scholarly journals Cotranscriptional and Posttranscriptional Features of the Transcriptome in Soybean Shoot Apex and Leaf

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiafu Zhu ◽  
Han Zhao ◽  
Fanjiang Kong ◽  
Baohui Liu ◽  
Min Liu ◽  
...  
Keyword(s):  
Shoot Apex ◽  
Gene Expression Regulation ◽  
Agronomic Traits ◽  
Rna Seq ◽  
Non Coding Rna ◽  
Natural Antisense Rna ◽  
Rna Biogenesis ◽  
Nascent Rna ◽  
Leguminous Crops ◽  
Highly Correlated

Transcription is the first step of central dogma, in which the genetic information stored in DNA is copied into RNA. In addition to mature RNA sequencing (RNA-seq), high-throughput nascent RNA assays have been established and applied to provide detailed transcriptional information. Here, we present the profiling of nascent RNA from trifoliate leaves and shoot apices of soybean. In combination with nascent RNA (chromatin-bound RNA, CB RNA) and RNA-seq, we found that introns were largely spliced cotranscriptionally. Although alternative splicing (AS) was mainly determined at nascent RNA biogenesis, differential AS between the leaf and shoot apex at the mature RNA level did not correlate well with cotranscriptional differential AS. Overall, RNA abundance was moderately correlated between nascent RNA and mature RNA within each tissue, but the fold changes between the leaf and shoot apex were highly correlated. Thousands of novel transcripts (mainly non-coding RNA) were detected by CB RNA-seq, including the overlap of natural antisense RNA with two important genes controlling soybean reproductive development, FT2a and Dt1. Taken together, we demonstrated the adoption of CB RNA-seq in soybean, which may shed light on gene expression regulation of important agronomic traits in leguminous crops.

scholarly journals Systematic analysis of RNA-seq-based gene co-expression across multiple plants

2017 ◽  
Author(s):  
Hua Yu ◽  
Bingke Jiao ◽  
Chengzhi Liang
Keyword(s):  
Plant Species ◽  
Regulatory Mechanism ◽  
Gene Expression Regulation ◽  
Agronomic Traits ◽  
Enrichment Analysis ◽  
Biological Processes ◽  
Rna Seq ◽  
Systematic Analysis ◽  
Gene Modules ◽  
Species Specific

AbstractThe complex cellular network was formed by the interacting gene modules. Building the high-quality RNA-seq-based Gene Co-expression Network (GCN) is critical for uncovering these modules and understanding the phenotypes of an organism. Here, we established and analyzed the RNA-seq-based GCNs in two monocot species rice and maize, and two eudicot species Arabidopsis and soybean, and subdivided them into co-expressed modules. Taking rice as an example, we associated these modules with biological functions and agronomic traits by enrichment analysis, and discovered a large number of conditin-specific or tissue-specific modules. In addition, we also explored the regulatory mechanism of the modules by enrichment of the known cis-elements, transcription factors and miRNA targets. Their coherent enrichment with the inferred functions of the modules revealed their synergistic effect on the gene expression regulation. Moreover, the comparative analysis of gene co-expression was performed to identify conserved and species-specific functional modules across 4 plant species. We discovered that the modules shared across 4 plants participate in the basic biological processes, whereas the species-specific modules were involved in the spatiotemporal-specific processes linking the genotypes to phenotypes. Our research provides the massive modules relating to the cellular activities and agronomic traits in several model and crop plant species.

Understanding non-coding DNA regions in yeast

2013 ◽  
Vol 41 (6) ◽  
pp. 1654-1659 ◽  
Author(s):  
Margarita Schlackow ◽  
Monika Gullerova
Keyword(s):  
Gene Expression ◽  
Fission Yeast ◽  
Rna Sequencing ◽  
Dna Microarrays ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Present Review ◽  
Rna Seq ◽  
Antisense Transcripts ◽  
Non Coding Rna

Non-coding transcripts play an important role in gene expression regulation in all species, including budding and fission yeast. Such regulatory transcripts include intergenic ncRNA (non-coding RNA), 5′ and 3′ UTRs, introns and antisense transcripts. In the present review, we discuss advantages and limitations of recently developed sequencing techniques, such as ESTs, DNA microarrays, RNA-Seq (RNA sequencing), DRS (direct RNA sequencing) and TIF-Seq (transcript isoform sequencing). We provide an overview of methods applied in yeast and how each of them has contributed to our knowledge of gene expression regulation and transcription.

scholarly journals SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Verônica R. de Melo Costa ◽  
Julianus Pfeuffer ◽  
Annita Louloupi ◽  
Ulf A. V. Ørom ◽  
Rosario M. Piro
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Time Course ◽  
Progressive Increase ◽  
Rna Seq ◽  
Transcript Processing ◽  
Aberrant Transcript ◽  
Genome Wide ◽  
Splicing Efficiency ◽  
Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

scholarly journals Non-Coding RNA Signatures of B-Cell Acute Lymphoblastic Leukemia

2021 ◽  
Vol 22 (5) ◽  
pp. 2683
Author(s):  
Princess D. Rodriguez ◽  
Hana Paculova ◽  
Sophie Kogut ◽  
Jessica Heath ◽  
Hilde Schjerven ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
B Cell ◽  
Molecular Mechanisms ◽  
Lymphoblastic Leukemia ◽  
Transcriptome Profiling ◽  
Rna Seq ◽  
Protein Coding ◽  
Non Coding Rna ◽  
Technological Developments ◽  
Cell Acute Lymphoblastic Leukemia

Non-coding RNAs (ncRNAs) comprise a diverse class of non-protein coding transcripts that regulate critical cellular processes associated with cancer. Advances in RNA-sequencing (RNA-Seq) have led to the characterization of non-coding RNA expression across different types of human cancers. Through comprehensive RNA-Seq profiling, a growing number of studies demonstrate that ncRNAs, including long non-coding RNA (lncRNAs) and microRNAs (miRNA), play central roles in progenitor B-cell acute lymphoblastic leukemia (B-ALL) pathogenesis. Furthermore, due to their central roles in cellular homeostasis and their potential as biomarkers, the study of ncRNAs continues to provide new insight into the molecular mechanisms of B-ALL. This article reviews the ncRNA signatures reported for all B-ALL subtypes, focusing on technological developments in transcriptome profiling and recently discovered examples of ncRNAs with biologic and therapeutic relevance in B-ALL.

scholarly journals Machine learning reduced gene/non-coding RNA features that classify Schizophrenia patients accurately and highlight insightful gene clusters

2020 ◽  
Author(s):  
Yichuan Liu ◽  
Hui-Qi Qu ◽  
Xiao Chang ◽  
Lifeng Tian ◽  
Joseph Glessner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neurodevelopmental Disorder ◽  
Wnt Signaling Pathway ◽  
Gene Clusters ◽  
Differentially Expressed ◽  
Cell Junction ◽  
Mrna Levels ◽  
Rna Seq ◽  
Non Coding Rna ◽  
Non Coding Rnas

AbstractSchizophrenia (SCZ) is a chronic and severely disabling neurodevelopmental disorder that affects people worldwide. RNA-seq has been a powerful method to detect the differentially expressed genes/non-coding RNAs in patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. In this study, dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals’ was obtained from the CommonMind consortium and analyzed with machine learning methods, including random forest, forward feature selection (ffs), and factor analysis, to reduce the numbers of gene/non-coding RNA feature vectors to overcome overfitting problem and explore involved functional clusters. In 2-fold shuffle testing, the average predictive accuracy for SCZ patients was 67% based on coding genes, and the 96% based on long non-coding RNAs (lncRNAs). Coding genes were further clustered into 14 factors and lncRNAs were clustered into 45 factors to represent the underlying features. The largest contribution factor for coding genes contains number of genes critical in neurodevelopment and previously reported in relation with various brain disorders. Genomic loci of lncRNAs were more insightful, enriched for genes critical in synapse function (p=7.3E-3), cell junction (p=0.017), neuron differentiation (p=8.3E-3), phosphorylation (8.2E-4), and involving the Wnt signaling pathway (p=0.029). Taken together, machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the formers regulate every level of gene expression, not limited to mRNA levels.

scholarly journals Pan-cancer systematic identification of lncRNAs associated with cancer prognosis

2018 ◽  
Author(s):  
Matthew H. Ung ◽  
Evelien Schaafsma ◽  
Daniel E. Mattox ◽  
George L. Wang ◽  
Chao Cheng
Keyword(s):  
Drug Targets ◽  
Patient Survival ◽  
Cancer Prognosis ◽  
Rna Seq ◽  
Non Coding Rna ◽  
Cancer Types ◽  
Microarray Studies ◽  
Systematic Identification ◽  
Pan Cancer ◽  
Novel Associations

AbstractThe “dark matter” of the genome harbors several non-coding RNA species including IncRNAs, which have been implicated in neoplasias but remain understudied. RNA-seq has provided deep insights into the nature of lncRNAs in cancer but current RNA-seq data are rarely accompanied by longitudinal patient survival information. In contrast, a plethora of microarray studies have collected these clinical metadata that can be leveraged to identify novel associations between gene expression and clinical phenotypes. In this study, we developed an analysis framework that computationally integrates RNA-seq and microarray data to systematically screen 9,463 lncRNAs for association with mortality risk across 20 cancer types. In total, we identified a comprehensive list of associations between lncRNAs and patient survival and demonstrate that these prognostic lncRNAs are under selective pressure and may be functional. Our results provide valuable insights that facilitate further exploration of lncRNAs and their potential as cancer biomarkers and drug targets.

scholarly journals Long non-coding RNA LINC00152 regulates cell proliferation and migration by epigenetically repressing LRIG1 expression in cholangiocarcinoma

2020 ◽  
Author(s):  
Ni Wang ◽  
Yang Yu ◽  
Boming Xu ◽  
Chunmei Zhang ◽  
Jie Liu ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Biological Function ◽  
Rna Seq ◽  
Normal Tissues ◽  
Qrt Pcr ◽  
Non Coding Rna ◽  
And Migration ◽  
Long Non Coding Rna ◽  
Proliferation And Migration

Abstract Background: Recently, long non-coding RNAs (lncRNAs) have been verified to have significant regulatory roles in multiple human cancer processes. Long non-coding RNA LINC00152, located on chromosome 2p11.2, was identified as an oncogenic lncRNA in various cancers. However, the biological function and molecular mechanism of LINC00152 in cholangiocarcinoma (CCA) are still unknown.Methods: Bioinformatic analysis was performed to determine LINC00152 expression levels in the CCA and normal tissues by using raw microarray data downloaded from Gene Expression Omnibus (GSE76297) and The Cancer Genome Atlas (TCGA). Quantitative reverse transcription PCR (qRT-PCR) was used to validate LINC00152 expression in the CCA tissues compared with that in the paired normal tissues. CCK8, colony formation, Edu assays, transwell assays, flow cytometry, and in vivo tumor formation assays were performed to investigate the biological function of LINC00152 on CCA cell phenotypes. RNA-seq was carried out to identify the downstream target gene which was further examined by qRT-PCR, western bolt and rescue experiments. RNA immunoprecipitation (RIP) and Chromatin immunoprecipitation (ChIP) assays were performed to reveal the factors involved in the mechanism of LINC00152 functions in CCA.Results: LINC00152 is significantly upregulated in cholangiocarcinoma. LINC00152 regulated the proliferation and migration of cholangiocarcinoma cells both in vitro and in vivo. RNA-seq revealed that LINC00152 knockdown preferentially affected genes linked with cell proliferation, cell differentiation and cell adhesion. Furthermore, mechanistic investigation validated that LINC00152 could bind EZH2 and modulate the histone methylation of promoter of leucine rich repeats and immunoglobulin like domains 1 (LRIG1), thereby affecting cholangiocarcinoma cells growth and migration.Conclusion: Taken together, these results demonstrated the significant roles of LINC00152 in cholangiocarcinoma and suggested a new diagnostic and therapeutic direction of cholangiocarcinoma.

scholarly journals Identification of Long Non-coding RNA Isolated From Naturally Infected Macrophages and Associated With Bovine Johne's Disease in Canadian Holstein Using a Combination of Neural Networks and Logistic Regression

2021 ◽  
Vol 8 ◽  
Author(s):  
Andrew Marete ◽  
Olivier Ariel ◽  
Eveline Ibeagha-Awemu ◽  
Nathalie Bissonnette
Keyword(s):  
Neural Networks ◽  
Logistic Regression ◽  
Differential Expression Analysis ◽  
Johne's Disease ◽  
Classification Systems ◽  
Johne’S Disease ◽  
Potential Candidate ◽  
Rna Seq ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Mycobacterium avium ssp. paratuberculosis (MAP) causes chronic enteritis in most ruminants. The pathogen MAP causes Johne's disease (JD), a chronic, incurable, wasting disease. Weight loss, diarrhea, and a gradual drop in milk production characterize the disease's clinical phase, culminating in death. Several studies have characterized long non-coding RNA (lncRNA) in bovine tissues, and a previous study characterizes (lncRNA) in macrophages infected with MAP in vitro. In this study, we aim to characterize the lncRNA in macrophages from cows naturally infected with MAP. From 15 herds, feces and blood samples were collected for each cow older than 24 months, twice yearly over 3–5 years. Paired samples were analyzed by fecal PCR and blood ELISA. We used RNA-seq data to study lncRNA in macrophages from 33 JD(+) and 33 JD(–) dairy cows. We performed RNA-seq analysis using the “new Tuxedo” suite. We characterized lncRNA using logistic regression and multilayered neural networks and used DESeq2 for differential expression analysis and Panther and Reactome classification systems for gene ontology (GO) analysis. The study identified 13,301 lncRNA, 605 of which were novel lncRNA. We found seven genes close to differentially expressed lncRNA, including CCDC174, ERI1, FZD1, TWSG1, ZBTB38, ZNF814, and ZSCAN4. None of the genes associated with susceptibility to JD have been cited in the literature. LncRNA target genes were significantly enriched for biological process GO terms involved in immunity and nucleic acid regulation. These include the MyD88 pathway (TLR5), GO:0043312 (neutrophil degranulation), GO:0002446 (neutrophil-mediated immunity), and GO:0042119 (neutrophil activation). These results identified lncRNA with potential roles in host immunity and potential candidate genes and pathways through which lncRNA might function in response to MAP infection.

Transcriptome-wide m6A detection with DART-Seq

2019 ◽  
Author(s):  
Kate D. Meyer
Keyword(s):  
Gene Expression Regulation ◽  
Preparation Method ◽  
Current Knowledge ◽  
Cross Reactivity ◽  
High Variability ◽  
Library Preparation ◽  
Rna Seq ◽  
Generation Sequencing ◽  
Library Preparation Method

Abstract m6A is the most abundant internal mRNA modification and plays diverse roles in gene expression regulation. Much of our current knowledge about m6A has been driven by recent advances in the ability to detect this mark transcriptome-wide. Antibody-based approaches have been the method of choice for global m6A mapping studies. These methods rely on m6A antibodies to immunoprecipitate methylated RNAs, followed by next-generation sequencing to identify m6A-containing transcripts1,2. While these methods enabled the first identification of m6A sites transcriptome-wide and have dramatically improved our ability to study m6A, they suffer from several limitations. These include requirements for high amounts of input RNA, costly and time-consuming library preparation, high variability across studies, and m6A antibody cross-reactivity with other modifications. Here, we describe DART-Seq (deamination adjacent to RNA modification targets), an antibody-free method for global m6A detection. In DART-Seq, the C to U deaminating enzyme, APOBEC1, is fused to the m6A-binding YTH domain. This fusion protein is then introduced to cellular RNA either through overexpression in cells or with in vitro assays, and subsequent deamination of m6A-adjacent cytidines is then detected by RNA sequencing to identify m6A sites. DART-Seq can successfully map m6A sites throughout the transcriptome using as little as 10 nanograms of total cellular RNA, and it is compatible with any standard RNA-seq library preparation method.

scholarly journals Developmental atlas of the RNA editome in Sus scrofa skeletal muscle

DNA Research ◽  
2019 ◽  
Vol 26 (3) ◽  
pp. 261-272 ◽  
Author(s):  
Yalan Yang ◽  
Min Zhu ◽  
Xinhao Fan ◽  
Yilong Yao ◽  
Junyu Yan ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Rna Editing ◽  
Sus Scrofa ◽  
Muscle Development ◽  
Rna Seq ◽  
Sequencing Data ◽  
Adenosine Deaminases ◽  
Bisulphite Sequencing ◽  
Highly Correlated ◽  
Gain Loss

AbstractAdenosine-to-inosine (A-to-I) RNA editing meditated by adenosine deaminases acting on RNA (ADARs) enzymes is a widespread post-transcriptional event in mammals. However, A-to-I editing in skeletal muscle remains poorly understood. By integrating strand-specific RNA-seq, whole genome bisulphite sequencing, and genome sequencing data, we comprehensively profiled the A-to-I editome in developing skeletal muscles across 27 prenatal and postnatal stages in pig, an important farm animal and biomedical model. We detected 198,892 A-to-I editing sites and found that they occurred more frequently at prenatal stages and showed low conservation among pig, human, and mouse. Both the editing level and frequency decreased during development and were positively correlated with ADAR enzymes expression. The hyper-edited genes were functionally related to the cell cycle and cell division. A co-editing module associated with myogenesis was identified. The developmentally differential editing sites were functionally enriched in genes associated with muscle development, their editing levels were highly correlated with expression of their host mRNAs, and they potentially influenced the gain/loss of miRNA binding sites. Finally, we developed a database to visualize the Sus scrofa RNA editome. Our study presents the first profile of the dynamic A-to-I editome in developing animal skeletal muscle and provides evidences that RNA editing is a vital regulator of myogenesis.

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