Bioinformatics and machine learning approaches to understand the regulation of mobile genetic elements

Transposable elements (TEs, or mobile genetic elements, MGEs) are ubiquitous genetic elements that make up a substantial proportion of the genome of many species. The recent growing interest in understanding the evolution and function of TEs has revealed that TEs play a dual role in genome evolution, development, disease, and drug resistance. Cells regulate TE expression against uncontrolled activity that can lead to developmental defects and disease, using multiple strategies, such as DNA chemical modification, small RNA (sRNA) silencing, chromatin modification, as well as sequence-specific repressors. Advancements in bioinformatics and machine learning approaches are increasingly contributing to the analysis of the regulation mechanisms. A plethora of tools and machine learning approaches have been developed for prediction, annotation, and expression profiling of sRNAs, for methylation analysis of TEs, as well as for genome-wide methylation analysis through bisulfite sequencing data. In this review, we provide a guided overview of the bioinformatic and machine learning state of the art of fields closely associated with TE regulation and function.peer-reviewe

Left thoracotomy utilizing splenectomy in blunt thoracic injury: An alternative surgical approach

AbstractINTRODUCTIONPosterolateral thoracotomy could be an alternative surgical approach in selected cases coexistence of abdominal injuries with ipsilateral thoracic injury.PRESENTATION OF CASEA 65-year-old male with left sided chest injury was initially admitted to a regional health center after a crawler overthrow accident. He underwent chest tube drainage of left hemithorax and he was transferred immediately to our hospital. A CT scan showed a large spleen which was injured by a wedged splint of the 10th rib into its parenchyma. Lung parenchyma was also lacerated by chest tube misplacement with associated hemothorax. He underwent a lower left lateral thoracotomy. Splenectomy was performed via a phrenotomy and subsequently the injured lung was repaired. His postoperative course was uneventful.DISCUSSIONIncisions in the diaphragm are commonly made to provide adequate exposure during a variety of thoracic and abdominal operations. Thoracic approach could potentially be advantageous for thoracic and abdominal injuries.CONCLUSIONThoracic approach is a safe alternative, providing excellent exposure of upper abdominal organs, and should be considered in selected cases of abdominal trauma, especially when an ipsilateral thoracic injury coexists

Using attribution sequence alignment to interpret deep learning models for miRNA binding site prediction

MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the posttranscriptional regulation of biological processes. miRNAs regulate transcripts through direct binding involving the Argonaute protein family. The exact rules of binding are not known, and several in silico miRNA target prediction methods have been developed to date. Deep learning has recently revolutionized miRNA target prediction. However, the higher predictive power comes with a decreased ability to interpret increasingly complex models. Here, we present a novel interpretation technique, called attribution sequence alignment, for miRNA target site prediction models that can interpret such deep learning models on a two-dimensional representation of miRNA and putative target sequence. Our method produces a human readable visual representation of miRNA:target interactions and can be used as a proxy for the further interpretation of biological concepts learned by the neural network. We demonstrate applications of this method in the clustering of experimental data into binding classes, as well as using the method to narrow down predicted miRNA binding sites on long transcript sequences. Importantly, the presented method works with any neural network model trained on a two-dimensional representation of interactions and can be easily extended to further domains such as protein–protein interactions.peer-reviewe

Small RNA targets : advances in prediction tools and high-throughput profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA–RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.peer-reviewe

PENGUINN : precise exploration of nuclear G-quadruplexes using interpretable neural networks

G-quadruplexes (G4s) are a class of stable structural nucleic acid secondary structures that are known to play a role in a wide spectrum of genomic functions, such as DNA replication and transcription. The classical understanding of G4 structure points to four variable length guanine strands joined by variable length nucleotide stretches. Experiments using G4 immunoprecipitation and sequencing experiments have produced a high number of highly probable G4 forming genomic sequences. The expense and technical difficulty of experimental techniques highlights the need for computational approaches of G4 identification. Here, we present PENGUINN, a machine learning method based on Convolutional neural networks, that learns the characteristics of G4 sequences and accurately predicts G4s outperforming state-of-the-art methods. We provide both a standalone implementation of the trained model, and a web application that can be used to evaluate sequences for their G4 potential.peer-reviewe

Application and testing of the extended-Kalman-filtering technique for determining the planetary boundary-layer height over Athens, Greece

The final publication is available at Springer via http://dx.doi.org/10.1007/s10546-020-00514-zWe investigate the temporal evolution of the planetary boundary-layer (PBL) height over the basin of Athens, Greece, during a 6-year period (2011–2016), using data from a Raman lidar system. The range-corrected lidar signals are selected around local noon (1200 UTC) and midnight (0000 UTC), for a total of 332 cases: 165 days and 167 nights. In this dataset, the extended-Kalman filtering technique is applied and tested for the determination of the PBL height. Several well-established techniques for the PBL height estimation based on lidar data are also tested for a total of 35 cases. The lidar-derived PBL heights are compared to those derived from radiosonde data. The mean PBL height over Athens is found to be 1617¿±¿324 m at 1200 UTC and 892¿±¿130 m at 0000 UTC for the period examined, while the mean PBL-height growth rate is found to be 170¿±¿64 m h-1 and 90¿±¿17 m h-1 during daytime and night-time, respectively.The research leading to these results has received additional funding from the European Union 7th Framework Program (FP7/2011-2015) and Horizon 2020/2015-2021 Research and Innovation program (ACTRIS) under grant agreements nos 262254, 654109, and 739530, as well as from Spanish National Science Foundation and FEDER funds PGC2018-094132-B-I00. CommSensLab-UPC is a María-de-Maeztu Excellence Unit, MDM-2016-0600, funded by the Agencia Estatal de Investigación, Spain.Peer ReviewedPostprint (author's final draft

Planetary boundary layer height variability over Athens, Greece, based on the synergy of Raman lidar and radiosonde data: application of the Kalman filter and other techniques (2011-2016)

The temporal evolution of the Planetary Boundary Layer height over Athens, Greece for a 5-year period (2011-2016) is presented. Using the EOLE Raman lidar system, the range-corrected lidar signals were selected around 12:00 UTC and 00:00 UTC for a total of 332 cases (165 days and 167 nights). The Kalman filter and other techniques were used to determine PBL height. The mean PBL height was found to be around 1617±324 m (12:00 UTC) and 892±130 m (00:00 UTC).Peer ReviewedPostprint (published version

CATSNAP : a user-friendly algorithm for determining the conservation of protein variants reveals extensive parallelisms in the evolution of alternative splicing

Understanding the evolutionary conservation of complex eukaryotic transcriptomes significantly illuminates the physiological relevance of alternative splicing (AS). Examining the evolutionary depth of a given AS event with ordinary homology searches is generally challenging and time-consuming. Here, we present CATSNAP, an algorithmic pipeline for assessing the conservation of putative protein isoforms generated by AS. It employs a machine learning approach following a database search with the provided pair of protein sequences. We used the CATSNAP algorithm for analyzing the conservation of emerging experimentally characterized alternative proteins from plants and animals. Indeed, most of them are conserved among other species. CATSNAP can detect the conserved functional protein isoforms regardless of the AS type by which they are generated. Notably, we found that while the primary amino acid sequence is maintained, the type of AS determining the inclusion or exclusion of protein regions varies throughout plant phylogenetic lineages in these proteins. We also document that this phenomenon is less seen among animals. In sum, our algorithm highlights the presence of unexpectedly frequent hotspots where protein isoforms recurrently arise to carry physiologically relevant functions. The user web interface is available at https://catsnap.cesnet.cz/.peer-reviewe