PIRT-Seq: a high-resolution whole-genome assay to identify protein-coding genes

The advent of high-density mutagenesis and data-mining studies suggest the existence of further coding potential within bacterial genomes. Small or overlapping genes are prevalent across all domains of life but are often overlooked for annotation and function because of challenges in their detection. To overcome limitations in existing protein detection methods, we applied a genetics-based approach. We combined transposon insertion sequencing using a dual-selection transposon with a translation reporter to identify translated open reading frames throughout the genome at scale but independent of genome annotation. We applied our method to the well-characterised species Escherichia coli. This method revealed over 200 putative novel protein coding sequences (CDS). These are mostly short CDSs (<50 amino acids) and include proteins that are highly conserved and neighbour functionally important genes. Using chromosomal tags, we validated the expression of selected CDSs. We present this method (Protein Identification through Reporter Transposon-Sequencing: PIRT-Seq) as a complementary method to whole cell proteomics and ribosome trapping for condition-dependent identification of protein CDSs, and as a high-throughput method for testing conditional gene expression. We anticipate this technique will be a starting point for future high-throughput genetics investigations to determine the existence of unannotated genes in multiple bacterial species.

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Publication Details

Journal
Nucleic Acids Research
Published
2025-07-30
DOI
https://doi.org/10.1093/nar/gkaf774
Citations
1
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
Field-Weighted Citation Impact
0.81
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article

PIRT-Seq: a high-resolution whole-genome assay to identify protein-coding genes

Emily C. A. Goodall, Zihao Yang, Budi Permana, Antoine Delhaye et al.
1 citations
Nucleic Acids Research
Genetics, Bioinformatics, and Biomedical Research
0.81
article

PIRT-Seq: a high-resolution whole-genome assay to identify protein-coding genes

Emily C. A. Goodall, Zihao Yang, Budi Permana, Antoine Delhaye, Brian M. Forde, Ian R. Henderson, Thom Cuddihy, Jack Alfred Bryant, Weine Kok, Jessica L. Rooke, Von Vergel L Torres, Karthik Pullela, Matthew R. Hemm, Freya J Hodges, Jean‐François Collet, Nicole Kahler, Kenneth Shires
article en
1 citations

Abstract

The advent of high-density mutagenesis and data-mining studies suggest the existence of further coding potential within bacterial genomes. Small or overlapping genes are prevalent across all domains of life but are often overlooked for annotation and function because of challenges in their detection. To overcome limitations in existing protein detection methods, we applied a genetics-based approach. We combined transposon insertion sequencing using a dual-selection transposon with a translation reporter to identify translated open reading frames throughout the genome at scale but independent of genome annotation. We applied our method to the well-characterised species Escherichia coli. This method revealed over 200 putative novel protein coding sequences (CDS). These are mostly short CDSs (<50 amino acids) and include proteins that are highly conserved and neighbour functionally important genes. Using chromosomal tags, we validated the expression of selected CDSs. We present this method (Protein Identification through Reporter Transposon-Sequencing: PIRT-Seq) as a complementary method to whole cell proteomics and ribosome trapping for condition-dependent identification of protein CDSs, and as a high-throughput method for testing conditional gene expression. We anticipate this technique will be a starting point for future high-throughput genetics investigations to determine the existence of unannotated genes in multiple bacterial species.

Nucleic Acids ResearchVol. 53(15)
University of Nottingham (GB), The University of Queensland (AU), University of Exeter (GB), Sustainability Institute (ZA), de Duve Institute (BE), Towson University (US)
Openalex Percentile: Top 25%
Genetics, Bioinformatics, and Biomedical Research
0.81
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