AraCyc (Mueller et al., 2003) at The Arabidopsis Information Resource (Rhee et al., 2003) and the Plant Metabolic Network (www.plantcyc.org), the Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa et al., 2004), and MetNetDB (Wurtele et al., 2007).Standards for the annotation of metabolomics experiments are still under active development and the metadata types collected in PM are based on the recommendations of the Metabolomics Standards Initiative (MSI; Fiehn et al., 2007a) and the Minimal Information for a Metabolomic Experiment (Bino et al., 2004) standards. MSI attempts to capture the complete annotation of metabolomics experiments and includes metadata of the experiments along with the metabolite abundance data. The initial database schema design was guided by the schema proposed in the Architecture for Metabolomics project (Jenkins et al., 2004).