Summary
A bioRxiv preprint introduces ProFinder, a computational tool that ranks candidate constitutive promoters in bacterial genomes. The authors report generating candidates for 99.76% of 56,742 genomes spanning 149 phyla.
A bioRxiv preprint introduces ProFinder, a computational tool that ranks candidate constitutive promoters in bacterial genomes. Its authors report that the method generated candidates for 99.76% of 56,742 genomes spanning 149 phyla, offering a way to identify possible genetic control sequences in bacteria that are difficult to study.
How ProFinder finds candidate promoters
A promoter is a stretch of DNA where cellular machinery begins transcribing a gene into RNA. A constitutive promoter is intended to support gene expression without being switched on only by a particular signal. Such promoters can help researchers build genetic tools, but sequences that work in one bacterial group may not work as well in a distantly related species.
ProFinder searches bacterial genome assemblies using three criteria described in the preprint: a panel of 98 conserved genes associated with constitutive activity, an empirically derived window for searching intergenic DNA, and a catalogue of 72 σ70 promoter motifs drawn from diverse bacterial lineages. Intergenic DNA is the sequence between genes. The σ70 factor is part of a common bacterial transcription system that helps RNA polymerase recognise promoter sequences.
Combining these signals, the program returns a ranked shortlist of putative promoters rather than a single guaranteed functional sequence. The authors say this design addresses weaknesses in existing computational predictors, which they describe as being trained on model Gammaproteobacteria and performing less well on phylogenetically distant or compositionally unusual genomes.
Results across bacterial genomes
The researchers applied ProFinder to 56,742 bacterial genomes representing 149 phyla. It returned candidate promoters for 99.76% of the genomes. The authors also report that its precision matched that of full-genome classifiers and that it was more robust at extreme GC content. GC content is the proportion of DNA bases that are guanine or cytosine; it varies substantially among bacteria and can affect sequence-based predictions.
The resulting catalogue is intended to make candidate promoter parts easier to find when developing genetic tools for non-model bacteria. The authors provide access to ProFinder through the University of Tübingen’s Plabase site.
These are computational predictions: the preprint abstract describes performance comparisons and genome-scale candidate generation, while the returned sequences are explicitly putative. Promoter activity still depends on how a sequence functions in its bacterial host, so the catalogue is best understood as a shortlist for further evaluation rather than a collection of already established working parts.