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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Aptardi predicts polyadenylation sites in sample-specific transcriptomes using high-throughput RNA sequencing and DNA

Ryan Lusk1, Evan Stene2, Farnoush Banaei-Kashani2

  • 1Department of Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. ryan.lusk@cuanschutz.edu.

Nature Communications
|March 13, 2021
PubMed
Summary

Accurately identifying polyadenylation sites is difficult. The aptardi tool combines DNA and RNA sequencing data using machine learning to improve the detection of expressed polyadenylation sites.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate annotation of polyadenylation sites is crucial for understanding gene expression regulation.
  • Short-read RNA sequencing alone presents computational challenges for precise polyadenylation site identification.
  • Existing DNA sequence-based methods predict potential sites but do not account for in vivo expression variability.

Purpose of the Study:

  • To develop a novel computational tool, aptardi, for accurate prediction of expressed polyadenylation sites.
  • To integrate DNA sequence information and RNA sequencing data within a machine learning framework.
  • To refine transcript 3'-ends by identifying and validating expressed polyadenylation sites.

Main Methods:

  • Developed aptardi (alternative polyadenylation transcriptome analysis from RNA-Seq data and DNA sequence information).
  • Utilized DNA nucleotide sequence, genome-aligned RNA-Seq data, and an initial transcriptome as input.
  • Employed a machine learning paradigm to predict expressed polyadenylation sites and refine transcript structures.

Main Results:

  • aptardi demonstrated average precision twice that of standard transcriptome assemblers.
  • The recall of aptardi was improved by over three-fold compared to existing methods.
  • The model showed robust performance across different tissues and mammalian species, including the Human Brain Reference RNA standard.

Conclusions:

  • aptardi significantly enhances the accuracy and recall of expressed polyadenylation site identification.
  • The tool's ability to integrate diverse data types offers a powerful approach to transcriptome analysis.
  • aptardi provides a user-friendly platform for downstream analyses like quantitation and differential gene expression.