Automated assembly of species metabolomes through data submission into a public repository

Digital Transformation in Materials Science: A Paradigm Change in Material's Development

The ongoing digitalization is rapidly changing and will further revolutionize all parts of life. This statement is currently omnipresent in the media as well as in the scientific community; however, the exact consequences of the proceeding digitalization for the field of materials science in general and the way research will be performed in the future are still unclear. There are first promising examples featuring the potential to change discovery and development approaches toward new materials. Nevertheless, a wide range of open questions have to be solved in order to enable the so-called digital-supported material research. The current state-of-the-art, the present and future challenges, as well as the resulting perspectives for materials science are described.

Modeling Meets Metabolomics-The WormJam Consensus Model as Basis for Metabolic Studies in the Model Organism Caenorhabditis elegans

Metabolism is one of the attributes of life and supplies energy and building blocks to organisms. Therefore, understanding metabolism is crucial for the understanding of complex biological phenomena. Despite having been in the focus of research for centuries, our picture of metabolism is still incomplete. Metabolomics, the systematic analysis of all small molecules in a biological system, aims to close this gap. In order to facilitate such investigations a blueprint of the metabolic network is required. Recently, several metabolic network reconstructions for the model organism Caenorhabditis elegans have been published, each having unique features. We have established the WormJam Community to merge and reconcile these (and other unpublished models) into a single consensus metabolic reconstruction. In a series of workshops and annotation seminars this model was refined with manual correction of incorrect assignments, metabolite structure and identifier curation as well as addition of new pathways. The WormJam consensus metabolic reconstruction represents a rich data source not only for in silico network-based approaches like flux balance analysis, but also for metabolomics, as it includes a database of metabolites present in C. elegans, which can be used for annotation. Here we present the process of model merging, correction and curation and give a detailed overview of the model. In the future it is intended to expand the model toward different tissues and put special emphasizes on lipid metabolism and secondary metabolism including ascaroside metabolism in accordance to their central role in C. elegans physiology.

Are microbiome studies ready for hypothesis-driven research?

Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita. We argue that an investment of community resources in infrastructure tasks, and in the controls and standards that underpin them, will greatly enhance the investment in hypothesis-driven research programs.

Caveat Usor: Assessing Differences between Major Chemistry Databases

The three databases of PubChem, ChemSpider, and UniChem capture the majority of open chemical structure records with February 2018 totals of 95, 63, and 154 million, respectively. Collectively, they constitute a massively enabling resource for cheminformatics, chemical biology, and drug discovery. As meta-portals, they subsume and link out to the major proportion of public bioactivity data extracted from the literature and screening center assay results. Therefore, they not only present three different entry points, but the many subsumed independent resources present a fourth entry point in the form of standalone databases. Because this creates a complex picture it is important for users to have at least some appreciation of differential content to enable utility judgments for the tasks at hand. This turns out to be challenging. By comparing the three resources in detail, this review assesses their differences, some of which are not obvious. This includes the fact that coverage is significantly different between the 587, 282, and 38 contributing sources, respectively. This not only presents the "who-has-what" question, but also the reason "why" any particular inclusion is considered valuable is rarely made explicit. Also confusing is that sources nominally in common (i.e., having the same submitter name) can have significantly different structure counts, not only in each of the three but also from their standalone instantiations. Assessing a series of examples indicates that differences in loading dates and structural standardization are the main causes of this inter-portal discordance.

ESI-LC-MS based-metabolomics data of mangosteen (Garcinia mangostana Linn.) fruit pericarp, aril and seed at different ripening stages

Fruit ripening is a complex phenomenon involving a series of biochemical, physiological and organoleptic changes. Ripening process in mangosteen (Garcinia mangostana Linn.) is unique of which the fruit will only ripen properly if harvested during its middle stage (emergence of purple/pink colour) but not earlier (green stage). The knowledge on the molecular mechanism and regulation behind this phenomenon is still limited. Hence, electrospray ionization liquid chromatography mass spectrometry (ESI-LC-MS) based metabolomics analysis was applied to determine the metabolome of mangosteen ripening. Specifically, mangosteen pericarp, aril and seed were collected at four different ripening stages (stage 0: green, stage 2: yellowish with pink patches, stage 4: brownish red and stage 6: dark purple) and subjected to metabolite profiling analysis. The data provided in this article have been deposited to the EMBL-EBI MetaboLights database (DOI: 10.1093/nar/gks1004. PubMed PMID: 23109552) with the identifier MTBLS595. The complete dataset can be accessed here https://www.ebi.ac.uk/metabolights/MTBLS595.