Development of a High-Density Genetic Map for Muscadine Grape Using a Mapping Population from Selfing of the Perfect-Flowered Vine 'Dixie'.

Intraspecific diversity of the immune grape Muscadinia rotundifolia Michaux. can serve as a rich source of valuable resistance loci to the most widespread pathogens and pests of grapevine. While only one Run1/Rpg1 resistance locus has been introgressed from M. rotundifolia to the Vitis vinifera gene pool, a number of other genes conferring resistance to powdery mildew and downy mildew have been identified in various Muscadinia cultivars. A larger introduction of Muscadinia varieties to the European continent would greatly facilitate experiments of interspecific crosses as well as stimulate biotechnological efforts to overcome the main barrier to F1 fertility caused by the differences in chromosome number. For the successful introduction of Muscadinia into the new European environment, it is necessary to overcome the difficulties associated with the physiological characteristics of the species, such as insufficient cold tolerance and very late fruit ripening. To facilitate the further discovery of valuable loci in Muscadinia and their transfer to grapevine breeding programs, we constructed a high-density linkage map using an S1 mapping population obtained from the self-pollination of M. rotundifolia cv. Dixie maintained on the southern coast of Crimea. Using ddRADseq, 3730 SNPs were ordered across 20 linkage groups spanning 2753.6 cM of the total map length. No segregation in resistance to diseases and pests was observed among the 'Dixie' S1 population, suggesting the presence of homozygous non-segregating resistant loci in the genetic background of 'Dixie'. Markers with high segregation distortion showed a bias towards chromosomal intervals on linkage groups 10 and 20, where loci affecting the survival of 'Dixie' S1 progeny may be localized. QTLs with significant additive and dominance effects were discovered on LG14 and LG18, affecting the morphological traits associated with the vigor of growth and adaptability of young Muscadinia vines in the conditions of Crimea.

The Assessment of Agrobiological and Disease Resistance Traits of Grapevine Hybrid Populations (Vitis vinifera L. × Muscadinia rotundifolia Michx.) in the Climatic Conditions of Crimea.

The Crimean autochthonous grape varieties are unique by their origin and serve as a valuable source for breeding new cultivars with increased salt and frost resistance, as well as high-quality berries. However, they suffer from fungal pathogens, as the dry and hot summer months contribute to the epiphytotic course of diseases. An increase in the resistance of Crimean grape varieties is currently achieved through interspecific hybridization. In this study, we describe the genetic and agrobiological diversity of three hybrid populations obtained using the Vitis interspecific hybrid 'Magarach 31-77-10' as a female parent and Muscadinia rotundifolia × Vitis vinifera BC5 hybrid plants as male parents. The hybrid nature of the populations was assessed using RADseq high-throughput genotyping. We discovered 12,734 SNPs, which were common to all three hybrid populations. We also proved with the SSR markers that the strong powdery and downy mildew resistance of the paternal genotypes is determined by the dominant Run1/Rpv1 locus inherited from M. rotundifolia. As a result, the disease development score (R, %) for both mildew diseases in the female parent 'Magarach 31-77-10' was three times higher than in male parents 2000-305-143 and 2000-305-163 over two years of phytopathological assessment. The highest values of yield-contributing traits (average bunch weight ~197 g and 1.3 kg as yield per plant) were detected in the population 4-11 (♀M. No. 31-77-10 × 2000-305-163). Despite the epiphytotic development of PM, the spread of oidium to the vegetative organs of hybrids 4-11 did not exceed 20%. Some hybrid genotypes with high productivity and resistance to pathogens were selected for further assessment as promising candidates for new varieties.

CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations.

Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.

UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase.

MOTIVATION: The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge. RESULTS: In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020_01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB. AVAILABILITY AND IMPLEMENTATION: UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire.

Oxfold: kinetic folding of RNA using stochastic context-free grammars and evolutionary information.

MOTIVATION: Many computational methods for RNA secondary structure prediction, and, in particular, for the prediction of a consensus structure of an alignment of RNA sequences, have been developed. Most methods, however, ignore biophysical factors, such as the kinetics of RNA folding; no current implementation considers both evolutionary information and folding kinetics, thus losing information that, when considered, might lead to better predictions. RESULTS: We present an iterative algorithm, Oxfold, in the framework of stochastic context-free grammars, that emulates the kinetics of RNA folding in a simplified way, in combination with a molecular evolution model. This method improves considerably on existing grammatical models that do not consider folding kinetics. Additionally, the model compares favourably to non-kinetic thermodynamic models.

A metabolic entropy approach for measurements of systemic metabolic disruptions in patho-physiological States.

Multicellular organisms maintain the stability of their internal environment using metabolic and physiological regulatory mechanisms that are disrupted during disease. The loss of homeostatic control results in a complex set of disordered states that may lead to metabolic network failure and irreversible system damage. We have applied a new statistical entropy-based approach to quantify temporal systemic disorder (divergence of metabolic responses) in experimental patho-physiological states, via NMR-spectroscopy generated metabolic profiles of urine. A recovery (R-) potential metric has also been developed to evaluate the relative extent to which defined metabolic processes are perturbed in the context of a global system in terms of multiple changes in concentrations of biofluid components accompanying the disrupted functional activity. This approach is sensitive to physiological as well as pathological interventions. We show that global disruptions of metabolic processes, lesion reversibility, and disorder in metabolic responses to a stressor can be visualized via metabolic entropy metrics, giving insights into biological robustness and thus providing a new tool for assessing deviation from homeostatic regulation.

Recursive segment-wise peak alignment of biological (1)h NMR spectra for improved metabolic biomarker recovery.

Chemical shift variation in small-molecule (1)H NMR signals of biofluids complicates biomarker information recovery in metabonomic studies when using multivariate statistical and pattern recognition tools. Current peak realignment methods are generally time-consuming or align major peaks at the expense of minor peak shift accuracy. We present a novel recursive segment-wise peak alignment (RSPA) method to reduce variability in peak positions across the multiple (1)H NMR spectra used in metabonomic studies. The method refines a segmentation of reference and test spectra in a top-down fashion, sequentially subdividing the initial larger segments, as required, to improve the local spectral alignment. We also describe a general procedure that allows robust comparison of realignment quality of various available methods for a range of peak intensities. The RSPA method is illustrated with respect to 140 (1)H NMR rat urine spectra from a caloric restriction study and is compared with several other widely used peak alignment methods. We demonstrate the superior performance of the RSPA alignment over a wide range of peaks and its capacity to enhance interpretability and robustness of multivariate statistical tools. The approach is widely applicable for NMR-based metabolic studies and is potentially suitable for many other types of data sets such as chromatographic profiles and MS data.