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Assisted transcriptome reconstruction and splicing orthology.

Samuel Blanquart1, Jean-Stéphane Varré2, Paul Guertin3,4

  • 1Inria, Université de Lille, Lille, France.

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Summary

Predicting protein isoforms across species is challenging. This study defines splicing orthologs to accurately predict mouse protein isoforms from human transcripts, achieving over 62% accuracy.

Keywords:
EukaryotesSplicing orthologsTranscriptome prediction

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Transcriptome reconstruction is computationally intensive, with current RNA-seq methods identifying only 21% of expressed transcripts.
  • Predictive accuracy of cross-species transcriptome analysis using external species' transcripts remains unquantified.
  • Accurate identification of splicing orthologs (transcripts with identical splicing patterns) is crucial for evaluating cross-species prediction models.

Purpose of the Study:

  • To propose a rigorous definition for splicing orthologs.
  • To evaluate the predictive power of cross-species transcriptomic data for protein isoform identification.
  • To quantify the accuracy of using human transcripts to predict mouse protein isoforms.

Main Methods:

  • Developed a procedural definition of splicing orthologs based on conserved splicing sites and protein-level alignments.
  • Compared human and mouse transcripts from the curated Consensus Coding Sequence (CCDS) database.
  • Analyzed the evolution of splicing orthologs over a decade using timestamped CCDS releases.

Main Results:

  • Identified 11,122 splicing orthologs between 24,382 human and 17,909 mouse transcripts.
  • Demonstrated that human transcripts can predict over 62% of mouse protein isoforms.
  • Showed that predictions improve to 74% when limited to transcripts known eight years prior.

Conclusions:

  • Proposed a refined definition for spliced isoform orthologs to account for conserved alternative splicing variations.
  • Validated the accuracy of orthologous isoform predictions using the new definition.
  • Highlighted the potential of cross-species transcriptomic data for enhancing protein diversity prediction in eukaryotes.