scholarly journals Mutual information estimation for transcriptional regulatory network inference

2017 ◽  
Author(s):  
Jonathan Ish-Horowicz ◽  
John Reid
Keyword(s):  
Mutual Information ◽  
Regulatory Networks ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Estimation Method ◽  
Computation Time ◽  
Estimation Methods ◽  
Inference Algorithm ◽  
Inference Algorithms ◽  
Transcriptional Regulatory

AbstractMutual information-based network inference algorithms are an important tool in the reverse-engineering of transcriptional regulatory networks, but all rely on estimates of the mutual information between the expression of pairs of genes. Various methods exist to compute estimates of the mutual information, but none have been firmly established as optimal for network inference. The performance of 9 mutual information estimation methods are compared using three popular network inference algorithms: CLR, MRNET and ARACNE. The performance of the estimators is compared on one synthetic and two real datasets. For estimators that discretise data, the effect of discretisation parameters are also studied in detail. Implementations of 5 estimators are provided in parallelised C++ with an R interface. These are faster than alternative implementations, with reductions in computation time up to a factor of 3,500.ResultsThe B-spline estimator consistently performs well on real and synthetic datasets. CLR was found to be the best performing inference algorithm, corroborating previous results indicating that it is the state of the art mutual inference algorithm. It is also found to be robust to the mutual information estimation method and their parameters. Furthermore, when using an estimator that discretises expression data, using N1/3 bins for N samples gives the most accurate inferred network. This contradicts previous findings that suggested using N1/2 bins.

scholarly journals Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

2018 ◽  
Author(s):  
Emily R. Miraldi ◽  
Maria Pokrovskii ◽  
Aaron Watters ◽  
Dayanne M. Castro ◽  
Nicholas De Veaux ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
Transcriptional Regulatory Network ◽  
Indirect Evidence ◽  
T Helper ◽  
Motif Analysis ◽  
T Helper 17 ◽  
Transcriptional Regulatory

AbstractTranscriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The Assay for Transposase Accessible Chromatin (ATAC)-seq, coupled with transcription-factor motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to influence gene expression modeling. We rigorously test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources (plentiful gene expression data, TF knock-outs and ChIP-seq experiments). In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF KO, ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs (“TF-TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that application of our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.

scholarly journals Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Xinbing Liu ◽  
Wei Gao ◽  
Wei Liu
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Regulatory Networks ◽  
Single Cell Analysis ◽  
Transcriptional Regulatory Network ◽  
Transcriptional Regulator ◽  
Cell Analysis ◽  
Rna Seq ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

Background. To further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out. Methods. The mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset. Results. A total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset. Conclusions. This study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

scholarly journals Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei-Wei Lin ◽  
Lin-Tao Xu ◽  
Yi-Sheng Chen ◽  
Ken Go ◽  
Chenyu Sun ◽  
...  
Keyword(s):  
Single Cell ◽  
Mouse Brain ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Rna Seq ◽  
Brain Vasculature ◽  
Transcriptional Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Background. The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods. The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results. Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions. In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.

Breast Cancer Transcriptional Regulatory Network Reprogramming by using the CRISPR/Cas9 system: An Oncogenetics Perspective

2021 ◽  
Vol 21 ◽  
Author(s):  
Desh Deepak Singh ◽  
Ravi Verma ◽  
Subhash K. Tripathi ◽  
Rajnish Sahu ◽  
Poonam Trivedi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Network ◽  
Treatment Options ◽  
Diagnostic Tools ◽  
Transcriptional Regulatory Networks ◽  
Development Of Resistance ◽  
Transcriptional Regulatory ◽  
Network Reprogramming

: Breast cancer (BC) is the second most commonly diagnosed cancer in the world. BC develops due to dysregulation of transcriptional profiles, substantial interpatient variations, genetic mutations, and dysregulation of signaling pathways in breast cells. These events are regulated by many genes such as BRCA1/2, PTEN, TP53, mTOR, TERT, AKT, PI3K and others genes. Treatment options for BC remain a hurdle, which warrants a comprehensive understanding that establishes an interlinking connection between these genes in BC tumorigenesis. Consequently, there is an increasing demand for alternative treatment approaches and the design of more effective treatments. In this regard, it is crucial to build the corresponding transcriptional regulatory networks governing BC by using advanced genetic tools and techniques. In the past, several molecular editing technologies have been used to edit genes with several limitations. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR Associated Protein 9 (CRISPR/Cas9) recently received a profound attention due to its potential in biomedical and therapeutic applications. Here, we review the role of various molecular signalling pathways dysregulated in BC development such as PTEN/PI3K/AKT/mTOR as well as BRCA1/BRCA2/TP53/TERT and their interplay between the related gene networks in BC initiation, progression and development of resistance against available targeted therapeutic agents. Use of CRISPR/Cas9 gene-editing technology to generate BC gene-specific transgenic cell lines and animal models to decipher their role and interactions with other gene products has been employed successfully. Moreover, the significance of using CRISPR/Cas9 technology to develop early BC diagnostic tools and treatments is discussed here.

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