scholarly journals MTTFsite: cross-cell type TF binding site prediction by using multi-task learning

2019 ◽  
Vol 35 (24) ◽  
pp. 5067-5077 ◽  
Author(s):  
Jiyun Zhou ◽  
Qin Lu ◽  
Lin Gui ◽  
Ruifeng Xu ◽  
Yunfei Long ◽  
...  
Keyword(s):  
Gene Expression ◽  
Histone Modification ◽  
Prediction Method ◽  
Cell Types ◽  
Supplementary Information ◽  
Learning Approaches ◽  
Cell Type ◽  
Baseline Method ◽  
Common Features ◽  
Task Learning

AbstractMotivationThe prediction of transcription factor binding sites (TFBSs) is crucial for gene expression analysis. Supervised learning approaches for TFBS predictions require large amounts of labeled data. However, many TFs of certain cell types either do not have sufficient labeled data or do not have any labeled data.ResultsIn this paper, a multi-task learning framework (called MTTFsite) is proposed to address the lack of labeled data problem by leveraging on labeled data available in cross-cell types. The proposed MTTFsite contains a shared CNN to learn common features for all cell types and a private CNN for each cell type to learn private features. The common features are aimed to help predicting TFBSs for all cell types especially those cell types that lack labeled data. MTTFsite is evaluated on 241 cell type TF pairs and compared with a baseline method without using any multi-task learning model and a fully shared multi-task model that uses only a shared CNN and do not use private CNNs. For cell types with insufficient labeled data, results show that MTTFsite performs better than the baseline method and the fully shared model on more than 89% pairs. For cell types without any labeled data, MTTFsite outperforms the baseline method and the fully shared model by more than 80 and 93% pairs, respectively. A novel gene expression prediction method (called TFChrome) using both MTTFsite and histone modification features is also presented. Results show that TFBSs predicted by MTTFsite alone can achieve good performance. When MTTFsite is combined with histone modification features, a significant 5.7% performance improvement is obtained.Availability and implementationThe resource and executable code are freely available at http://hlt.hitsz.edu.cn/MTTFsite/ and http://www.hitsz-hlt.com:8080/MTTFsite/.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Cross-Cell-Type Prediction of TF-Binding Site by Integrating Convolutional Neural Network and Adversarial Network

2019 ◽  
Vol 20 (14) ◽  
pp. 3425 ◽  
Author(s):  
Gongqiang Lan ◽  
Jiyun Zhou ◽  
Ruifeng Xu ◽  
Qin Lu ◽  
Hongpeng Wang
Keyword(s):  
Data Augmentation ◽  
Gene Expression Regulation ◽  
Cell Types ◽  
Cell Type ◽  
Baseline Method ◽  
Common Features ◽  
Adversarial Network ◽  
Tfbs Prediction ◽  
Feature Extractor ◽  
Different Cell Types

Transcription factor binding sites (TFBSs) play an important role in gene expression regulation. Many computational methods for TFBS prediction need sufficient labeled data. However, many transcription factors (TFs) lack labeled data in cell types. We propose a novel method, referred to as DANN_TF, for TFBS prediction. DANN_TF consists of a feature extractor, a label predictor, and a domain classifier. The feature extractor and the domain classifier constitute an Adversarial Network, which ensures that learned features are common features across different cell types. DANN_TF is evaluated on five TFs in five cell types with a total of 25 cell-type TF pairs and compared to a baseline method which does not use Adversarial Network. For both data augmentation and cross-cell-type prediction, DANN_TF performs better than the baseline method on most cell-type TF pairs. DANN_TF is further evaluated by an additional 13 TFs in the five cell types with a total of 65 cell-type TF pairs. Results show that DANN_TF achieves significantly higher AUC than the baseline method on 96.9% pairs of the 65 cell-type TF pairs. This is a strong indication that DANN_TF can indeed learn common features for cross-cell-type TFBS prediction.

scholarly journals alona: a web server for single-cell RNA-seq analysis

2020 ◽  
Vol 36 (12) ◽  
pp. 3910-3912 ◽  
Author(s):  
Oscar Franzén ◽  
Johan L M Björkegren
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cluster Structure ◽  
Web Server ◽  
Cell Types ◽  
Supplementary Information ◽  
Marker Genes ◽  
Cell Type

Abstract Summary Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis. We have developed a web server, alona that incorporates several of the most popular single-cell analysis algorithms into a flexible pipeline. alona can perform quality filtering, normalization, batch correction, clustering, cell type annotation and differential gene expression analysis. Data are visualized in the web browser using an interface based on JavaScript, allowing the user to query genes of interest and visualize the cluster structure. alona accepts a compressed gene expression matrix and identifies cell clusters with a graph-based clustering strategy. Cell types are identified from a comprehensive collection of marker genes or by specifying a custom set of marker genes. Availability and implementation The service runs at https://alona.panglaodb.se and the Python package can be downloaded from https://oscar-franzen.github.io/adobo/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

Specificity of gene expression in adipocytes

1985 ◽  
Vol 5 (2) ◽  
pp. 419-421
Author(s):  
K M Zezulak ◽  
H Green
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Cell Type ◽  
3T3 Cells ◽  
Cell Type Specificity ◽  
Number Of Genes ◽  
Enhanced Expression ◽  
Adipose Cells ◽  
Distinctive Phenotype

During the differentiation of preadipose 3T3 cells into adipose cells, the mRNAs for three proteins increase strikingly in abundance. To determine the degree of cell-type specificity in the expression of these mRNAs, we estimated their abundances in several nonadipose tissues of the mouse. None of these mRNAs was strictly confined to adipocytes, but the ensemble of three mRNAs was rather specific to adipocytes. Insofar as is revealed by these three markers, the distinctive phenotype of adipocytes is the result of the enhanced expression of a number of genes, none of which is completely silent in all other cell types.

scholarly journals RNA-Seq Data-Mining Allows the Discovery of Two Long Non-Coding RNA Biomarkers of Viral Infection in Humans

2020 ◽  
Vol 21 (8) ◽  
pp. 2748 ◽  
Author(s):  
Ruth Barral-Arca ◽  
Alberto Gómez-Carballa ◽  
Miriam Cebey-López ◽  
María José Currás-Tuala ◽  
Sara Pischedda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Viral Infections ◽  
Umbilical Vein ◽  
Cell Types ◽  
Dermal Fibroblasts ◽  
Learning Approaches ◽  
Rna Seq ◽  
Wide Range ◽  
Healthy Control ◽  
Umbilical Vein Endothelial Cells

There is a growing interest in unraveling gene expression mechanisms leading to viral host invasion and infection progression. Current findings reveal that long non-coding RNAs (lncRNAs) are implicated in the regulation of the immune system by influencing gene expression through a wide range of mechanisms. By mining whole-transcriptome shotgun sequencing (RNA-seq) data using machine learning approaches, we detected two lncRNAs (ENSG00000254680 and ENSG00000273149) that are downregulated in a wide range of viral infections and different cell types, including blood monocluclear cells, umbilical vein endothelial cells, and dermal fibroblasts. The efficiency of these two lncRNAs was positively validated in different viral phenotypic scenarios. These two lncRNAs showed a strong downregulation in virus-infected patients when compared to healthy control transcriptomes, indicating that these biomarkers are promising targets for infection diagnosis. To the best of our knowledge, this is the very first study using host lncRNAs biomarkers for the diagnosis of human viral infections.

scholarly journals A scalable platform for the development of cell-type-specific viral drivers

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Sinisa Hrvatin ◽  
Christopher P Tzeng ◽  
M Aurel Nagy ◽  
Hume Stroud ◽  
Charalampia Koutsioumpa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heterologous Gene Expression ◽  
High Specificity ◽  
Cell Types ◽  
Regulatory Elements ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Cell Type Specific ◽  
The Many ◽  
Dna Regulatory Elements

Enhancers are the primary DNA regulatory elements that confer cell type specificity of gene expression. Recent studies characterizing individual enhancers have revealed their potential to direct heterologous gene expression in a highly cell-type-specific manner. However, it has not yet been possible to systematically identify and test the function of enhancers for each of the many cell types in an organism. We have developed PESCA, a scalable and generalizable method that leverages ATAC- and single-cell RNA-sequencing protocols, to characterize cell-type-specific enhancers that should enable genetic access and perturbation of gene function across mammalian cell types. Focusing on the highly heterogeneous mammalian cerebral cortex, we apply PESCA to find enhancers and generate viral reagents capable of accessing and manipulating a subset of somatostatin-expressing cortical interneurons with high specificity. This study demonstrates the utility of this platform for developing new cell-type-specific viral reagents, with significant implications for both basic and translational research.

scholarly journals Human cytomegalovirus transcriptome activity differs during replication in human fibroblast, epithelial and astrocyte cell lines

2012 ◽  
Vol 93 (5) ◽  
pp. 1046-1058 ◽  
Author(s):  
James C. Towler ◽  
Bahram Ebrahimi ◽  
Brian Lane ◽  
Andrew J. Davison ◽  
Derrick J. Dargan
Keyword(s):  
Gene Expression ◽  
Human Cytomegalovirus ◽  
Growth Kinetics ◽  
Viral Gene Expression ◽  
Cell Types ◽  
Viral Gene ◽  
Genome Replication ◽  
Cell Type ◽  
Low Passage ◽  
Temporal Expression Pattern

Broad cell tropism contributes to the pathogenesis of human cytomegalovirus (HCMV), but the extent to which cell type influences HCMV gene expression is unclear. A bespoke HCMV DNA microarray was used to monitor the transcriptome activity of the low passage Merlin strain of HCMV at 12, 24, 48 and 72 h post-infection, during a single round of replication in human fetal foreskin fibroblast cells (HFFF-2s), human retinal pigmented epithelial cells (RPE-1s) and human astrocytoma cells (U373MGs). In order to correlate transcriptome activity with concurrent biological responses, viral cytopathic effect, growth kinetics and genomic loads were examined in the three cell types. The temporal expression pattern of viral genes was broadly similar in HFFF-2s and RPE-1s, but dramatically different in U373MGs. Of the 165 known HCMV protein-coding genes, 41 and 48 were differentially regulated in RPE-1s and U373MGs, respectively, compared with HFFF-2s, and 22 of these were differentially regulated in both RPE-1s and U373MGs. In RPE-1s, all differentially regulated genes were downregulated, but, in U373MGs, some were down- and others upregulated. Differentially regulated genes were identified among the immediate-early, early, early late and true-late viral gene classes. Grouping of downregulated genes according to function at landmark stages of the replication cycle led to the identification of potential bottleneck stages (genome replication, virion assembly, and virion maturation and release) that may account for cell type-dependent viral growth kinetics. The possibility that cell type-specific differences in expressed cellular factors are responsible for modulation of viral gene expression is discussed.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

scholarly journals The intersectional genetics landscape for human

2019 ◽  
Author(s):  
Andre Macedo ◽  
Alisson M. Gontijo
Keyword(s):  
Gene Expression ◽  
Therapeutic Potential ◽  
Regulatory Element ◽  
Logic Gate ◽  
Cell Types ◽  
Boolean Logic ◽  
Interindividual Variation ◽  
Model Organisms ◽  
Cell Type ◽  
Diagnostic Potential

The human body is made up of hundreds, perhaps thousands of cell types and states, most of which are currently inaccessible genetically. Genetic accessibility carries significant diagnostic and therapeutic potential by allowing the selective delivery of genetic messages or cures to cells. Research in model organisms has shown that single regulatory element (RE) activities are seldom cell type specific, limiting their usage in genetic systems designed to restrict gene expression posteriorly to their delivery to cells. Intersectional genetic approaches can increase the number of genetically accessible cells. A typical intersectional method acts like an AND logic gate by converting the input of two or more active REs into a single synthetic output, which becomes unique for that cell. Here, we systematically assessed the intersectional genetics landscape of human using a curated subset of cells from a large RE usage atlas obtained by Cap Analysis of Gene Expression Sequencing (CAGE-Seq) of thousands of primary and cancer cells (the FANTOM5 consortium atlas). We developed the heuristics and algorithms to retrieve and quality rank AND gate intersections intra- and inter-individually. We find that >90% of the 154 primary cell types surveyed can be distinguished from each other with as little as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential “Versatile Entry Codes” (VEnCodes). We show that VEnCodes could be found for 100% of the 158 cancer cell types surveyed, and that most of these are highly robust to intra- and interindividual variation. Our tools for generating and quality-ranking VEnCodes can be adapted to other RE usage databases and to other intersectional methods using alternative Boolean logic operations. Our work demonstrate the potential of intersectional approaches for future gene delivery technologies in human.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

Sign in / Sign up

Export Citation Format

Share Document