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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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Related Experiment Video

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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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Computational identification of human long intergenic non-coding RNAs using a GA-SVM algorithm.

Yanqiu Wang1, Yang Li, Qi Wang

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.

Gene
|October 15, 2013
PubMed
Summary

This study introduces a novel classifier, linc-SF, to accurately identify long intergenic non-coding RNAs (lincRNAs) using sequence and structural features. The developed method significantly improves lincRNA prediction accuracy, overcoming previous reproducibility issues.

Keywords:
ACCClassificationF-measureFeature selectionFmGAGA–SVMLincRNAsMCCMFESESPSVMa wrapper feature selection algorithm that combines support vector machine and genetic algorithmaccuracycorrelation coefficientgenetic algorithmlinc-SFlincRNAslncRNAslong intergenic non-coding RNAslong non-coding RNAsmicroRNA precursorsminimum free energypre-miRNAssensitivityspecificitysupport vector machinethe LincRNA Classifier based on Selected Features

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Long intergenic non-coding RNAs (lincRNAs) are implicated in disease development.
  • Current lincRNA identification methods, primarily next-generation sequencing, suffer from poor reproducibility due to tissue-specific expression.
  • A need exists for robust methods to identify lincRNAs independent of expression data.

Purpose of the Study:

  • To develop a reliable classifier for distinguishing lincRNAs from non-lincRNAs.
  • To utilize sequence, structural, and protein-coding potential features for lincRNA identification.
  • To overcome the limitations of poor reproducibility in lincRNA discovery.

Main Methods:

  • Employed sequence, structural, and protein-coding potential features to build a lincRNA classifier.
  • Utilized the GA-SVM algorithm for optimized feature subset extraction.
  • Constructed the LincRNA Classifier based on Selected Features (linc-SF) using a support vector machine (SVM).

Main Results:

  • An optimized feature subset demonstrated superior performance in identifying human lincRNAs via five-fold cross-validation.
  • The linc-SF classifier achieved high prediction accuracy on two independent lincRNA datasets.
  • Recognition rates of 100% and 99.8% were achieved for the independent lincRNA sets, validating the classifier's effectiveness.

Conclusions:

  • The linc-SF classifier, utilizing selected sequence and structural features, is highly effective for human lincRNA prediction.
  • This approach offers improved reproducibility and accuracy compared to traditional expression-based methods.
  • The developed classifier provides a valuable tool for lincRNA research and disease association studies.