scholarly journals Basal Role for Nrf2-transgne on transcriptional (mRNA/miRNA) regulation in the mouse myocardium

2018 ◽  
Author(s):  
Arun Jyothidasan ◽  
Gobinath Shanmugam ◽  
John Zhang ◽  
Brain Dally ◽  
David Crossman ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
In Silico ◽  
Defense Mechanisms ◽  
High Throughput Sequencing ◽  
Redox Homeostasis ◽  
In Silico Analysis ◽  
Mirna Regulation ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Cellular Environment

Background: NFE2L2 (nuclear factor, erythroid 2 like 2; Nrf2), is a stress responsive transcription factor that regulates cellular redox homeostasis. The action mechanism of Nrf2 occurs through sensing oxidative stress in the cellular environment in responses to various stresses including altered metabolism, toxic insult and xenobiotic stresses. However, under the basal condition, the role of excess Nrf2 on global miRNA and mRNA interactions in the myocardium is unknown. Here, we tested a hypothesis that excess Nrf2 (transgene) will promote the transcription of antioxidants, which then might escalate the basal defense mechanisms in the heart. Furthermore, we investigated whether changes in the miRNA profile might have a strong role on the transcriptome in TG hearts. Methods: Non transgenic (NTG), and Cardiac specific Nrf2 transgenic (Nrf2 TG) mice in the C57/BL6 background at the age of 6 to 8 months were used to examine transcriptional and posttranscriptional signatures (mRNA and miRNA expressions) by performing next generation sequencing (NGS) for RNA (RNAseq) and small RNA (sRNAseq) for miRNA expressions in the heart (n=3 to 6/group). Validation of the NGS data was performed by quantitative real time PCR (qRT PCR) using specific primers targeting selected miRNAs or mRNAs. Finally, in silico analyses were performed to determine the miRNA and mRNA interactome networks and the pathways that are potentially regulated by these networks. Results: NGS analysis for mRNA indicated that there were 5727 differently expressed genes DEGs) in the Nrf2 TG vs. NTG myocardium. Of which, 3552 were upregulated and 2175 were downregulated significantly. Small RNAseq analysis revealed that 112 miRNAs were significantly altered in Nrf2TG versus NTG hearts. Among these miRNAs, 68 were upregulated and 44 were downregulated significantly. Validation of key targets for mRNA and miRNA by qPCR confirmed the NGS results. In silico analysis (IPA) revealed that the majority of the miRNAs and mRNAs that are significantly altered in response to transgene expression are potentially involved in Nrf2 mediated oxidative stress response, ILK Signaling, hypoxia signaling in the cardiovascular system and glutathione mediated detoxification etc. Conclusion: These high throughput sequencing data sets from cardiac specific Nrf2TG mice revealed the transcriptome wide effects of Nrf2 in the myocardium. Furthermore, our comprehensive analysis indicates that Nrf2 may directly or indirectly regulates these sub sets of cardiac miRNA and mRNA interactome networks under basal physiological setting.

An In Silico Detection of a Citrus Viroid from Raw High-Throughput Sequencing Data

2021 ◽  
pp. 275-283
Author(s):  
Tyler Dang ◽  
Andres Espindola ◽  
Georgios Vidalakis ◽  
Kitty Cardwell
Keyword(s):  
High Throughput ◽  
In Silico ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data

scholarly journals Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yuhua Fu ◽  
Pengyu Fan ◽  
Lu Wang ◽  
Ziqiang Shu ◽  
Shilin Zhu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Target Genes ◽  
Target Prediction ◽  
Large Data ◽  
Sequencing Data ◽  
Regulate Gene Expression ◽  
High Throughput Sequencing Data ◽  
Annotation Information ◽  
Public Data ◽  
Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

scholarly journals In Silico Study Suggesting the Bias of Primers Choice in the Molecular Identification of Fungal Aerosols

2021 ◽  
Vol 7 (2) ◽  
pp. 99
Author(s):  
Hamza Mbareche ◽  
Marc Veillette ◽  
Guillaume J. Bilodeau
Keyword(s):  
In Silico ◽  
Fungal Diversity ◽  
High Throughput Sequencing ◽  
In Silico Analysis ◽  
Its Region ◽  
Internal Transcribed Spacers ◽  
Sequence Length ◽  
Universal Method ◽  
Continuous Work ◽  
Its1 And Its2

This paper presents an in silico analysis to assess the current state of the fungal UNITE database in terms of the two eukaryote nuclear ribosomal regions, Internal Transcribed Spacers 1 and 2 (ITS1 and ITS2), used in describing fungal diversity. Microbial diversity is often evaluated with amplicon-based high-throughput sequencing approaches, which is a target enrichment method that relies on the amplification of a specific target using particular primers before sequencing. Thus, the results are highly dependent on the quality of the primers used for amplification. The goal of this study is to validate if the mismatches of the primers on the binding sites of the targeted taxa could explain the differences observed when using either ITS1 or ITS2 in describing airborne fungal diversity. Hence, the choice of the pairs of primers for each barcode concur with a study comparing the performance of ITS1 and ITS2 in three occupational environments. The sequence length varied between the amplicons retrieved from the UNITE database using the pair of primers targeting ITS1 and ITS2. However, the database contains an equal number of unidentified taxa from ITS1 and ITS2 regions in the six taxonomic levels employed (phylum, class, order, family, genus, species). The chosen ITS primers showed differences in their ability to amplify fungal sequences from the UNITE database. Eleven taxa consisting of Trichocomaceae, Dothioraceae, Botryosphaeriaceae, Mucorales, Saccharomycetes, Pucciniomycetes, Ophiocordyceps, Microsporidia, Archaeorhizomycetes, Mycenaceae, and Tulasnellaceae showed large variations between the two regions. Note that members of the latter taxa are not all typical fungi found in the air. As no universal method is currently available to cover all the fungal kingdom, continuous work in designing primers, and particularly combining multiple primers targeting the ITS region is the best way to compensate for the biases of each one to get a larger view of the fungal diversity.

scholarly journals deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

2020 ◽  
Vol 49 (D1) ◽  
pp. D877-D883
Author(s):  
Fangzhou Xie ◽  
Shurong Liu ◽  
Junhao Wang ◽  
Jiajia Xuan ◽  
Xiaoqin Zhang ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Clinical Information ◽  
Sequencing Data ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
High Throughput Sequencing Data ◽  
Expression Atlas ◽  
Expression Evolution ◽  
Noninvasive Biomarkers ◽  
Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

scholarly journals Improving gene function predictions using independent transcriptional components

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Carlos G. Urzúa-Traslaviña ◽  
Vincent C. Leeuwenburgh ◽  
Arkajyoti Bhattacharya ◽  
Stefan Loipfinger ◽  
Marcel A. T. M. van Vugt ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
High Throughput Sequencing ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Sequencing Data ◽  
New Members ◽  
High Throughput Sequencing Data ◽  
Gene Sets ◽  
Functional Understanding

AbstractThe interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

scholarly journals Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
Author(s):  
Sten Anslan ◽  
R. Henrik Nilsson ◽  
Christian Wurzbacher ◽  
Petr Baldrian ◽  
Leho Tedersoo ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Computation Time ◽  
Potential Effect ◽  
Data Sets ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
High Throughput Sequencing Data ◽  
Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

scholarly journals circtools—a one-stop software solution for circular RNA research

2018 ◽  
Vol 35 (13) ◽  
pp. 2326-2328 ◽  
Author(s):  
Tobias Jakobi ◽  
Alexey Uvarovskii ◽  
Christoph Dieterich
Keyword(s):  
High Throughput Sequencing ◽  
Circular Rna ◽  
Statistical Testing ◽  
Supplementary Information ◽  
Circular Rnas ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Multi Stage ◽  
Sequence Reconstruction ◽  
One Stop

Abstract Motivation Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. Results Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, screening for enrichment of RBP binding sites, differential exon RNase R resistance and circRNA-specific primer design. circtools supports researchers with visualization options and data export into commonly used formats. Availability and implementation circtools is available via https://github.com/dieterich-lab/circtools and http://circ.tools under GPLv3.0. Supplementary information Supplementary data are available at Bioinformatics online.

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