ROZWÓJ OPROGRAMOWANIA ALGORYTMICZNEGO I ŚRODKÓW INFORMATYCZNYCH DLA ONTOLOGII JĘZYKOWEJ BAZUJĄCYCH NA METODZIE TWORZENIA STRUKTURALNEGO ELEKTRONICZNEGO ZASOBU ENCYKLOPEDYCZNEGO

Intellectualization of the information retrieval process requires appliance of the specialized linguistic resources. The domain specific linguistic ontology might be one of such resources. This article presents software organization method for automated formation of the ontological knowledge base by converting structured encyclopaedic resource to the appropriate ontology objects. The concepts ontology database creation procedures, concepts hierarchies and associative relations networks are presented and also studies of the qualitative and quantitative composition of the current experimental ontology based on Ukrainian Wikipedia segment are performed.Intelektualizacja procesu wyszukiwania informacji wymaga urządzenia z wyspecjalizowanymi zasobami lingwistycznymi. Specyficzna ontologia językowa może być domeną jednego z takich zasobów. W artykule przedstawiono oprogramowanie do automatycznego tworzenia bazy wiedzy ontologicznej przez konwersję zorganizowanych zasobów encyklopedycznych do odpowiednich obiektów ontologicznych. Przedstawiono koncepcję procedury tworzenia bazy danych ontologii, koncepcję hierarchii oraz sieci relacji, a także przeprowadzone badania jakościowego i ilościowego składu bieżącej ontologii eksperymentalnej opartej na ukraińskiej wersji Wikipedii

Genomic evidence supports the introgression between two sympatric stickleback species inhabiting the White Sea basin

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Intergeneric Hybridization of Two Stickleback Species Leads to Introgression of Membrane-Associated Genes and Invasive TE Expansion

Interspecific hybridization has occurred relatively frequently during the evolution of vertebrates. This process usually abolishes reproductive isolation between the parental species. Moreover, it results in the exchange of genetic material and can lead to hybridogenic speciation. Hybridization between species has predominately been observed at the interspecific level, whereas intergeneric hybridization is rarer. Here, using whole-genome sequencing analysis, we describe clear and reliable signals of intergeneric introgression between the three-spined stickleback (Gasterosteus aculeatus) and its primarily distant freshwater relative to the nine-spined stickleback (Pungitius pungitius) that inhabit northwestern Russia. Through comparative analysis, we demonstrate that such introgression phenomena occur in the moderate-salinity White Sea basin, although it is not detected in Japanese sea stickleback populations. Bioinformatical analysis of the sites influenced by introgression showed that they are located near transposable elements, whereas those in protein-coding sequences are primarily found in membrane-associated and alternative splicing-related genes.Intergeneric Hybridization of Two Stickleback Species Leads to Introgression of Membrane-Associated Genes and Invasive TE ExpansionpublishedVersio

Classification of diffraction patterns in single particle imaging experiments performed at X-ray free-electron lasers using a convolutional neural network

Single particle imaging (SPI) is a promising method for native structure determination which has undergone a fast progress with the development of X-ray Free-Electron Lasers. Large amounts of data are collected during SPI experiments, driving the need for automated data analysis. The necessary data analysis pipeline has a number of steps including binary object classification (single versus multiple hits). Classification and object detection are areas where deep neural networks currently outperform other approaches. In this work, we use the fast object detector networks YOLOv2 and YOLOv3. By exploiting transfer learning, a moderate amount of data is sufficient for training of the neural network. We demonstrate here that a convolutional neural network (CNN) can be successfully used to classify data from SPI experiments. We compare the results of classification for the two different networks, with different depth and architecture, by applying them to the same SPI data with different data representation. The best results are obtained for YOLOv2 color images linear scale classification, which shows an accuracy of about 97% with the precision and recall of about 52% and 61%, respectively, which is in comparison to manual data classification.Comment: 23 pages, 6 figures, 3 table

CircParser : a novel streamlined pipeline for circular RNA structure and host gene prediction in non-model organisms

Circular RNAs (circRNAs) are long noncoding RNAs that play a significant role in various biological processes, including embryonic development and stress responses. These regulatory molecules can modulate microRNA activity and are involved in different molecular pathways as indirect regulators of gene expression. Thousands of circRNAs have been described in diverse taxa due to the recent advances in high throughput sequencing technologies, which led to a huge variety of total RNA sequencing being publicly available. A number of circRNA de novo and host gene prediction tools are available to date, but their ability to accurately predict circRNA host genes is limited in the case of low-quality genome assemblies or annotations. Here, we present CircParser, a simple and fast Unix/Linux pipeline that uses the outputs from the most common circular RNAs in silico prediction tools (CIRI, CIRI2, CircExplorer2, find_circ, and circFinder) to annotate circular RNAs, assigning presumptive host genes from local or public databases such as National Center for Biotechnology Information (NCBI). Also, this pipeline can discriminate circular RNAs based on their structural components (exonic, intronic, exon-intronic or intergenic) using a genome annotation file.publishedVersio