MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

GenomeView : a next-generation genome browser

Due to ongoing advances in sequencing technologies, billions of nucleotide sequences are now produced on a daily basis. A major challenge is to visualize these data for further downstream analysis. To this end, we present GenomeView, a stand-alone genome browser specifically designed to visualize and manipulate a multitude of genomics data. GenomeView enables users to dynamically browse high volumes of aligned short-read data, with dynamic navigation and semantic zooming, from the whole genome level to the single nucleotide. At the same time, the tool enables visualization of whole genome alignments of dozens of genomes relative to a reference sequence. GenomeView is unique in its capability to interactively handle huge data sets consisting of tens of aligned genomes, thousands of annotation features and millions of mapped short reads both as viewer and editor. GenomeView is freely available as an open source software package

Characterizing regulatory path motifs in integrated networks using perturbational data

Pathicular – a Cytoscape plugin for analysing cellular responses to transcription factor perturbations is presente

Diffany: an ontology-driven framework to infer, visualise and analyse differential molecular networks

Background: Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time. Results: In this work, we present a generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies. As an illustrative application, we demonstrate its usefulness on a plant abiotic stress study and we experimentally confirmed a predicted regulator. Availability: Diffany is freely available as open-source java library and Cytoscape plugin from http://bioinformatics.psb.ugent.be/supplementary_data/solan/diffany/

CyClus3D: a Cytoscape plugin for clustering network motifs in integrated networks

Network motifs in integrated molecular networks represent functional relationships between distinct data types. They aggregate to form dense topological structures corresponding to functional modules which cannot be detected by traditional graph clustering algorithms. We developed CyClus3D, a Cytoscape plugin for clustering composite three-node network motifs using a 3D spectral clustering algorithm

The Plant PTM Viewer, a central resource for exploring plant protein modifications

Posttranslational modifications (PTMs) of proteins are central in any kind of cellular signaling. Modern mass spectrometry technologies enable comprehensive identification and quantification of various PTMs. Given the increased numbers and types of mapped protein modifications, a database is necessary that simultaneously integrates and compares site‐specific information for different PTMs, especially in plants for which the available PTM data are poorly catalogued. Here, we present the Plant PTM Viewer (http://www.psb.ugent.be/PlantPTMViewer), an integrative PTM resource that comprises approximately 370,000 PTM sites for 19 types of protein modifications in plant proteins from five different species. The Plant PTM Viewer provides the user with a protein sequence overview in which the experimentally evidenced PTMs are highlighted together with an estimate of the confidence by which the modified peptides and, if possible, the actual modification sites were identified and with functional protein domains or active site residues. The PTM sequence search tool can query PTM combinations in specific protein sequences, whereas the PTM BLAST tool searches for modified protein sequences to detect conserved PTMs in homologous sequences. Taken together, these tools help to assume the role and potential interplay of PTMs in specific proteins or within a broader systems biology context. The Plant PTM Viewer is an open repository that allows the submission of mass spectrometry‐based PTM data to remain at pace with future PTM plant studies

Food Sources Contributing to Intake of Choline and Individual Choline Forms in a Norwegian Cohort of Patients With Stable Angina Pectoris

Choline is an essential nutrient involved in a wide range of physiological functions. It occurs in water- and lipid-soluble forms in the body and diet. Foods with a known high choline content are eggs, beef, chicken, milk, fish, and selected plant foods. An adequate intake has been set in the US and Europe, however, not yet in the Nordic countries. A higher intake of lipid-soluble choline forms has been associated with increased risk of acute myocardial infarction, highlighting the need for knowledge about food sources of the individual choline forms. In general, little is known about the habitual intake and food sources of choline, and individual choline forms.publishedVersio

The Association of Meat Intake With All-Cause Mortality and Acute Myocardial Infarction Is Age-Dependent in Patients With Stable Angina Pectoris

Background: Red and processed meat intake have been associated with increased risk of morbidity and mortality, and a restricted intake is encouraged in patients with cardiovascular disease. However, evidence on the association between total meat intake and clinical outcomes in this patient group is lacking. Objectives: To investigate the association between total meat intake and risk of all-cause mortality, acute myocardial infarction, cancer, and gastrointestinal cancer in patients with stable angina pectoris. We also investigated whether age modified these associations. Materials and Methods: This prospective cohort study consisted of 1,929 patients (80% male, mean age 62 years) with stable angina pectoris from the Western Norway B-Vitamin Intervention Trial. Dietary assessment was performed by the administration of a semi-quantitative food frequency questionnaire. Cox proportional hazards models were used to investigate the association between a relative increase in total meat intake and the outcomes of interest. Results: The association per 50 g/1,000 kcal higher intake of total meat with morbidity and mortality were generally inconclusive but indicated an increased risk of acute myocardial infarction [HR: 1.26 (95% CI: 0.98, 1.61)] and gastrointestinal cancer [1.23 (0.70, 2.16)]. However, we observed a clear effect modification by age, where total meat intake was associated with an increased risk of mortality and acute myocardial infarction among younger individuals, but an attenuation, and even reversal of the risk association with increasing age. Conclusion: Our findings support the current dietary guidelines emphasizing a restricted meat intake in cardiovascular disease patients but highlights the need for further research on the association between meat intake and health outcomes in elderly populations. Future studies should investigate different types of meat separately in other CVD-cohorts, in different age-groups, as well as in the general population.publishedVersio

Optimising orbit counting of arbitrary order by equation selection

BACKGROUND : Graphlets are useful for bioinformatics network analysis. Based on the structure of Hoˇcevar and Demšar’s ORCA algorithm, we have created an orbit counting algorithm, named Jesse. This algorithm, like ORCA, uses equations to count the orbits, but unlike ORCA it can count graphlets of any order. To do so, it generates the required internal structures and equations automatically. Many more redundant equations are generated, however, and Jesse’s running time is highly dependent on which of these equations are used. Therefore, this paper aims to investigate which equations are most efficient, and which factors have an effect on this efficiency. RESULTS : With appropriate equation selection, Jesse’s running time may be reduced by a factor of up to 2 in the best case, compared to using randomly selected equations. Which equations are most efficient depends on the density of the graph, but barely on the graph type. At low graph density, equations with terms in their right-hand side with few arguments are more efficient, whereas at high density, equations with terms with many arguments in the right-hand side are most efficient. At a density between 0.6 and 0.7, both types of equations are about equally efficient. CONCLUSION : Our Jesse algorithm became up to a factor 2 more efficient, by automatically selecting the best equations based on graph density. It was adapted into a Cytoscape App that is freely available from the Cytoscape App Store to ease application by bioinformaticians.Ghent University – imec and the European Union Seventh Framework Programme (FP7/2007-2013) – European Research Council Advanced Grant Agreement 322739-DOUBLEUP.https://bmcbioinformatics.biomedcentral.comam2020BiochemistryGeneticsMicrobiology and Plant Patholog