Alternative splicing is a crucial mechanism of gene regulation that enables condition- and tissue-specific expression of gene isoforms. Its dysregulation plays a role in various diseases such as cancer, neurological disorders, and metabolic conditions. Despite its importance, accurate detection of alternative splicing events remains challenging. Comprehensive alternative splicing event detection typically requires deep sequencing with over 100 million reads; however, much of the publicly accessible RNA sequencing data is of lower sequencing depth. Recent advances, particularly deep learning models working with genomic sequences, offer new avenues for predicting alternative splicing without reliance on high sequencing depth data. Our study addresses the question: Can we utilize the vast repository of publicly available RNA sequencing data for comprehensive alternative splicing detection, despite the low sequencing depth? Our results demonstrate the potential of sequence-based deep learning tools such as AlphaGenome, SpliceAI and DeepSplice for initial hypothesis development and as additional filters in standard RNA sequencing pipelines, especially when sequencing depth is limited. Nonetheless, validation with higher sequencing depths remains essential for confirmation of splice events. Overall, our findings underscore the need for integrative methods combining genomic sequence data and RNA sequencing data for the prediction of tissue- and condition-specific alternative splicing in resource-limited settings.

Ontologies often involve complex logical structures, so changes to individual classes or the addition of new axioms can have significant implications for other parts of the ontology. Due to this complexity, dependencies between symbols in the vocabulary of the ontology are not always immediately apparent. In this paper, we define three semantics-based approaches for establishing dependency relationships between these symbols and explore their specific properties. Additionally, we apply these dependency relations in a case study.

Complex systems are full of unpredictable and uncertain behaviours that depend on many factors. Various fields of science have set themselves the task of studying these systems and predicting their behaviour under various premises with the help of computer models. Kwakkel et al. [1] published an uncertainty matrix to systematically record and communicate uncertainties about systems. This paper takes up the matrix, analyses the concepts of model and uncertainty in literature, and models them ontologically in the Modelling Uncertainties Ontology (MUNO), a BFO-based mid-level ontology for modelling uncertainties in models for many domains. MUNO is evaluated against requirements and competency questions. Based on the ontology, an RDF-shape is created and used as an example in a case study to annotate the uncertainties for an energy system model.

MOTIVATION: Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs. RESULTS: We propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on ∼525 000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a method's performance. We find that StrucTFactor significantly (P-value < 0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient. AVAILABILITY AND IMPLEMENTATION: Data and source code are available at https://github.com/lieboldj/StrucTFactor and on our website at https://apps.cosy.bio/StrucTFactor.

Energy is a fundamental phenomenon of physics, but energy also plays an important role in the representation of many domains, since many processes involve energy transformation or transfer. However, energy is represented very differently in existing ontologies. Even in ontologies that share BFO as top-level ontology, energy is sometimes treated as disposition, as quality, and as a material entity. As we discuss in the paper, there are reasons for each choice, which makes the ontological representation of energy a challenging subject. In this paper we present an ontological analysis of energy in a BFO-based mid-level ontology (MENO), including the different kinds of energy, their relations to dispositions as well as their realisation in processes.

Solid decision making should be ideally based on clear arguments that can be justified by trustworthy information sources. However, argument spaces can quickly get quite complex and it is very often hard to trace the line of arguments found in literature or social media such as blogs and forums. In this paper, we propose a framework for a decision supporting interactive information retrieval system using methods for argument exploration based on textual documents. This concept is supported by a prototype that focuses on the actual analysis of the retrieved arguments in order to obtain a justified decision. For that we use a simplified argumentation graph with nodes as arguments and simple attacking and supporting relations. A web-based plausibility value is propagated (using ranked-based argumentation semantics) through the network for estimating the quality of the arguments. This is based on a web search for documents that support these arguments. The final decision can further be supported by chosing certain preferred interpretations of an abstract dialectical framework, leading to an integrated view of searching, creating, analysing and deciding.

MOTIVATION: Advancing our understanding of how nervous systems work will require the ability to store and annotate 3D anatomical datasets, recording morphology, partonomy and connectivity at multiple levels of granularity from subcellular to gross anatomy. It will also require the ability to integrate this data with other data-types including functional, genetic and electrophysiological data. The web ontology language OWL2 provides the means to solve many of these problems. Using it, one can rigorously define and relate classes of anatomical structure using multiple criteria. The resulting classes can be used to annotate datasets recording, for example, gene expression or electrophysiology. Reasoning software can be used to automate classification and error checking and to construct and answer sophisticated combinatorial queries. But for such queries to give consistent and biologically meaningful results, it is important that both classes and the terms (relations) used to relate them are carefully defined. RESULTS: We formally define a set of relations for recording the spatial and connectivity relationships of neuron classes and brain regions in a broad range of species, from vertebrates to arthropods. We illustrate the utility of our approach via its application in the ontology that drives the Virtual Fly Brain web resource. AVAILABILITY AND IMPLEMENTATION: The relations we define are available from http://purl.obolibrary.org/obo/ro.owl. They are used in the Drosophila anatomy ontology (http://purl.obolibrary.org/obo/fbbt/2011-09-06/), which drives the web resource http://www.virtualflybrain.org

Bioinformatics (2012) 28(9) 1262–1269. The author has chosen to now publish the above paper as Open Access.