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 RNA-seq transcriptional profiling of Leishmania amazonensis reveals an arginase-dependent gene expression regulation

2017 ◽  
Vol 11 (10) ◽  
pp. e0006026 ◽  
Author(s):  
Juliana Ide Aoki ◽  
Sandra Marcia Muxel ◽  
Ricardo Andrade Zampieri ◽  
Maria Fernanda Laranjeira-Silva ◽  
Karl Erik Müller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Regulation ◽  
Transcriptional Profiling ◽  
Expression Regulation ◽  
Leishmania Amazonensis ◽  
Rna Seq

scholarly journals Genotype-free individual genome reconstruction of Multiparental Population Models by RNA sequencing data

2020 ◽  
Author(s):  
Kwangbom Choi ◽  
Hao He ◽  
Daniel M. Gatti ◽  
Vivek M. Philip ◽  
Narayanan Raghupathy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Gene Expression Regulation ◽  
Agricultural Research ◽  
Model Systems ◽  
Specific Gene ◽  
Rna Seq ◽  
Sequencing Data ◽  
Individual Genome ◽  
Genome Reconstruction

AbstractMulti-parent populations (MPPs), genetically segregating model systems derived from two or more inbred founder strains, are widely used in biomedical and agricultural research. Gene expression profiling by direct RNA sequencing (RNA-Seq) is commonly applied to MPPs to investigate gene expression regulation and to identify candidate genes. In genetically diverse populations, including most MPPs, quantification of gene expression is improved when the RNA-Seq reads are aligned to individualized transcriptomes that incorporate known polymorphic loci. However, the process of constructing and analyzing individual genomes can be computationally demanding and error prone. We propose a new approach, genome reconstruction by RNA-Seq (GBRS), that relies on simultaneous alignment of RNA-Seq reads to the founder strain transcriptomes. GBRS can reconstruct the diploid genome of each individual and quantify both total and allele-specific gene expression. We demonstrate that GBRS performs as well as methods that rely on high-density genotyping arrays to reconstruct the founder haplotype mosaic of MPP individuals. Using GBRS in addition to other genotyping methods provides quality control for detecting sample mix-ups and improves power to detect expression quantitative trait loci. GBRS software is freely available at https://github.com/churchill-lab/gbrs.

scholarly journals Insight into m 6 A methylation from occurrence to functions

Open Biology ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 200091
Author(s):  
Wenxiu Ru ◽  
Xiaoyan Zhang ◽  
Binglin Yue ◽  
Ao Qi ◽  
Xuemei Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Cancer Development ◽  
Regulatory Effect ◽  
Non Coding Rna ◽  
Reversible Modification ◽  
Regulatory Functions ◽  
Insight Into

RNA m 6 A methylation is a post-transcriptional modification that occurs at the nitrogen-6 position of adenine. This dynamically reversible modification is installed, removed and recognized by methyltransferases, demethylases and readers, respectively. This modification has been found in most eukaryotic mRNA, tRNA, rRNA and other non-coding RNA. Recent studies have revealed important regulatory functions of the m 6 A including effects on gene expression regulation, organism development and cancer development. In this review, we summarize the discovery and features of m 6 A, and briefly introduce the mammalian m 6 A writers, erasers and readers. Finally, we discuss progress in identifying additional functions of m 6 A and the outstanding questions about the regulatory effect of this widespread modification.

Gerea: Prediction Of Gene Expression Regulators From Transcriptome Profiling Data To Transition Networks

2021 ◽  
Vol 16 ◽  
Author(s):  
Min Yao ◽  
Caiyun Jiang ◽  
Chenglong Li ◽  
Yongxia Li ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Target Gene ◽  
Gene Expression Regulation ◽  
Transcriptome Profiling ◽  
Expression Regulation ◽  
Mouse Gene ◽  
Mouse Gene Expression ◽  
Causality Inference ◽  
Identify Gene Expression

Background: Mammalian genes are regulated at the transcriptional and post-transcriptional levels. These mechanisms may involve the direct promotion or inhibition of transcription via a regulator or post-transcriptional regulation through factors such as micro (mi)RNAs. Objective: This study aimed to construct gene regulation relationships modulated by causality inference-based miRNA-(transition factor)-(target gene) networks and analyze gene expression data to identify gene expression regulators. Methods: Mouse gene expression regulation relationships were manually curated from literature using a text mining method which was then employed to generate miRNA-(transition factor)-(target gene) networks. An algorithm was then introduced to identify gene expression regulators from transcriptome profiling data by applying enrichment analysis to these networks. Results: A total of 22,271 mouse gene expression regulation relationships were curated for 4,018 genes and 242 miRNAs. GEREA software was developed to perform the integrated analyses. We applied the algorithm to transcriptome data for synthetic miR-155 oligo-treated mouse CD4+ T-cells and confirmed that miR-155 is an important network regulator. The software was also tested on publicly available transcriptional profiling data for Salmonella infection, resulting in the identification of miR-125b as an important regulator. Conclusion: The causality inference-based miRNA-(transition factor)-(target gene) networks serve as a novel resource for gene expression regulation research, and GEREA is an effective and useful adjunct to the currently available methods. The regulatory networks and the algorithm implemented in the GEREA software package are available under a free academic license at website : http://www.thua45.cn/gerea.

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