scholarly journals Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yuming Zhao ◽  
Fang Wang ◽  
Su Chen ◽  
Jun Wan ◽  
Guohua Wang
Keyword(s):  
Transcription Factor ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
High Throughput Sequencing ◽  
Prediction Models ◽  
Promoter Prediction ◽  
Sequencing Data ◽  
Protein Coding ◽  
Mirna Genes ◽  
Intergenic Regions

MicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks.

scholarly journals Gene Expression and Regulatory Webwork of POLR2K in Bladder Carcinogenesis by Integrated Bioinformatics Approaches

2020 ◽  
Author(s):  
Liliang Yang ◽  
Kaizhen Wang ◽  
Wenjing Guo ◽  
Xian Chen ◽  
Qinglong Guo ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Transcription Factor ◽  
Rna Polymerase ◽  
Rna Polymerase Ii ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Transcription Factor Binding ◽  
Sequencing Data ◽  
Factor Binding

Abstract Background：RNA polymerase II subunit K (POLR2K) belongs to one of the multiple subunits of RNA polymerase II (Pol II), whose biological function is to synthesize mRNA. Aberrant POLR2K expression is related to carcinogenesis. However, POLR2K’s underlying role in bladder cancer has not been explored. In the current study, we intend to analyze the function of POLR2K and its regulatory network within bladder cancer.Methods: Public sequencing data was obtained from GEO and TCGA to investigate POLR2K expression and regulatory network within bladder cancer (BLCA) by using GEPIA and Oncomine as well as cBioPortal online tool. LinkedOmics was employed to identify genes displaying significantly differential expression patterns and to perform GO and KEGG analyses. After differential genes was assigned and ranked, GSEA analyses was performed to obtain target networks for transcription factors, miRNAs, and kinases that could regulate POLR2K–associated gene network. Subsequent functional webwork analyses were used to identify cancer-relevant pathways Moreover, POLR2K gene is verified, by ChIP-seq in MCF-7 cell line , with transcription factor binding evidence in the ENCODE Transcription Factor Binding Site Profiles dataset.Conclusions: The current study implies that POLR2K gene is overexpressed and often amplified in BLCA, providing the first evidence that POLR2K deregulation, in particular increased transcription, may promote BLCA. These findings uncover a unique expression patterns of POLR2K and its potential regulatory networks in BLCA, contributing greatly to study of the role of POLR2K in cancer development.

scholarly journals Multi-omic regulatory networks capture downstream effects of kinase inhibition in Mycobacterium tuberculosis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Albert T. Young ◽  
Xavier Carette ◽  
Michaela Helmel ◽  
Hanno Steen ◽  
Robert N. Husson ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Mycobacterium Tuberculosis ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Protein Interaction Data ◽  
Kinase Inhibition ◽  
Protein Protein Interaction ◽  
Downstream Effects ◽  
Regulate Cell Growth ◽  
Regulatory Effects

AbstractThe ability of Mycobacterium tuberculosis (Mtb) to adapt to diverse stresses in its host environment is crucial for pathogenesis. Two essential Mtb serine/threonine protein kinases, PknA and PknB, regulate cell growth in response to environmental stimuli, but little is known about their downstream effects. By combining RNA-Seq data, following treatment with either an inhibitor of both PknA and PknB or an inactive control, with publicly available ChIP-Seq and protein–protein interaction data for transcription factors, we show that the Mtb transcription factor (TF) regulatory network propagates the effects of kinase inhibition and leads to widespread changes in regulatory programs involved in cell wall integrity, stress response, and energy production, among others. We also observe that changes in TF regulatory activity correlate with kinase-specific phosphorylation of those TFs. In addition to characterizing the downstream regulatory effects of PknA/PknB inhibition, this demonstrates the need for regulatory network approaches that can incorporate signal-driven transcription factor modifications.

scholarly journals Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Neel Patel ◽  
William S. Bush
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcriptional Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Prediction Models ◽  
Long Distance ◽  
Chromatin Looping ◽  
Gene Regulatory ◽  
The Impact

Abstract Background Transcriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features. Results We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression. Conclusions Our models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.

Screening and survival analysis of melanoma immunodrug response-related genes and the function of magnetic nanoparticles in gene extraction

2021 ◽  
Vol 11 (8) ◽  
pp. 1306-1312
Author(s):  
Li Song ◽  
Ningchao Du ◽  
Haitao Luo ◽  
Furong Li
Keyword(s):  
Survival Analysis ◽  
Magnetic Nanoparticles ◽  
Drug Response ◽  
High Throughput Sequencing ◽  
Cox Proportional Hazards ◽  
Sequencing Data ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Long Non Coding Rna ◽  
Rna Genes

This study aimed to identify the association of protein coding and long non coding RNA genes with immunotherapy response in melanoma. Based on RNA sequencing data of melanoma specimens, the expression levels of protein coding and long non coding RNA genes were calculated using the Kallisto RNA-seq quantification method, and differently expressed genes were detected using the DESeq2 method. Cox proportional hazards regression was used to evaluate the effects of gene expression on survival. According to the clinical data of 14 patients with drug response and 11 patients without drug response, 18 protein coding genes and 14 long non coding RNAs showed differential expressions (multiple of difference > 2 and P < 0.01 after correction), among which the coding genes of differential expression were significantly enriched through the process of cell adhesion (P < 0.01). The results of survival analysis showed that 18 coding genes and 14 long non coding RNA genes had significant effects on patient survival (P < 0.01). In this study, magnetic nanoparticles can be used to extract genomic DNA and total RNA due to their paramagnetism and biocompatibility, then transcriptome high-throughput sequencing was performed. The method has the advantages of removing dangerous reagents such as phenol and chloroform, replacing inorganic coating such as silica with organic oil, and shortening reaction time. Protein coding and long non coding RNA genes as well as magnetic nanoparticles may serve as potential cancer immune biomarker targets for developing future oncological treatments.

scholarly journals Multilocus Characterization, Gene Expression Analysis of Putative Immunodominant Protein Coding Regions, and Development of Recombinase Polymerase Amplification Assay for Detection of ‘Candidatus Phytoplasma Pruni’ in Prunus avium

2019 ◽  
Vol 109 (6) ◽  
pp. 983-992 ◽  
Author(s):  
Dan Edward V. Villamor ◽  
Kenneth C. Eastwell
Keyword(s):  
High Throughput Sequencing ◽  
Sweet Cherry ◽  
Prunus Avium ◽  
Protein A ◽  
Recombinase Polymerase Amplification ◽  
Sequencing Data ◽  
Coding Region ◽  
Protein Coding ◽  
Coding Regions ◽  
Reverse Transcription Pcr

Western X (WX) disease, caused by ‘Candidatus Phytoplasma pruni’, is a devastating disease of sweet cherry resulting in the production of small, bitter-flavored fruits that are unmarketable. Escalation of WX disease in Washington State prompted the development of a rapid detection assay based on recombinase polymerase amplification (RPA) to facilitate timely removal and replacement of diseased trees. Here, we report on a reliable RPA assay targeting putative immunodominant protein coding regions that showed comparable sensitivity to polymerase chain reaction (PCR) in detecting ‘Ca. Phytoplasma pruni’ from crude sap of sweet cherry tissues. Apart from the predominant strain of ‘Ca. Phytoplasma pruni’, the RPA assay also detected a novel strain of phytoplasma from several WX-affected trees. Multilocus sequence analyses using the immunodominant protein A (idpA), imp, rpoE, secY, and 16S ribosomal RNA regions from several ‘Ca. Phytoplasma pruni’ isolates from WX-affected trees showed that this novel phytoplasma strain represents a new subgroup within the 16SrIII group. Examination of high-throughput sequencing data from total RNA of WX-affected trees revealed that the imp coding region is highly expressed, and as supported by quantitative reverse transcription PCR data, it showed higher RNA transcript levels than the previously proposed idpA coding region of ‘Ca. Phytoplasma pruni’.

scholarly journals Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data

2011 ◽  
Vol 7 (11) ◽  
pp. e1002190 ◽  
Author(s):  
Chao Cheng ◽  
Koon-Kiu Yan ◽  
Woochang Hwang ◽  
Jiang Qian ◽  
Nitin Bhardwaj ◽  
...  
Keyword(s):  
High Throughput ◽  
Regulatory Network ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data

scholarly journals CircHIPK3 dysregulation of the miR-30c/DLL4 axis is essential for KSHV lytic replication

2021 ◽  
Author(s):  
Katherine L Harper ◽  
Timothy J Mottram ◽  
Chinedu A Arene ◽  
Becky Foster ◽  
Molly R Patterson ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Cell Cycle Control ◽  
Lytic Replication ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Novel Host ◽  
Cellular Pathways

Non coding RNA (ncRNA) regulatory networks are emerging as critical regulators of gene expression. These intricate networks of ncRNA-ncRNA interactions modulate multiple cellular pathways and impact the development and progression of multiple diseases. Herpesviruses, including Kaposi's sarcoma-associated herpesvirus, are adept at utilising ncRNAs, encoding their own as well as dysregulating host ncRNAs to modulate virus gene expression and the host response to infection. Research has mainly focused on unidirectional ncRNA-mediated regulation of target protein-coding transcripts; however, we have identified a novel host ncRNA regulatory network essential for KSHV lytic replication in B cells. KSHV-mediated upregulation of the host cell circRNA, circHIPK3, is a key component of this network, functioning as a competing endogenous RNA of miR-30c, leading to increased levels of the miR-30c target, DLL4. Dysregulation of this network highlights a novel mechanism of cell cycle control during KSHV lytic replication in B cells. Importantly, disruption at any point within this novel ncRNA regulatory network has a detrimental effect on KSHV lytic replication, highlighting the essential nature of this network and potential for therapeutic intervention.

scholarly journals Discovering unknown human and mouse transcription factor binding sites and their characteristics from ChIP-seq data

2021 ◽  
Vol 118 (20) ◽  
pp. e2026754118
Author(s):  
Chun-Ping Yu ◽  
Chen-Hao Kuo ◽  
Chase W. Nelson ◽  
Chi-An Chen ◽  
Zhi Thong Soh ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Sequencing Data ◽  
Transcription Start Sites ◽  
Factor Binding ◽  
Quality Control Criterion ◽  
Intergenic Regions ◽  
Human And Mouse

Transcription factor binding sites (TFBSs) are essential for gene regulation, but the number of known TFBSs remains limited. We aimed to discover and characterize unknown TFBSs by developing a computational pipeline for analyzing ChIP-seq (chromatin immunoprecipitation followed by sequencing) data. Applying it to the latest ENCODE ChIP-seq data for human and mouse, we found that using the irreproducible discovery rate as a quality-control criterion resulted in many experiments being unnecessarily discarded. By contrast, the number of motif occurrences in ChIP-seq peak regions provides a highly effective criterion, which is reliable even if supported by only one experimental replicate. In total, we obtained 2,058 motifs from 1,089 experiments for 354 human TFs and 163 motifs from 101 experiments for 34 mouse TFs. Among these motifs, 487 have not previously been reported. Mapping the canonical motifs to the human genome reveals a high TFBS density ±2 kb around transcription start sites (TSSs) with a peak at −50 bp. On average, a promoter contains 5.7 TFBSs. However, 70% of TFBSs are in introns (41%) and intergenic regions (29%), whereas only 12% are in promoters (−1 kb to +100 bp from TSSs). Notably, some TFs (e.g., CTCF, JUN, JUNB, and NFE2) have motifs enriched in intergenic regions, including enhancers. We inferred 142 cobinding TF pairs and 186 (including 115 completely) tethered binding TF pairs, indicating frequent interactions between TFs and a higher frequency of tethered binding than cobinding. This study provides a large number of previously undocumented motifs and insights into the biological and genomic features of TFBSs.

scholarly journals sRNATargetDigger: A bioinformatics software for bidirectional identification of sRNA-target pairs with co-regulatory sRNAs information

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244480
Author(s):  
Xinghuo Ye ◽  
Zhihong Yang ◽  
Yeqin Jiang ◽  
Lan Yu ◽  
Rongkai Guo ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Target Genes ◽  
Small Interfering Rnas ◽  
Sequencing Data ◽  
Software Packages ◽  
Bioinformatics Software ◽  
Function Modules ◽  
Target Regulation

Identification of the target genes of microRNAs (miRNAs), trans-acting small interfering RNAs (ta-siRNAs), and small interfering RNAs (siRNAs) is an important step for understanding their regulatory roles in plants. In recent years, many bioinformatics software packages based on small RNA (sRNA) high-throughput sequencing (HTS) and degradome sequencing data analysis have provided strong technical support for large-scale mining of sRNA-target pairs. However, sRNA-target regulation is achieved using a complex network of interactions since one transcript might be co-regulated by multiple sRNAs and one sRNA may also affect multiple targets. Currently used mining software can realize the mining of multiple unknown targets using known sRNA, but it cannot rule out the possibility of co-regulation of the same target by other unknown sRNAs. Hence, the obtained regulatory network may be incomplete. We have developed a new mining software, sRNATargetDigger, that includes two function modules, “Forward Digger” and “Reverse Digger”, which can identify regulatory sRNA-target pairs bidirectionally. Moreover, it has the ability to identify unknown sRNAs co-regulating the same target, in order to obtain a more authentic and reliable sRNA-target regulatory network. Upon re-examination of the published sRNA-target pairs in Arabidopsis thaliana, sRNATargetDigger found 170 novel co-regulatory sRNA-target pairs. This software can be downloaded from http://www.bioinfolab.cn/sRNATD.html.

scholarly journals noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

2021 ◽  
Author(s):  
I. Moutsopoulos ◽  
L. Maischak ◽  
E. Lauzikaite ◽  
S. A. Vasquez Urbina ◽  
E. C. Williams ◽  
...  
Keyword(s):  
Background Noise ◽  
Regulatory Networks ◽  
High Throughput Sequencing ◽  
Noise Removal ◽  
Sequencing Data ◽  
Technical Noise ◽  
Transcript Quantification ◽  
Optimal Information ◽  
The Cost ◽  
The Impact

AbstractHigh-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses.We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices.We exemplify the effects of minimising technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.TeaserNoise removal from sequencing quantification improves the convergence of downstream tools and robustness of conclusions.

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