scholarly journals Prioritization of enhancer mutations by combining allele-specific chromatin accessibility with deep learning

2019 ◽  
Author(s):  
Zeynep Kalender Atak ◽  
Ibrahim Ihsan Taskiran ◽  
Christopher Flerin ◽  
David Mauduit ◽  
Liesbeth Minnoye ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Deep Learning ◽  
Allelic Imbalance ◽  
Chromatin Accessibility ◽  
Genome Variation ◽  
Explainable Ai ◽  
Allele Specific ◽  
The Impact ◽  
Deep Learning Model

Brief AbstractPrioritization of non-coding genome variation benefits from explainable AI to predict and interpret the impact of a mutation on gene regulation. Here we apply a specialized deep learning model to phased melanoma genomes and identify functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

scholarly journals Cis acting variation is common, can propagates across multiple regulatory layers, but is often buffered in developmental programs

2020 ◽  
Author(s):  
Swann Floc’hlay ◽  
Emily Wong ◽  
Bingqing Zhao ◽  
Rebecca R. Viales ◽  
Morgane Thomas-Chollier ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Transcription Factors ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Cis Acting ◽  
Dna Mutations ◽  
Allele Specific ◽  
Regulatory Dna ◽  
The Impact

AbstractPrecise patterns of gene expression are driven by interactions between transcription factors, regulatory DNA sequence, and chromatin. How DNA mutations affecting any one of these regulatory ‘layers’ is buffered or propagated to gene expression remains unclear. To address this, we quantified allele-specific changes in chromatin accessibility, histone modifications, and gene expression in F1 embryos generated from eight Drosophila crosses, at three embryonic stages, yielding a comprehensive dataset of 240 samples spanning multiple regulatory layers. Genetic variation in cis-regulatory elements is common, highly heritable, and surprisingly consistent in its effects across embryonic stages. Much of this variation does not propagate to gene expression. When it does, it acts through H3K4me3 or alternatively through chromatin accessibility and H3K27ac. The magnitude and evolutionary impact of mutations is influenced by a genes’ regulatory complexity (i.e. enhancer number), with transcription factors being most robust to cis-acting, and most influenced by trans-acting, variation. Overall, the impact of genetic variation on regulatory phenotypes appears context-dependent even within the constraints of embryogenesis.

scholarly journals Uncovering divergence in gene expression regulation in the adaptation of yeast to nitrogen scarcity

2021 ◽  
Author(s):  
Carlos A. Villarroel ◽  
Paulo Canessa ◽  
Macarena Bastias ◽  
Francisco A Cubillos
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Gene Expression Regulation ◽  
Transcription Factor Binding ◽  
Chromatin Accessibility ◽  
Expression Regulation ◽  
Factor Binding ◽  
Allele Specific ◽  
The Impact ◽  
Allelic Bias

Saccharomyces cerevisiae rewires its transcriptional output to survive stressful environments, such as nitrogen scarcity under fermentative conditions. Although divergence in nitrogen metabolism has been described among natural yeast populations, the impact of regulatory genetic variants modulating gene expression and nitrogen consumption remains to be investigated. Here, we employed an F1 hybrid from two contrasting S. cerevisiae strains, providing a controlled genetic environment to map cis factors involved in the divergence of gene expression regulation in response to nitrogen scarcity. We used a dual approach to obtain genome-wide allele-specific profiles of chromatin accessibility, transcription factor binding, and gene expression through ATAC-seq and RNA-seq. We observed large variability in allele-specific expression and accessibility between the two genetic backgrounds, with a third of these differences specific to a deficient nitrogen environment. Furthermore, we discovered events of allelic bias in gene expression correlating with allelic bias in transcription factor binding solely under nitrogen scarcity, where the majority of these transcription factors orchestrates the Nitrogen Catabolite Repression regulatory pathway and demonstrates a cis x environment-specific response. Our approach allowed us to find cis variants modulating gene expression, chromatin accessibility and allelic differences in transcription factor binding in response to low nitrogen culture conditions.

scholarly journals The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Renzhou Gui ◽  
Tongjie Chen ◽  
Han Nie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Correlation Matrix ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Fmri Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Impact ◽  
Deep Learning Model

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

scholarly journals A Joint Deep Learning Model for Simultaneous Batch Effect Correction, Denoising and Clustering in Single-Cell Transcriptomics

2020 ◽  
Author(s):  
Justin Lakkis ◽  
David Wang ◽  
Yuanchao Zhang ◽  
Gang Hu ◽  
Kui Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Single Cell ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Batch Effect ◽  
Marker Genes ◽  
Deep Learning Model ◽  
Variable Genes

AbstractRecent development of single-cell RNA-seq (scRNA-seq) technologies has led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effect, which is inevitable in studies involving human tissues. Most existing methods remove batch effect in a low-dimensional embedding space. Although useful for clustering, batch effect is still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effect. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Popular methods such as Seurat3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effect in the gene expression space, but MNN can only analyze two batches at a time and it becomes computationally infeasible when the number of batches is large. Here we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data, while correcting batch effect both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC consistently outperforms scVI, DCA, and MNN. With CarDEC denoising, those non-highly variable genes offer as much signal for clustering as the highly variable genes, suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC’s denoised and batch corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effect. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.

scholarly journals PAC: Highly accurate quantification of allelic gene expression for population and disease genetics

2021 ◽  
Author(s):  
Anna Saukkonen ◽  
Helena Kilpinen ◽  
Alan Hodgkinson
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Rna Sequencing ◽  
Specific Gene ◽  
Read Alignment ◽  
Specific Gene Expression ◽  
Powerful Approach ◽  
Allelic Gene ◽  
Allele Specific ◽  
Accurate Quantification

Analysis of allele-specific gene expression (ASE) is a powerful approach for studying gene regulation. However, detection of ASE events relies on accurate alignment of RNA-sequencing reads, where challenges still remain. We have developed PAC, a method that combines multiple steps to improve the quantification of allelic reads, including personalised (i.e. diploid) read alignment with improved allocation of multi-mapping reads. We show that PAC outperforms standard alignment approaches for ASE detection in both accuracy and in the number of sites it can reliably quantify.

scholarly journals Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Giancarlo Bonora ◽  
Vijay Ramani ◽  
Ritambhara Singh ◽  
He Fang ◽  
Dana L. Jackson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Nuclear Structure ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Chromatin Accessibility ◽  
X Inactivation ◽  
Rna Seq ◽  
X Chromosomes ◽  
Allele Specific

Abstract Background Mammalian development is associated with extensive changes in gene expression, chromatin accessibility, and nuclear structure. Here, we follow such changes associated with mouse embryonic stem cell differentiation and X inactivation by integrating, for the first time, allele-specific data from these three modalities obtained by high-throughput single-cell RNA-seq, ATAC-seq, and Hi-C. Results Allele-specific contact decay profiles obtained by single-cell Hi-C clearly show that the inactive X chromosome has a unique profile in differentiated cells that have undergone X inactivation. Loss of this inactive X-specific structure at mitosis is followed by its reappearance during the cell cycle, suggesting a “bookmark” mechanism. Differentiation of embryonic stem cells to follow the onset of X inactivation is associated with changes in contact decay profiles that occur in parallel on both the X chromosomes and autosomes. Single-cell RNA-seq and ATAC-seq show evidence of a delay in female versus male cells, due to the presence of two active X chromosomes at early stages of differentiation. The onset of the inactive X-specific structure in single cells occurs later than gene silencing, consistent with the idea that chromatin compaction is a late event of X inactivation. Single-cell Hi-C highlights evidence of discrete changes in nuclear structure characterized by the acquisition of very long-range contacts throughout the nucleus. Novel computational approaches allow for the effective alignment of single-cell gene expression, chromatin accessibility, and 3D chromosome structure. Conclusions Based on trajectory analyses, three distinct nuclear structure states are detected reflecting discrete and profound simultaneous changes not only to the structure of the X chromosomes, but also to that of autosomes during differentiation. Our study reveals that long-range structural changes to chromosomes appear as discrete events, unlike progressive changes in gene expression and chromatin accessibility.

scholarly journals Impact and evolutionary determinants of Neanderthal introgression on transcriptional and post-transcriptional regulation

2019 ◽  
Author(s):  
Martin Silvert ◽  
Lluis Quintana-Murci ◽  
Maxime Rotival
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Transcriptional Regulation ◽  
Regulatory Elements ◽  
Strong Impact ◽  
Modern Humans ◽  
Archaic Admixture ◽  
Archaic Introgression ◽  
Post Transcriptional Regulation ◽  
The Impact

AbstractArchaic admixture is increasingly recognized as an important source of diversity in modern humans, with Neanderthal haplotypes covering 1-3% of the genome of present-day Eurasians. Recent work has shown that archaic introgression has contributed to human phenotypic diversity, mostly through the regulation of gene expression. Yet, the mechanisms through which archaic variants alter gene expression, and the forces driving the introgression landscape at regulatory regions remain elusive. Here, we explored the impact of archaic introgression on transcriptional and post-transcriptional regulation, focusing on promoters and enhancers across 127 different tissues as well as microRNA-mediated regulation. Although miRNAs themselves harbor few archaic variants, we found that some of these variants may have a strong impact on miRNA-mediated gene regulation. Enhancers were by far the regulatory elements most affected by archaic introgression, with one third of the tissues tested presenting significant enrichments. Specifically, we found strong enrichments of archaic variants in adipose-related tissues and primary T cells, even after accounting for various genomic and evolutionary confounders such as recombination rate and background selection. Interestingly, we identified signatures of adaptive introgression at enhancers of some key regulators of adipogenesis, raising the interesting hypothesis of a possible adaptation of early Eurasians to colder climates. Collectively, this study sheds new light onto the mechanisms through which archaic admixture have impacted gene regulation in Eurasians and, more generally, increases our understanding of the contribution of Neanderthals to the regulation of acquired immunity and adipose homeostasis in modern humans.

scholarly journals Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 669
Author(s):  
Irfan Ullah Khan ◽  
Nida Aslam ◽  
Talha Anwar ◽  
Hind S. Alsaif ◽  
Sara Mhd. Bachar Chrouf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Data ◽  
Disease Diagnosis ◽  
University Hospital ◽  
X Ray ◽  
Clinical Patient ◽  
Proposed Model ◽  
Chest X Ray ◽  
The Impact ◽  
Deep Learning Model

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

scholarly journals Analysis of Enterprise Financial and Economic Impact Based on Background Deep Learning Model under Business Administration

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jingxiao Hu
Keyword(s):  
Deep Learning ◽  
Business Management ◽  
Learning Model ◽  
Business Administration ◽  
Modern Management ◽  
Economic Exchanges ◽  
Financial Index ◽  
Financial Economy ◽  
The Impact ◽  
Deep Learning Model

Enterprise finance has become an indispensable financial channel for people to invest in their lives, and business management can provide a better economic environment for the development of enterprise finance. The structure of enterprises is gradually becoming more and more complex, and business administration shoulders considerable responsibilities and obligations in the organization and supervision of today’s social management structure. How can China play its functions under the new situation after the world economic exchanges are more frequent is an important link to promote the stable development of financial markets. In view of the problems of economic activity behavior and certainty of financial index system under the background of existing business administration, this paper puts forward the deep learning model to make risk analysis, income analysis, profit and loss analysis, and so on. The formula of deep learning model is used to calculate the data graph of financial economy, and finally, various data are compared to get the research of several business management methods on the development of enterprise financial economy. Among them, the model of current management mode belongs to two modes: e-commerce and EPR management. They not only have very unique management characteristics but also greatly promote the development of modern management, and their roles also well interpret the characteristics of modern management. The experiment also analyzes the financial data under the four algorithms for uncertainty comparison, profit and loss comparison, discreteness comparison, volatility comparison, and possibility analysis. Finally, after the source of uncertainty, the risk prediction and risk management are carried out by constructing decision trees, and these structural models are used to bring comprehensive analysis to the financial economy of enterprises and to build the impact of good trends and development prospects.

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