Automatic intron detection in metagenomes using neural networks.

Tato práce se zabývá detekcí intronů v metagenomech hub pomocí hlubokých neuronových sítí. Přesné biologické mechanizmy rozpoznávání a vyřezávání intronů nejsou zatím plně známy a jejich strojová detekce není považovaná za vyřešený problém. Rozpoznávání a vyřezávání intronů z DNA sekvencí je důležité pro identifikaci genů v metagenomech a hledání jejich homologií mezi známými DNA sekvencemi,které jsou dostupné ve veřejných databázích. Rozpoznání genů a nalezení jejich případných homologů umožňuje identifikaci jak již známých tak i nových druhů a jejich taxonomické zařazení. V rámci práce vznikly dva modely neuronových sítí, které detekují začátky a konce intronů, takzvaná donorová a akceptorová místa sestřihu. Detekovaná místa sestřihu jsou následně zkombinována do kandidátních intronů. Překrývající se kandidátní introny jsou poté odstraněny pomocí jednoduchého skórovacího algoritmu. Práce navazuje na existující řešení, které využívá metody podpůrných vektorů (SVM). Výsledné neuronové sítě dosahují lepších výsledků než SVM a to při více než desetinásobně nižším výpočetním čase na zpracování stejně obsáhlého genomu.This work is concerned with the detection of introns in metagenomes with deep neural networks. Exact biological mechanisms of intron recognition and splicing are not fully known yet and their automated detection has remained unresolved. Detection and removal of introns from DNA sequences is important for the identification of genes in metagenomes and for searching for homologs among the known DNA sequences available in public databases. Gene prediction and the discovery of their homologs allows the identification of known and new species and their taxonomic classification. Two neural network models were developed as part of this thesis. The models' aim is the detection of intron starts and ends with the so-called donor and acceptor splice sites. The splice sites are later combined into candidate introns which are further filtered by a simple score-based overlap resolving algorithm. The work relates to an existing solution based on support vector machines (SVM). The resulting neural networks achieve better results than SVM and require more than order of magnitude less computational resources in order to process equally large genome

Thorough in silico and in vitro cDNA analysis of 21 putative BRCA1 and BRCA2 splice variants and a complex tandem duplication in BRCA2 allowing the identification of activated cryptic splice donor sites in BRCA2 exon 11

For 21 putative BRCA1 and BRCA2 splice site variants, the concordance between mRNA analysis and predictions by in silico programs was evaluated. Aberrant splicing was confirmed for 12 alterations. In silico prediction tools were helpful to determine for which variants cDNA analysis is warranted, however, predictions for variants in the Cartegni consensus region but outside the canonical sites, were less reliable. Learning algorithms like Adaboost and Random Forest outperformed the classical tools. Further validations are warranted prior to implementation of these novel tools in clinical settings. Additionally, we report here for the first time activated cryptic donor sites in the large exon 11 of BRCA2 by evaluating the effect at the cDNA level of a novel tandem duplication (5 breakpoint in intron 4; 3 breakpoint in exon 11) and of a variant disrupting the splice donor site of exon 11 (c.6841+1G>C). Additional sites were predicted, but not activated. These sites warrant further research to increase our knowledge on cis and trans acting factors involved in the conservation of correct transcription of this large exon. This may contribute to adequate design of ASOs (antisense oligonucleotides), an emerging therapy to render cancer cells sensitive to PARP inhibitor and platinum therapies

Machine learning models towards elucidating the plant intron retention code

2017 Fall.Includes bibliographical references.Alternative Splicing is a process that allows a single gene to encode multiple proteins. Intron Retention (IR) is a type of alternative splicing which is mainly prevalent in plants, but has been shown to regulate gene expression in various organisms and is often involved in rare human diseases. Despite its important role, not much research has been done to understand IR. The motivation behind this research work is to better understand IR and how it is regulated by various biological factors. We designed a combination of 137 features, forming an "intron retention code", to reveal the factors that contribute to IR. Using random forest and support vector machine classifiers, we show the usefulness of these features for the task of predicting whether an intron is subject to IR or not. An analysis of the top-ranking features for this task reveals a high level of similarity of the most predictive features across the three plant species, demonstrating the conservation of the factors that determine IR. We also found a high level of similarity to the top features contributing to IR in mammals. The task of predicting the response to drought stress proved more difficult, with lower levels of accuracy and lower levels of similarity across species, suggesting that additional features need to be considered for predicting condition-specific IR

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis

CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

Background: Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. Methods: It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. Results: We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. Conclusions: While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction

Using machine learning to predict pathogenicity of genomic variants throughout the human genome

Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org