Biology subjects

Skoblov, M.

Publications and source records attributed to Skoblov, M..

5 recordsLinked to original sources

bioRxiv2024

Revision of splicing variants in the DMD gene

BackgroundPathogenic variants in the dystrophin (DMD) gene lead to X-linked recessive Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD). Nucleotide variants that affect splicing are a known cause of hereditary diseases. However, their representation in the public genomic variation databases is limited due to the low accuracy of their interpretation, especially if they are located within exons. The analysis of splicing variants in the DMD gene is essential both for understanding the underlying molecular mechanisms of the dystrophinopathies pathogenesis and selecting suitable therapies for patients. ResultsUsing deep in silico mutagenesis of the entire DMD gene sequence and subsequent SpliceAI splicing predictions, we identified 7,948 DMD single nucleotide variants that could potentially affect splicing, 863 of them were located in exons. Next, we analyzed over 1,300 disease-associated DMD SNVs previously reported in the literature (373 exonic and 956 intronic) and intersected them with SpliceAI predictions. We predicted that [~]95% of the intronic and [~]10% of the exonic reported variants could actually affect splicing. Interestingly, the majority (75%) of patient-derived intronic variants were located in the AG-GT terminal dinucleotides of the introns, while these positions accounted for only 13% of all intronic variants predicted in silico. Of the 97 potentially spliceogenic exonic variants previously reported in patients with dystrophinopathy, we selected 38 for experimental validation. For this, we developed and tested a minigene expression system encompassing 27 DMD exons. The results showed that 35 (19 missense, 9 synonymous, and 7 nonsense) of the 38 DMD exonic variants tested actually disrupted splicing. We compared the observed consequences of splicing changes between variants leading to severe Duchenne and milder Becker muscular dystrophy and showed a significant difference in their distribution. This finding provides extended insights into relations between molecular consequences of splicing variants and the clinical features. ConclusionsOur comprehensive bioinformatics analysis, combined with experimental validation, improves the interpretation of splicing variants in the DMD gene. The new insights into the molecular mechanisms of pathogenicity of exonic single nucleotide variants contribute to a better understanding of the clinical features observed in patients with Duchenne and Becker muscular dystrophy.

genetics↗

bioRxiv2019

Comprehensive analysis of human microRNA-mRNA interactome

MicroRNAs play a key role in the regulation of gene expression. A majority of microRNA-mRNA interactions remain unidentified. Despite extensive research, our ability to predict human microRNA-mRNA interactions using computational algorithms remains limited by a complexity of the models for non-canonical interactions, and an abundance of false positive results.\n\nHere we present the landscape of microRNA-mRNA human interactions, which we derived from comprehensive analysis of datasets describing direct microRNA-mRNA interactions experimentally defined in HEK293 and Huh7.5 cell lines, along with other available microRNA and mRNA expression data. We have also established a collection of reliable microRNA binding regions that we systematically extracted in course of analysis of 79 CLIP datasets, which is available at http://score.generesearch.ru/services/mirna/.\n\nWhile only 1-2% of human genes interact with microRNAs, some RNAs display a substantial sponge effect, which is specific to the cell line of study. Some microRNAs are expressed at a very high level, while interacting with only a few mRNAs, thus, indeed, serving as specific gene expression regulators. Other miRNAs might be expressed at relatively low levels, and interact with many mRNAs. Some of the microRNAs might switch between these two classes, depending on cellular context. Results of our study provide an initial resolution into the complex patterns of human microRNA-mRNA interactions.

bioinformatics↗

bioRxiv2018

Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules.

Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention. Here we explored this approach by experimenting with various deep learning architectures for targeted tokenisation and named entity recognition. Our final model, based on a combination of convolutional and stateful recurrent neural networks with attention-like loops and hybrid word-and character-level embeddings, reaches near human-level performance on the testing dataset with no manually asserted rules. To make our model easily accessible for standalone use and integration in third-party software, weve developed a Python package with a minimalistic user interface.

bioinformatics↗

bioRxiv2017

Transcriptome-Wide Prediction Of lncRNA-RNA Interactions By A Thermodynamics Algorithm

MotivationThe discovery of thousands of long noncoding RNAs (lncRNAs) in mammals raises a question about their functionality. It has been shown that some of them function post-transcriptionally via formation of inter-molecular duplexes. Sequence alignment tools are frequently used for transcriptome-wide prediction of RNA-RNA interactions. However, such approaches have poor prediction accuracy since they ignore RNA secondary structure and interaction energy. On the other hand, application of the thermodynamics-based algorithms to long transcripts is not computationally feasible on a large scale.\n\nResultsHere we describe a new computational pipeline ASSA that combines sequence alignment and thermodynamics tools for efficient prediction of RNA-RNA interactions between long transcripts. ASSA outperforms four other tools in terms of the Area Under the Curve. ASSA predictions for the lncRNA HOTAIR confirm that it binds to the chromatin through hybridization with the nascent transcripts. Analysis of the 49 murine lncRNA knockdown experiments reveals one transcript that may regulate its targets via RNA-RNA interactions.\n\nAvailabilityASSA is available at http://assa.sourceforge.net/.\n\nContactivan.antonov@gatech.edu

bioinformatics↗

bioRxiv2017

Generalising Better: Applying Deep Learning To Integrate Deleteriousness Prediction Scores For Whole-Exome SNV Studies

Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data. Since a meta-estimator basically combines different scoring systems with highly complicated nonlinear relationships, we investigated how deep learning (supervised and unsupervised), which is particularly efficient at discovering hierarchies of features, can improve classification performance. While it is believed that one should only use deep learning for high-dimensional input spaces and other models (logistic regression, support vector machines, Bayesian classifiers, etc) for simpler inputs, we still believe that the ability of neural networks to discover intricate structure in highly heterogenous datasets can aid a meta-estimator. We compare the performance with various popular predictors, many of which are recommended by the American College of Medical Genetics and Genomics (ACMG), as well as available deep learning-based predictors. Thanks to hardware acceleration we were able to use a computationally expensive genetic algorithm to stochastically optimise hyper-parameters over many generations. Overfitting was hindered by noise injection and dropout, limiting coadaptation of hidden units. Although we stress that this work was not conceived as a tool comparison, but rather an exploration of the possibilities of deep learning application in ensemble scores, our results show that even relatively simple modern neural networks can significantly improve both prediction accuracy and coverage. We provide open-access to our finest model at http://score.generesearch.ru.

bioinformatics↗