Automatically visualise and analyse data on pathways using PathVisioRPC from any programming environment

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium

Background

For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations.

Results

The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers.

Conclusions

In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2748-y) contains supplementary material, which is available to authorized users.

Perturbation of IIS/TOR signaling alters the landscape of sex-differential gene expression in Drosophila

Background

The core functions of the insulin/insulin-like signaling and target of rapamycin (IIS/TOR) pathway are nutrient sensing, energy homeostasis, growth, and regulation of stress responses. This pathway is also known to interact directly and indirectly with the sex determination regulatory hierarchy. The IIS/TOR pathway plays a role in directing sexually dimorphic traits, including dimorphism of growth, metabolism, stress and behavior. Previous studies of sexually dimorphic gene expression in the adult head, which includes both nervous system and endocrine tissues, have revealed variation in sex-differential expression, depending in part on genotype and environment. To understand the degree to which the environmentally responsive insulin signaling pathway contributes to sexual dimorphism of gene expression, we examined the effect of perturbation of the pathway on gene expression in male and female Drosophila heads.

Results

Our data reveal a large effect of insulin signaling on gene expression, with greater than 50% of genes examined changing expression. Males and females have a shared gene expression response to knock-down of InR function, with significant enrichment for pathways involved in metabolism. Perturbation of insulin signaling has a greater impact on gene expression in males, with more genes changing expression and with gene expression differences of larger magnitude. Primarily as a consequence of the response in males, we find that reduced insulin signaling results in a striking increase in sex-differential expression. This includes sex-differences in expression of immune, defense and stress response genes, genes involved in modulating reproductive behavior, genes linking insulin signaling and ageing, and in the insulin signaling pathway itself.

Conclusions

Our results demonstrate that perturbation of insulin signaling results in thousands of genes displaying sex differences in expression that are not differentially expressed in control conditions. Thus, insulin signaling may play a role in variability of somatic, sex-differential expression. The finding that perturbation of the IIS/TOR pathway results in an altered landscape of sex-differential expression suggests a role of insulin signaling in the physiological underpinnings of trade-offs, sexual conflict and sex differences in expression variability.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5308-3) contains supplementary material, which is available to authorized users.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.

Tandem Domains with Tuned Interactions Are a Powerful Biological Design Principle

Allosteric effects of mutations, ligand binding, or post-translational modifications on protein function occur through changes to the protein's shape, or conformation. In a cell, there are many copies of the same protein, all experiencing these perturbations in a dynamic fashion and fluctuating through different conformations and activity states. According to the "conformational selection and population shift" theory, ligand binding selects a particular conformation. This perturbs the ensemble and induces a population shift. In a new PLOS Biology paper, Melacini and colleagues describe a novel model of protein regulation, the "Double-Conformational Selection Model", which demonstrates how two tandem ligand-binding domains interact to regulate protein function. Here we explain how tandem domains with tuned interactions-but not single domains-can provide a blueprint for sensitive activation sensors within a narrow window of ligand concentration, thereby promoting signaling control.

WikiPathways: capturing the full diversity of pathway knowledge

WikiPathways (http://www.wikipathways.org) is an open, collaborative platform for capturing and disseminating models of biological pathways for data visualization and analysis. Since our last NAR update, 4 years ago, WikiPathways has experienced massive growth in content, which continues to be contributed by hundreds of individuals each year. New aspects of the diversity and depth of the collected pathways are described from the perspective of researchers interested in using pathway information in their studies. We provide updates on extensions and services to support pathway analysis and visualization via popular standalone tools, i.e. PathVisio and Cytoscape, web applications and common programming environments. We introduce the Quick Edit feature for pathway authors and curators, in addition to new means of publishing pathways and maintaining custom pathway collections to serve specific research topics and communities. In addition to the latest milestones in our pathway collection and curation effort, we also highlight the latest means to access the content as publishable figures, as standard data files, and as linked data, including bulk and programmatic access.