Is there a correlation between aberrant embryonic crown-rump length growth velocities and subsequent birth weights?

In this study, we tested the hypothesis that anomalous first trimester growth affects birth weight. Four hundred and fifteen women with viable singleton pregnancies at the primary transvaginal scan who had at least two crown rump length (CRL) and birth weight data were included. A linear mixed model was fitted to the Box-Cox transformed CRL values to evaluate the association between the GA and the embryonic growth. For multivariate analysis we included maternal age, height, weight, parity, number of miscarriages, vaginal bleeding, smoking, foetal gender, birth weight, small-for-gestation (SGA) and large-for gestation (LGA) categories at delivery. Smoking appeared to be significant for predicting the initial CRL from the beginning of the pregnancy (p value = 0.013). The SGA foetuses appeared to have slightly slower embryonic growth rates compared to non-SGA (p value = 0.045), after taking into account the effect of smoking on the initial CRL. None of the other variables including subsequent birth weight or LGA category have statistically significant effect on the first trimester embryonic growth curve when tested separately.

Prediction of subsequent miscarriage risk in women who present with a viable pregnancy at the first early pregnancy scan.

OBJECTIVES: To generate and evaluate a new prediction model for miscarriage in women who present with a viable intrauterine pregnancy (IUP) at the primary early pregnancy scan and to compare this new model to a previously published model. MATERIALS AND METHODS: Data were collected prospectively from women presenting to the early pregnancy unit with a viable IUP between November 2006 and January 2013. More than 30 historical, clinical and ultrasonographic variables were recorded on a standardised datasheet at the first visit. Women were followed until the final outcome was known at the end of the first trimester: viable IUP or miscarriage. A new multinomial logistic regression model was developed retrospectively on training cases and tested prospectively on test cases. The performance of the new prediction model was evaluated using receiver operating characteristic (ROC) curves and compared to a previously published model. After removing cases with missing values for the model of Oates, the area under the ROC curve (AUC) was also calculated for the new model and the Oates model. RESULTS: A total of 1115 consecutive first-trimester women presented to the early pregnancy unit. Eight hundred and sixty-two women with a viable IUP at the first scan whose outcome was known at the end of the first trimester were included in the final analysis. Five hundred and sixty-six women were included in the training set and 296 in the test set. 92.1% were viable and 7.9% had miscarried at the end of the first trimester. The most significant independent prognostic variables for the logistic regression model were as follows: maternal age, embryonic heart rate (EHR), logarithm [gestational sac (GS) volume/crown-rump length (CRL)], CRL and the presence or absence of clots per vagina (PV) at presentation. The performance of the new model compared with the Oates model gave an AUC of 0.870 vs 0.847 for the training set and 0.783 vs 0.744 for the test set. After removing cases with missing values for the model of Oates 2013, the performance of the new model compared to the Oates model gave an AUC of 0.887 vs 0.861 for the training set and 0.816 vs 0.734 for the test set (P-value <0.04). CONCLUSIONS: We have developed a new prediction model which indicates the likelihood of miscarriage. In women who present with a viable IUP at the primary scan, advancing maternal age in the presence of clots PV increases the probability of subsequent miscarriage. Whereas, in women with a higher EHR in the presence of an increased GS volume/CRL ratio, the likelihood of subsequent miscarriage is reduced. This new model outperforms the previously published model developed in our unit.

Mouse model phenotypes provide information about human drug targets.

MOTIVATION: Methods for computational drug target identification use information from diverse information sources to predict or prioritize drug targets for known drugs. One set of resources that has been relatively neglected for drug repurposing is animal model phenotype. RESULTS: We investigate the use of mouse model phenotypes for drug target identification. To achieve this goal, we first integrate mouse model phenotypes and drug effects, and then systematically compare the phenotypic similarity between mouse models and drug effect profiles. We find a high similarity between phenotypes resulting from loss-of-function mutations and drug effects resulting from the inhibition of a protein through a drug action, and demonstrate how this approach can be used to suggest candidate drug targets. AVAILABILITY AND IMPLEMENTATION: Analysis code and supplementary data files are available on the project Web site at https://drugeffects.googlecode.com.

Analyzing gene expression data in mice with the Neuro Behavior Ontology.

We have applied the Neuro Behavior Ontology (NBO), an ontology for the annotation of behavioral gene functions and behavioral phenotypes, to the annotation of more than 1,000 genes in the mouse that are known to play a role in behavior. These annotations can be explored by researchers interested in genes involved in particular behaviors and used computationally to provide insights into the behavioral phenotypes resulting from differences in gene expression. We developed the OntoFUNC tool and have applied it to enrichment analyses over the NBO to provide high-level behavioral interpretations of gene expression datasets. The resulting increase in the number of gene annotations facilitates the identification of behavioral or neurologic processes by assisting the formulation of hypotheses about the relationships between gene, processes, and phenotypic manifestations resulting from behavioral observations.

Systematic analysis of experimental phenotype data reveals gene functions.

High-throughput phenotyping projects in model organisms have the potential to improve our understanding of gene functions and their role in living organisms. We have developed a computational, knowledge-based approach to automatically infer gene functions from phenotypic manifestations and applied this approach to yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), zebrafish (Danio rerio), fruitfly (Drosophila melanogaster) and mouse (Mus musculus) phenotypes. Our approach is based on the assumption that, if a mutation in a gene [Formula: see text] leads to a phenotypic abnormality in a process [Formula: see text], then [Formula: see text] must have been involved in [Formula: see text], either directly or indirectly. We systematically analyze recorded phenotypes in animal models using the formal definitions created for phenotype ontologies. We evaluate the validity of the inferred functions manually and by demonstrating a significant improvement in predicting genetic interactions and protein-protein interactions based on functional similarity. Our knowledge-based approach is generally applicable to phenotypes recorded in model organism databases, including phenotypes from large-scale, high throughput community projects whose primary mode of dissemination is direct publication on-line rather than in the literature.

Practical applications of metabolomics in plant biology.

The technologies being developed for the large-scale, essentially unbiased analysis of the small molecules present in organic extracts made from plant materials are greatly changing our way of thinking about what is possible in plant biology. A range of different separation and detection techniques are being refined and expanded and their combination with advanced data management and data analysis approaches is already giving plant scientists far deeper insights into the complexity of plant metabolism and plant metabolic composition than was imaginable just a few years ago. This field of "metabolomics", while still in its infancy, has nevertheless already been welcomed with open arms by the plant science community, partly because of these said advantages but also because of the broad potential applicability of the approaches in both fundamental and applied science. The diversity in application already ranges from understanding the considerable complexity of primary metabolic networks in Arabidopsis, to the changes which occur in the biochemical composition of foods occurring, for example, during the Pasteurization of tomato purée for long-term storage or the boiling of Basmati rice for direct consumption. The insights being gained are revealing valuable information on the strict control yet flexible nature of plant metabolic networks in many different systems. This volume aims to give a comprehensive overview of the approaches available for the performance of a "typical" plant metabolomics experiment, the choice of analytical techniques and to offer warnings on the potential pitfalls in experimental design and execution.

A strategy for selecting data mining techniques in metabolomics.

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.

Meeting Report from the Second "Minimum Information for Biological and Biomedical Investigations" (MIBBI) workshop.

This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-Institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://mibbi.org/.

The first RSBI (ISA-TAB) workshop: "can a simple format work for complex studies?".

This article summarizes the motivation for, and the proceedings of, the first ISA-TAB workshop held December 6-8, 2007, at the EBI, Cambridge, UK. This exploratory workshop, organized by members of the Microarray Gene Expression Data (MGED) Society's Reporting Structure for Biological Investigations (RSBI) working group, brought together a group of developers of a range of collaborative systems to discuss the use of a common format to address the pressing need of reporting and communicating data and metadata from biological, biomedical, and environmental studies employing combinations of genomics, transcriptomics, proteomics, and metabolomics technologies along with more conventional methodologies. The expertise of the participants comprised database development, data management, and hands-on experience in the development of data communication standards. The workshop's outcomes are set to help formalize the proposed Investigation, Study, Assay (ISA)-TAB tab-delimited format for representing and communicating experimental metadata. This article is part of the special issue of OMICS on the activities of the Genomics Standards Consortium (GSC).

The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics.

The Functional Genomics Experiment data model (FuGE) has been developed to facilitate convergence of data standards for high-throughput, comprehensive analyses in biology. FuGE models the components of an experimental activity that are common across different technologies, including protocols, samples and data. FuGE provides a foundation for describing entire laboratory workflows and for the development of new data formats. The Microarray Gene Expression Data society and the Proteomics Standards Initiative have committed to using FuGE as the basis for defining their respective standards, and other standards groups, including the Metabolomics Standards Initiative, are evaluating FuGE in their development efforts. Adoption of FuGE by multiple standards bodies will enable uniform reporting of common parts of functional genomics workflows, simplify data-integration efforts and ease the burden on researchers seeking to fulfill multiple minimum reporting requirements. Such advances are important for transparent data management and mining in functional genomics and systems biology.

Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI).

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.

Establishing reporting standards for metabolomic and metabonomic studies: a call for participation.

Metabolite concentrations in cellular systems are very much dependent on the physiological, environmental, and genetic status of an organism and are regarded as the ultimate result of cellular regulation, resulting in the visible phenotypes. Therefore, the comprehensive analysis of metabolite levels and fluxes renders a suitable tool for assessing the degree of perturbation in biological systems. Lessons derived from development of other OMICS areas (genomics, proteomics, and transcriptomics) have shown that large-scale comparisons and interpretations will require the re-use of data over long periods of time and by multiple laboratories with different expertise and backgrounds. Reaching this goal will require standardization of reporting structures of metabolomic studies for journal publication purposes, for regulatory deposition, and for database dissemination. An initiative by the Metabolomics Society is presented that aims to define important aspects of metabolomic workflows. These include biological study designs, chemical analysis, and data processing, as well as the ontologies that are necessary in this framework.

MeMo: a hybrid SQL/XML approach to metabolomic data management for functional genomics.

BACKGROUND: The genome sequencing projects have shown our limited knowledge regarding gene function, e.g. S. cerevisiae has 5-6,000 genes of which nearly 1,000 have an uncertain function. Their gross influence on the behaviour of the cell can be observed using large-scale metabolomic studies. The metabolomic data produced need to be structured and annotated in a machine-usable form to facilitate the exploration of the hidden links between the genes and their functions. DESCRIPTION: MeMo is a formal model for representing metabolomic data and the associated metadata. Two predominant platforms (SQL and XML) are used to encode the model. MeMo has been implemented as a relational database using a hybrid approach combining the advantages of the two technologies. It represents a practical solution for handling the sheer volume and complexity of the metabolomic data effectively and efficiently. The MeMo model and the associated software are available at http://dbkgroup.org/memo/. CONCLUSION: The maturity of relational database technology is used to support efficient data processing. The scalability and self-descriptiveness of XML are used to simplify the relational schema and facilitate the extensibility of the model necessitated by the creation of new experimental techniques. Special consideration is given to data integration issues as part of the systems biology agenda. MeMo has been physically integrated and cross-linked to related metabolomic and genomic databases. Semantic integration with other relevant databases has been supported through ontological annotation. Compatibility with other data formats is supported by automatic conversion.

Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops.

There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome "fingerprinting" to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.

Toward supportive data collection tools for plant metabolomics.

Over recent years, a number of initiatives have proposed standard reporting guidelines for functional genomics experiments. Associated with these are data models that may be used as the basis of the design of software tools that store and transmit experiment data in standard formats. Central to the success of such data handling tools is their usability. Successful data handling tools are expected to yield benefits in time saving and in quality assurance. Here, we describe the collection of datasets that conform to the recently proposed data model for plant metabolomics known as ArMet (architecture for metabolomics) and illustrate a number of approaches to robust data collection that have been developed in collaboration between software engineers and biologists. These examples also serve to validate ArMet from the data collection perspective by demonstrating that a range of software tools, supporting data recording and data upload to central databases, can be built using the data model as the basis of their design.

A proposed framework for the description of plant metabolomics experiments and their results.

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.