Ribosomal DNA, tri- and bi-partite pericentromeres in the permanent translocation heterozygote Rhoeo spathacea

High- and low-stringency FISH and base-specific fluorescence were performed on the permanent translocation heterozygote Rhoeo spathacea (2n = 12). Our results indicate that 45S rDNA arrays, rDNA-related sequences and other GC-rich DNA fraction(s) are located within the pericentromeric regions of all twelve chromosomes, usually colocalizing with the chromomycin A3-positive bands. Homogenization of the pericentromeric regions appears to result from the concerted spread of GC-rich sequences, with differential amplification likely. We found new 5S rDNA patterns, which suggest a variability in the breakpoints and in the consequent chromosome reorganizations. It was found that the large 5S rDNA locus residing on each of the 8E and 9E arms consisted of two smaller loci. On each of the two chromosome arms 3b and 4b, in addition to the major subtelomeric 5S rDNA locus, a new minor locus was found interstitially about 40% along the arm length. The arrangement of cytotogenetic landmarks and chromosome arm measurements are discussed with regard to genome repatterning in Rhoeo

Knowledge Graph Completion to Predict Polypharmacy Side Effects

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.Comment: 13th International Conference on Data Integration in the Life Sciences (DILS2018

STITCH 4: integration of protein-chemical interactions with user data

STITCH is a database of protein-chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions. Available at http://stitch.embl.de, the resulting interaction network includes 390 000 chemicals and 3.6 million proteins from 1133 organisms. Compared with the previous version, the number of high-confidence protein-chemical interactions in human has increased by 45%, to 367 000. In this version, we added features for users to upload their own data to STITCH in the form of internal identifiers, chemical structures or quantitative data. For example, a user can now upload a spreadsheet with screening hits to easily check which interactions are already known. To increase the coverage of STITCH, we expanded the text mining to include full-text articles and added a prediction method based on chemical structures. We further changed our scheme for transferring interactions between species to rely on orthology rather than protein similarity. This improves the performance within protein families, where scores are now transferred only to orthologous proteins, but not to paralogous proteins. STITCH can be accessed with a web-interface, an API and downloadable files

FACIL: Fast and Accurate Genetic Code Inference and Logo

Motivation: The intensification of DNA sequencing will increasingly unveil uncharacterized species with potential alternative genetic codes. A total of 0.65% of the DNA sequences currently in Genbank encode their proteins with a variant genetic code, and these exceptions occur in many unrelated taxa. Results: We introduce FACIL (Fast and Accurate genetic Code Inference and Logo), a fast and reliable tool to evaluate nucleic acid sequences for their genetic code that detects alternative codes even in species distantly related to known organisms. To illustrate this, we apply FACIL to a set of mitochondrial genomic contigs of Globobulimina pseudospinescens. This foraminifer does not have any sequenced close relative in the databases, yet we infer its alternative genetic code with high confidence values. Results are intuitively visualized in a Genetic Code Logo

STITCH 3: zooming in on protein–chemical interactions

To facilitate the study of interactions between proteins and chemicals, we have created STITCH, an aggregated database of interactions connecting over 300 000 chemicals and 2.6 million proteins from 1133 organisms. Compared to the previous version, the number of chemicals with interactions and the number of high-confidence interactions both increase 4-fold. The database can be accessed interactively through a web interface, displaying interactions in an integrated network view. It is also available for computational studies through downloadable files and an API. As an extension in the current version, we offer the option to switch between two levels of detail, namely whether stereoisomers of a given compound are shown as a merged entity or as separate entities. Separate display of stereoisomers is necessary, for example, for carbohydrates and chiral drugs. Combining the isomers increases the coverage, as interaction databases and publications found through text mining will often refer to compounds without specifying the stereoisomer. The database is accessible at http://stitch.embl.de/

Stretching and twisting of the DNA duplexes in coarse grained dynamical models

Three coarse-grained models of the double-stranded DNA are proposed and compared in the context of mechanical manipulation such as twisting and various schemes of stretching. The models differ in the number of effective beads (between two and five) representing each nucleotide. They all show similar behavior and, in particular, lead to a torque-force phase diagrams qualitatively consistent with experiments and all-atom simulations

STRING v10: protein-protein interaction networks, integrated over the tree of life

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein-protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/

<i>Schizosaccharomyces pombe</i> Pol II transcription elongation factor ELL functions as part of a rudimentary super elongation complex

ELL family transcription factors activate the overall rate of RNA polymerase II (Pol II) transcription elongation by binding directly to Pol II and suppressing its tendency to pause. In metazoa, ELL regulates Pol II transcription elongation as part of a large multisubunit complex referred to as the Super Elongation Complex (SEC), which includes P-TEFb and EAF, AF9 or ENL, and an AFF family protein. Although orthologs of ELL and EAF have been identified in lower eukaryotes including Schizosaccharomyces pombe, it has been unclear whether SEClike complexes function in lower eukaryotes. In this report, we describe isolation from S. pombe of an ELL-containing complex with features of a rudimentary SEC. This complex includes S. pombe Ell1, Eaf1, and a previously uncharacterized protein we designate Ell1 binding protein 1 (Ebp1), which is distantly related to metazoan AFF family members. Like the metazoan SEC, this S. pombe ELL complex appears to function broadly in Pol II transcription. Interestingly, it appears to have a particularly important role in regulating genes involved in cell separation

A strategy to incorporate prior knowledge into correlation network cutoff selection

Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization