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Related Concept Videos

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. 
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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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Related Experiment Video

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RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
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Identification of long non-coding transcripts with feature selection: a comparative study.

Giovanna M M Ventola1,2, Teresa M R Noviello1,2, Salvatore D'Aniello3

  • 1Department of Science and Technology, University of Sannio, via Port'Arsa, 11, Benevento, 82100, Italy.

BMC Bioinformatics
|March 25, 2017
PubMed
Summary

Identifying novel long non-coding RNAs (lncRNAs) is crucial for understanding gene regulation. This study systematically assesses sequence features, revealing species-specific signatures that improve computational prediction accuracy for lncRNAs.

Keywords:
ClassificationFeature selectionlncRNA

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long non-coding RNAs (lncRNAs) are key gene regulators.
  • High-throughput RNA-seq data necessitates efficient computational methods for novel lncRNA identification.
  • Existing methods utilize features like open reading frames, conservation scores, nucleotide arrangements, and RNA secondary structure.

Purpose of the Study:

  • To systematically assess sequence-based features for distinguishing lncRNAs from other transcript classes.
  • To identify novel signatures (groups of features) for improved lncRNA detection.
  • To evaluate the impact of these signatures on prediction performance across different species.

Main Methods:

  • Systematic assessment of diverse sequence-derived features, including transposable element repeats.
  • Evaluation of feature selection algorithms and signature stability.
  • Application of machine learning algorithms to predict lncRNAs using identified signatures.
  • Comparative analysis of prediction performance against existing tools.

Main Results:

  • Identified distinct lncRNA signatures in human, mouse, and zebrafish, with some shared and some species-specific features.
  • Incorporating novel signatures into machine learning models significantly improved prediction performance (1-24% increase in area under precision-recall curve).
  • Demonstrated the effectiveness of the proposed approach over standard coding potential tools.

Conclusions:

  • Understanding optimal features enhances automatic annotation pipelines, particularly for under-annotated genomes like zebrafish.
  • Developed a web tool for novel lncRNA recognition using identified signatures.
  • The tool supports FASTA and GTF formats and is accessible online.