Background RNA secondary structure comparison is a fundamental task for several studies, among which are RNA structure prediction and evolution. The comparison can currently be done efficiently only for pseudoknot-free structures due to their inherent tree representation. Results In this work, we introduce an algebraic language to represent RNA secondary structures with arbitrary pseudoknots. Each structure is associated with a unique algebraic RNA tree that is derived from a tree grammar having concatenation , nesting and crossing as operators. From an algebraic RNA tree, an abstraction is defined in which the primary structure is neglected. The resulting structural RNA tree allows us to define a new measure of similarity calculated exploiting classical tree alignment. Conclusions The tree grammar with its operators permit to uniquely represent any RNA secondary structure as a tree. Structural RNA trees allow us to perform comparison of RNA secondary structures with arbitrary pseudoknots without taking into account the primary structure.
Summary Current methods for comparing RNA secondary structures are based on tree representations and exploit edit distance or alignment algorithms. Most of them can only process structures without pseudoknots. To overcome this limitation, we introduce ASPRAlign, a Java tool that aligns particular algebraic tree representations of RNA. These trees neglect the primary sequence and can handle structures with arbitrary pseudoknots. A measure of comparison, called ASPRA distance, is computed with a worst-case time complexity of O(n2) where n is the number of nucleotides of the longer structure. Availability and implementation ASPRAlign is implemented in Java and source code is released under the GNU GPLv3 license. Code and documentation are freely available at https://github.com/bdslab/aspralign. Contact luca.tesei@unicam.it Supplementary information Supplementary data are available at Bioinformatics online.
The design of an Ambient Assisted Living (AAL) aims to create better living conditions for the elderly, especially those who choose to live in their own houses, as long as possible. To this objective, AAL systems must mainly monitor the health status of the elderly through the analysis of data gathered via technologies based on sensor devices. Sensors networks produce collections of data of fine-grained nature, regarding general information such as device name, data type, data value, timestamp, but also specific one. The data analysis, due to its granularity and heterogeneity, makes very difficult to infer a clear overall view of the status of the elderly, it demands automatic tools for selecting meaningful data and mapping them in a common conceptual schema. In the last decade, ontologies became widely used tool to describe application domains and to enrich data with its meaning. In this paper, we propose an ontology-based methodology to perform semantic queries on a data repository, where records originated from networks of heterogeneous sources are stored. A semantic query is a pattern matching process that supports the recognition of specific temporal sequences of events that can be extracted from fine-grained data. In our framework a domain ontology are exploited at different levels of abstraction and the reasoning techniques are used to pre-process data for the final temporal analysis. The proposed approach is a deliverable of the ongoing AALISABETH project funded by Region Marche Government; while the software component is integrated into the AALISABETH framework
We introduce a new algebraic representation of RNA secondary structures as a composition of hairpins, considered as basic loops. Starting from it, we define an abstract algebraic representation and we propose a novel methodology to classify RNA structures based on two topological invariants, the genus and the crossing number. It takes advantage of the abstract representation to easily obtain two intersection graphs: one of the RNA molecule and another one of the relative shape. The edges cardinality of the former corresponds to the number of interactions among hairpins, whereas the edges cardinality of the latter is the crossing number of the shape associated to the molecule. The aforementioned crossing number together with the genus permits to define a more precise energy function than the standard one which is based on the genus only. Our methodology is validated over a subset of RNA structures extracted from Pseudobase++ database, and we classify them according to the two topological invariants.
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