A Complete and Recursive Feature Theory

Various feature descriptions are being employed in logic programming languages and constrained-based grammar formalisms. The common notational primitive of these descriptions are functional attributes called features. The descriptions considered in this paper are the possibly quantified first-order formulae obtained from a signature of binary and unary predicates called features and sorts, respectively. We establish a first-order theory FT by means of three axiom schemes, show its completeness, and construct three elementarily equivalent models. One of the models consists of so-called feature graphs, a data structure common in computational linguistics. The other two models consist of so-called feature trees, a record-like data structure generalizing the trees corresponding to first-order terms. Our completeness proof exhibits a terminating simplification system deciding validity and satisfiability of possibly quantified feature descriptions.Comment: Short version appeared in the 1992 Annual Meeting of the Association for Computational Linguistic

GraphProt: modeling binding preferences of RNA-binding proteins

We present GraphProt, a computational framework for learning sequence- and structure-binding preferences of RNA-binding proteins (RBPs) from high-throughput experimental data. We benchmark GraphProt, demonstrating that the modeled binding preferences conform to the literature, and showcase the biological relevance and two applications of GraphProt models. First, estimated binding affinities correlate with experimental measurements. Second, predicted Ago2 targets display higher levels of expression upon Ago2 knockdown, whereas control targets do not. Computational binding models, such as those provided by GraphProt, are essential for predicting RBP binding sites and affinities in all tissues. GraphProt is freely available at http://www.bioinf.uni-freiburg.de/Software/GraphProt

CRISPRstrand: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci

Motivation: The discovery of CRISPR-Cas systems almost 20 years ago rapidly changed our perception of the bacterial and archaeal immune systems. CRISPR loci consist of several repetitive DNA sequences called repeats, inter-spaced by stretches of variable length sequences called spacers. This CRISPR array is transcribed and processed into multiple mature RNA species (crRNAs). A single crRNA is integrated into an interference complex, together with CRISPR-associated (Cas) proteins, to bind and degrade invading nucleic acids. Although existing bioinformatics tools can recognize CRISPR loci by their characteristic repeat-spacer architecture, they generally output CRISPR arrays of ambiguous orientation and thus do not determine the strand from which crRNAs are processed. Knowledge of the correct orientation is crucial for many tasks, including the classification of CRISPR conservation, the detection of leader regions, the identification of target sites (protospacers) on invading genetic elements and the characterization of protospacer-adjacent motifs. Results: We present a fast and accurate tool to determine the crRNA-encoding strand at CRISPR loci by predicting the correct orientation of repeats based on an advanced machine learning approach. Both the repeat sequence and mutation information were encoded and processed by an efficient graph kernel to learn higher-order correlations. The model was trained and tested on curated data comprising >4500 CRISPRs and yielded a remarkable performance of 0.95 AUC ROC (area under the curve of the receiver operator characteristic). In addition, we show that accurate orientation information greatly improved detection of conserved repeat sequence families and structure motifs. We integrated CRISPRstrand predictions into our CRISPRmap web server of CRISPR conservation and updated the latter to version 2.0. Availability: CRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

The long noncoding RNA mimi scaffolds neuronal granules to maintain nervous system maturity

RNA binding proteins and messenger RNAs (mRNAs) assemble into ribonucleoprotein granules that regulate mRNA trafficking, local translation, and turnover. The dysregulation of RNA-protein condensation disturbs synaptic plas-ticity and neuron survival and has been widely associated with human neurological disease. Neuronal granules are thought to condense around particular proteins that dictate the identity and composition of each granule type. Here, we show in Drosophila that a previously uncharacterized long noncoding RNA, mimi, is required to scaffold large neuronal granules in the adult nervous system. Neuronal ELAV-like proteins directly bind mimi and mediate granule assembly, while Staufen maintains condensate integrity. mimi granules contain mRNAs and proteins involved in synaptic processes; granule loss in mimi mutant flies impairs nervous system maturity and neuropeptide-mediated signaling and causes phenotypes of neurodegeneration. Our work reports an architectural RNA for a neuronal granule and provides a handle to interrogate functions of a condensate independently of those of its constituent proteins

The PETfold and PETcofold web servers for intra- and intermolecular structures of multiple RNA sequences

The function of non-coding RNA genes largely depends on their secondary structure and the interaction with other molecules. Thus, an accurate prediction of secondary structure and RNA–RNA interaction is essential for the understanding of biological roles and pathways associated with a specific RNA gene. We present web servers to analyze multiple RNA sequences for common RNA structure and for RNA interaction sites. The web servers are based on the recent PET (Probabilistic Evolutionary and Thermodynamic) models PETfold and PETcofold, but add user friendly features ranging from a graphical layer to interactive usage of the predictors. Additionally, the web servers provide direct access to annotated RNA alignments, such as the Rfam 10.0 database and multiple alignments of 16 vertebrate genomes with human. The web servers are freely available at: http://rth.dk/resources/petfold

Beta reduction constraints

The constraint language for lambda structures (CLLS) can model lambda terms that are known only partially. In this paper, we introduce beta reduction constraints to describe beta reduction steps between partially known lambda terms. We show that beta reduction constraints can be expressed in an extension of CLLS by group parallelism. We then extend a known semi-decision procedure for CLLS to also deal with group parallelism and thus with beta-reduction constraints

Characterization of the zinc finger μ-protein HVO_0758 from Haloferax volcanii: biological roles, zinc binding, and NMR solution structure

It is increasingly recognized that very small proteins (μ-proteins) are ubiquitously found in all species of the three domains of life, and that they fulfill important functions. The halophilic archaeon Haloferax volcanii contains 282 μ-proteins of less than 70 amino acids. Notably, 43 of these contain two C(P)XCG motifs, suggesting their potential to complex a zinc ion. To explore the significance of these proteins, 16 genes encoding C(P)XCG proteins had been deleted, and the majority of mutants exhibited phenotypic differences to the wild-type. One such protein, HVO_2753, was thoroughly characterized in a previous study. In the present study an in-depth analysis of a second protein, HVO_0758, was performed. To achieve this goal, the HVO_0758 protein was produced heterologously in Escherichia coli and homologously in H. volcanii. The purified protein was characterized using various biochemical approaches and NMR spectroscopy. The findings demonstrated that HVO_0758 is indeed a bona fide zinc finger protein, and that all four cysteine residues are essential for folding. The NMR solution structure was solved, revealing that HVO_0758 is comprised of an N-terminal alpha helix containing several positively charged residues and a globular core with the zinc finger domain. The transcriptomes of the HVO_0758 deletion mutant and, for comparison, the HVO_2753 deletion mutant were analyzed with RNA-Seq and compared against that of the wild-type. In both mutants many motility and chemotaxis genes were down-regulated, in agreement to the phenotype of the deletion mutants, which had a swarming deficit. The two H. volcanii zinc-finger μ-proteins HVO_0758 and HVO_2753 showed many differences. Taken together, two zinc finger μ-proteins of H. volcanii have been characterized intensively, which emerged as pivotal contributors to swarming behavior and biofilm formation

Theories with the independence property

A first-order theory T has the Independence Property provided T ⊢ (Q)(Φ⇒

The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression.

Molecular classification of cancers into subtypes has resulted in an advance in our understanding of tumour biology and treatment response across multiple tumour types. However, to date, cancer profiling has largely focused on protein-coding genes, which comprise <1% of the genome. Here we leverage a compendium of 58,648 long noncoding RNAs (lncRNAs) to subtype 947 breast cancer samples. We show that lncRNA-based profiling categorizes breast tumours by their known molecular subtypes in breast cancer. We identify a cohort of breast cancer-associated and oestrogen-regulated lncRNAs, and investigate the role of the top prioritized oestrogen receptor (ER)-regulated lncRNA, DSCAM-AS1. We demonstrate that DSCAM-AS1 mediates tumour progression and tamoxifen resistance and identify hnRNPL as an interacting protein involved in the mechanism of DSCAM-AS1 action. By highlighting the role of DSCAM-AS1 in breast cancer biology and treatment resistance, this study provides insight into the potential clinical implications of lncRNAs in breast cancer