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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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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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Prediction and classification of ncRNAs using structural information.

Bharat Panwar, Amit Arora1, Gajendra P S Raghava

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This study developed accurate methods to distinguish coding from non-coding RNA (ncRNA) and classify ncRNAs into families. A simple tri-nucleotide composition feature achieved high accuracy, outperforming existing methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Non-coding RNA (ncRNA) transcripts play crucial roles in cellular activities, necessitating precise classification.
  • Vast amounts of sequence data from next-generation sequencing require robust algorithms for transcript discrimination and ncRNA family assignment.
  • Current ncRNA classification algorithms have limitations in performance, highlighting the need for improved methods.

Purpose of the Study:

  • To develop accurate computational tools for discriminating coding from non-coding RNA (ncRNA) sequences.
  • To classify diverse ncRNA transcripts into their respective families using structural and sequence-based features.
  • To enhance the accuracy and efficiency of ncRNA classification compared to existing methodologies.

Main Methods:

  • A Support Vector Machine (SVM)-based approach utilizing tri-nucleotide composition was employed to differentiate coding and non-coding transcripts.
  • Graph properties of predicted ncRNA structures were used as features for classification into 18 distinct ncRNA classes.
  • Various machine learning algorithms, including RandomForest, were evaluated for ncRNA classification performance.

Main Results:

  • The SVM method using tri-nucleotide composition achieved a Matthews Correlation Coefficient (MCC) of 0.98 for coding vs. non-coding discrimination.
  • The RandomForest model demonstrated superior performance in classifying ncRNA transcripts into 18 classes, outperforming other tested algorithms.
  • The developed method showed higher sensitivity and specificity across multiple classes compared to the GraPPLE study, with an overall MCC of 0.40 versus 0.29.

Conclusions:

  • Tri-nucleotide composition is a sufficient feature for accurately discriminating coding and non-coding RNA sequences.
  • Graph properties of ncRNA structures combined with the RandomForest algorithm provide a highly effective approach for ncRNA family classification.
  • An accessible online and standalone tool, RNAcon, has been developed to facilitate ncRNA classification.