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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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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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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.
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Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives.

Tanvir Alam1, Hamada R H Al-Absi1, Sebastian Schmeier2

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.

Non-Coding RNA
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Summary
This summary is machine-generated.

Deep learning (DL) models are revolutionizing the study of long non-coding RNAs (lncRNAs) by improving genomic data analysis. This review highlights DL

Keywords:
Attention mechanismCNNLSTMconvolutional neural networkdeep learninglncRNAlncRNAomelong non-coding RNAmachine learning

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Long non-coding RNAs (lncRNAs) are crucial regulatory molecules transcribed from the mammalian genome.
  • The lncRNAome presents complex data, necessitating advanced analytical approaches.
  • Traditional machine learning (ML) models have limitations in handling large genomic datasets.

Purpose of the Study:

  • To review the application of deep learning (DL) techniques in lncRNAome research.
  • To explore DL's contribution across nine distinct areas of lncRNAome.
  • To identify challenges in developing DL models for lncRNAome analysis.

Main Methods:

  • Comprehensive literature review of DL applications in lncRNAome studies.
  • Analysis of DL techniques utilized in various lncRNAome research domains.
  • Identification and discussion of computational challenges in DL model development for lncRNAome.

Main Results:

  • DL models demonstrate superior performance over traditional ML in genomic data analysis.
  • DL has been successfully applied across nine key areas within the lncRNAome.
  • Significant challenges exist in developing and implementing DL models for lncRNAome research.

Conclusions:

  • DL-based methods are increasingly vital for advancing lncRNAome research.
  • This review provides a comprehensive summary of DL's role in multiple lncRNAome facets.
  • Addressing computational challenges is key to further leveraging DL in this field.