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Updated: Dec 20, 2025

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CodAn: predictive models for precise identification of coding regions in eukaryotic transcripts.

Pedro G Nachtigall, Andre Y Kashiwabara, Alan M Durham

    Briefings in Bioinformatics
    |May 28, 2020
    PubMed
    Summary

    CodAn accurately identifies coding sequences (CDSs) and untranslated regions (UTRs) in eukaryotic transcriptomes. This tool improves transcriptome annotation by correctly predicting start and stop codons, outperforming existing methods.

    Keywords:
    CDS characterizationUTR characterizationannotationmRNA

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Accurate identification of coding sequences (CDSs) is crucial for transcriptome annotation.
    • Incorrect CDS prediction can lead to non-existent protein identification and compromise genomic databases.
    • Understanding untranslated regions (UTRs) is vital for regulating mRNA translation.

    Purpose of the Study:

    • To introduce CodAn (Coding sequence Annotator), a novel computational approach.
    • To predict confident CDS and UTR regions in eukaryotic transcriptome sequences.
    • To address challenges in full or partial transcriptome annotation.

    Main Methods:

    • Development of the CodAn algorithm for CDS and UTR prediction.
    • Testing CodAn on full-length and partial transcripts.
    • Comparative performance analysis against existing transcriptome annotation tools.

    Main Results:

    • CodAn demonstrates confident predictions regardless of transcript completeness or known strand sense.
    • CodAn exhibits superior performance compared to other methods, particularly in accurate start and stop codon identification.
    • The tool effectively identifies complete CDS regions.

    Conclusions:

    • CodAn is a highly effective tool for accurate CDS and UTR prediction in eukaryotes.
    • It offers improved performance for transcriptome annotation projects.
    • CodAn is recommended for researchers working with transcriptomic data.