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StartLink and StartLink+: Prediction of Gene Starts in Prokaryotic Genomes.

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Last modified
  • 06/17/2025
Type of Material
Authors
    Karl Gemayel, Georgia Tech, AtlantaAlexandre Lomsadze, Emory UniversityMark Borodovsky, Emory University
Language
  • English
Date
  • 2021
Publisher
  • Frontiers
Publication Version
Copyright Statement
  • © 2021 Gemayel, Lomsadze and Borodovsky.
License
Final Published Version (URL)
Title of Journal or Parent Work
Volume
  • 1
Start Page
  • 704157
End Page
  • 704157
Grant/Funding Information
  • This work was supported in part by the National Institutes of Health (NIH) (GM128145 to MB).
  • Funding for open access charge was obtained by the National Institutes of Health (GM128145).
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Abstract
  • State-of-the-art algorithms of ab initio gene prediction for prokaryotic genomes were shown to be sufficiently accurate. A pair of algorithms would agree on predictions of gene 3'ends. Nonetheless, predictions of gene starts would not match for 15-25% of genes in a genome. This discrepancy is a serious issue that is difficult to be resolved due to the absence of sufficiently large sets of genes with experimentally verified starts. We have introduced StartLink that infers gene starts from conservation patterns revealed by multiple alignments of homologous nucleotide sequences. We also have introduced StartLink+ combining both ab initio and alignment-based methods. The ability of StartLink to predict the start of a given gene is restricted by the availability of homologs in a database. We observed that StartLink made predictions for 85% of genes per genome on average. The StartLink+ accuracy was shown to be 98-99% on the sets of genes with experimentally verified starts. In comparison with database annotations, we observed that the annotated gene starts deviated from the StartLink+ predictions for ∼5% of genes in AT-rich genomes and for 10-15% of genes in GC-rich genomes on average. The use of StartLink+ has a potential to significantly improve gene start annotation in genomic databases.
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Research Categories
  • Engineering, Biomedical

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