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De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
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A Statistical Method without Training Step for the Classification of Coding Frame in Transcriptome Sequences.

Nicolas Carels1, Diego Frías

  • 1Fundação Oswaldo Cruz (FIOCRUZ), Instituto Oswaldo Cruz (IOC), Laboratório de Genômica Funcional e Bioinformática, Rio de Janeiro, RJ, Brazil.

Bioinformatics and Biology Insights
|February 13, 2013
PubMed
Summary

The Universal Feature Method (UFM) accurately classifies coding open reading frames (cORFs) in expressed sequence tags (ESTs) with over 95% success. This method aids in analyzing transcriptome data across eukaryotes without prior knowledge.

Keywords:
CDSESTORFRNYUFMclassificationgenomics

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Expressed Sequence Tags (ESTs) are crucial for transcriptome analysis.
  • Accurate classification of coding open reading frames (cORFs) is essential for understanding gene function.
  • Existing methods may require prior knowledge or struggle with diverse eukaryotic transcriptomes.

Purpose of the Study:

  • To investigate the Universal Feature Method (UFM) for classifying coding open reading frames (cORFs) in expressed sequence tags (ESTs).
  • To evaluate UFM's accuracy and applicability across various eukaryotic organisms.
  • To establish UFM as a tool for efficient transcriptome analysis.

Main Methods:

  • Utilized the Universal Feature Method (UFM), which scores purine bias and stop codon frequencies.
  • Developed a 5-factor scoring system for ORF classification.
  • Validated UFM against protein sequences from the Protein Data Bank (PDB).
  • Combined UFM with CAP3 for assembling large EST samples.

Main Results:

  • UFM successfully classifies cORFs ≥200 bp (known strand) and ≥300 bp (unknown strand) with >95% accuracy.
  • Statistical parameters were established using ESTs from diverse eukaryotes (e.g., human, plants, insects).
  • UFM is applicable to ~95% of higher eukaryote protein-encoding genes.
  • Transcriptome phenotypes were analyzed in rice, maize, and humans using UFM-assembled cORFs.

Conclusions:

  • UFM is a robust, low-complexity tool for rapid cORF extraction from transcriptome data.
  • It enables accurate genome phenotype description in plants without prior knowledge.
  • Caution is advised for human transcriptome analysis due to noisy sequences like pseudogenes.
  • UFM facilitates prior knowledge extraction regarding the coding fraction of any eukaryote's transcriptome.