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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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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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Ribosomal RNA Synthesis02:53

Ribosomal RNA Synthesis

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Ribosome synthesis is a highly complex and coordinated process involving more than 200 assembly factors. The synthesis and processing of ribosomal components occurs not only in the nucleolus but also in the nucleoplasm and the cytoplasm of eukaryotic cells.
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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RNA-CODE: a noncoding RNA classification tool for short reads in NGS data lacking reference genomes.

Cheng Yuan1, Yanni Sun

  • 1Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America.

Plos One
|November 9, 2013
PubMed
Summary
This summary is machine-generated.

A new tool, RNA-CODE, efficiently identifies non-coding RNAs (ncRNAs) in short sequencing reads without a reference genome. This advances analysis of transcriptomic and metagenomic data, especially for non-model organisms.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Transcriptomic sequencing is rapidly expanding, particularly for non-model organisms.
  • Non-coding RNAs (ncRNAs) are crucial for biological processes but challenging to identify in short sequencing reads.
  • Existing ncRNA identification tools are not optimized for next-generation sequencing (NGS) data, especially very short reads.

Purpose of the Study:

  • To develop and implement a comprehensive tool, RNA-CODE, for classifying non-coding RNAs (ncRNAs) from very short sequencing reads.
  • To enable accurate ncRNA identification in next-generation sequencing (NGS) data, even without a high-quality reference genome.

Main Methods:

  • RNA-CODE is a computational tool designed for classifying short RNA sequencing reads into different ncRNA families.
  • The tool is specifically engineered to handle NGS data lacking complete reference genomes.
  • It classifies reads to enable quantification of ncRNA expression and composition profiles.

Main Results:

  • RNA-CODE demonstrated competitive performance in sensitivity and specificity compared to existing tools.
  • The tool was successfully applied to RNA-seq data from Arabidopsis and human gut metagenomic data.
  • Results show RNA-CODE's effectiveness in identifying ncRNAs in diverse biological samples.

Conclusions:

  • RNA-CODE provides an effective solution for identifying and classifying ncRNAs in short NGS reads, particularly for non-model organisms.
  • The tool facilitates the analysis of ncRNA expression in RNA-seq and composition in metagenomic datasets.
  • RNA-CODE enhances post-NGS analysis capabilities for genomic and transcriptomic studies.