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Related Concept Videos

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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RNA-seq03:21

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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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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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Nucleic Acid Structure01:25

Nucleic Acid Structure

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria
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NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences.

Sen Yang1, Yan Wang1,2, Shuangquan Zhang1

  • 1Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun, China.

Frontiers in Genetics
|March 18, 2020
PubMed
Summary
This summary is machine-generated.

NCResNet accurately identifies noncoding RNAs (ncRNAs) using novel sequence, structure, and physicochemical features. This deep learning method significantly improves accuracy for both short and long open reading frame RNAs.

Keywords:
RNA sequence featuresdeep neural networksnoncoding RNAnoncoding RNA predictionprotein coding RNA

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Noncoding RNAs (ncRNAs) are crucial in biological processes, diseases, and cancers, but their precise identification from novel sequences remains challenging.
  • Existing methods often rely solely on sequence features and struggle with short open reading frame (ORF) RNA identification.
  • Next-generation sequencing has rapidly increased the discovery of novel RNAs, necessitating improved identification tools.

Purpose of the Study:

  • To develop a novel, reliable method for precise identification of ncRNAs, particularly those with short ORFs.
  • To overcome limitations of existing methods by incorporating diverse feature types and advanced deep learning techniques.

Main Methods:

  • Proposed NCResNet, a deep neural network model.
  • Utilized 57 hybrid features encompassing sequence, protein, RNA structure, and physicochemical properties.
  • Implemented feature enhancement and deep feature learning strategies.

Main Results:

  • NCResNet demonstrated superior accuracy and Matthews Correlation Coefficient (MCC) compared to state-of-the-art methods across 8 species.
  • Achieved >10% and >15% improvements in accuracy and MCC for short-ORF datasets (mouse, yeast, zebrafish, cow).
  • Showed enhanced performance on long-ORF RNA datasets as well.

Conclusions:

  • NCResNet offers a robust and accurate approach for identifying ncRNAs, addressing limitations of previous methods.
  • The model's ability to integrate diverse features makes it effective for both short and long ORF sequences.
  • The findings facilitate better RNA functional analysis and regulatory studies.