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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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RNA Stability01:53

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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RNA Splicing01:32

RNA Splicing

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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RNA Editing

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq
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Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

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Physiological RNA dynamics in RNA-Seq analysis.

Zhongneng Xu1, Shuichi Asakawa2

  • 1Department of Ecology, Jinan University, Guangzhou 510632, China.

Briefings in Bioinformatics
|July 17, 2018
PubMed
Summary
This summary is machine-generated.

Physiological RNA dynamics, including accumulation and degradation, significantly impact transcriptome analysis, leading to inaccurate results. Improved techniques and bioinformatics tools are essential for reliable transcriptomic data and research findings.

Keywords:
RNA quantificationRNA-Seqalternative splicingdifferential expressionphysiological RNA accumulationphysiological RNA degradation

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Physiological RNA dynamics, such as accumulation and degradation, present challenges in transcriptome analysis.
  • These dynamics can affect various aspects of RNA analysis, including quantification and sequence characteristics.

Purpose of the Study:

  • To review the effects of physiological RNA degradation and accumulation on RNA sequencing data analysis.
  • To highlight the impact of these RNA dynamics on the accuracy of transcriptomic studies.

Main Methods:

  • Literature review of studies investigating RNA dynamics in transcriptome analysis.
  • Analysis of how RNA accumulation and degradation influence key transcriptomic metrics.

Main Results:

  • Physiological RNA accumulation impacts RNA quantification accuracy.
  • Physiological RNA degradation affects RNA sequence length, feature site identification, and quantification.
  • These dynamics can lead to incorrect estimations in transcription quantification, differential expression, co-expression, RNA decay rates, alternative splicing, and the identification of novel genes, SNPs, small RNAs, and gene fusions.

Conclusions:

  • Current transcriptomic data may require re-evaluation due to the influence of physiological RNA dynamics.
  • Development of new and improved techniques and bioinformatics software is crucial for accurate transcriptome research.