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Related Concept Videos

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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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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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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Updated: Jun 7, 2025

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TIdeS: A Comprehensive Framework for Accurate Open Reading Frame Identification and Classification in Eukaryotic

Xyrus X Maurer-Alcalá1, Eunsoo Kim1,2

  • 1Division of Invertebrate Zoology and Institute for Comparative Genomics, American Museum of Natural History, New York, NY, USA.

Genome Biology and Evolution
|November 21, 2024
PubMed
Summary

A new framework, Transcript Identification and Selection (TIdeS), improves open reading frame (ORF) prediction from eukaryotic transcriptomes, even with contamination. TIdeS offers a robust solution for analyzing complex biological interactions and curating genomic datasets.

Keywords:
ORF predictionbiotic interactionscontaminationmachine learningphylogenomics

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Area of Science:

  • Eukaryotic genomics
  • Bioinformatics
  • Molecular biology

Background:

  • Analyzing eukaryotic genetic information presents challenges like contamination and complex biotic interactions, especially for uncultured organisms.
  • Current tools for predicting open reading frames (ORFs) from transcriptomes are often inadequate in these complex scenarios.

Purpose of the Study:

  • To introduce Transcript Identification and Selection (TIdeS), a novel framework designed to overcome limitations in current omics approaches for ORF prediction.
  • To provide a robust solution for precise ORF predictions and subsequent decontamination of eukaryotic transcriptomic and genomic data.

Main Methods:

  • Development and application of the Transcript Identification and Selection (TIdeS) framework.
  • Testing TIdeS on transcriptomes from 32 diverse eukaryotic taxa.
  • Comparison of TIdeS performance against conventional ORF-prediction methods like TransDecoder.

Main Results:

  • TIdeS significantly outperforms conventional ORF-prediction methods, identifying a higher proportion of complete and in-frame ORFs.
  • TIdeS accurately classifies ORFs with minimal input data, demonstrating effectiveness even with substantial contamination.
  • The framework successfully handles complex biological interactions, such as host-symbiont and prey-predator relationships.

Conclusions:

  • TIdeS offers a flexible, single-stop solution for accurate ORF prediction and dataset decontamination in eukaryotic transcriptomics.
  • The framework facilitates robust exploration of biotic interactions and reproducible dataset curation for phylogenomic studies and beyond.