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ABSTRACTPhylogenomic footprinting is an approach for ab initioidentification of genome-wide regulatory elements in bacterial species based on sequence conservation. The statistical power of the phylogenomic approach depends on the degree of sequence conservation, the length of regulatory elements, and the level of phylogenetic divergence among genomes. Building on an earlier model, we propose a binomial model that uses synonymous tree lengths as neutral expectations for determining the statistical significance of conserved intergenic spacer (IGS) sequences. Simulations show that the binomial model is robust to variations in the value of evolutionary parameters, including base frequencies and the transition-to-transversion ratio. We used the model to search for regulatory sequences in the Lyme disease species group (Borrelia burgdorferisensu lato) using 23 genomes. The model indicates that the currently available set of Borreliagenomes would not yield regulatory sequences shorter than five bases, suggesting that genome sequences of additional B. burgdorferi sensu latospecies are needed. Nevertheless, we show that previously known regulatory elements are indeed strongly conserved in sequence or structure across these Borreliaspecies. Further, we predict with sufficient confidence two new RpoS binding sites, 39 promoters, 19 transcription terminators, 28 noncoding RNAs, and four sets of coregulated genes. These putative cis- and trans-regulatory elements suggest novel, Borrelia-specific mechanisms regulating the transition between the tick and host environments, a key adaptation and virulence mechanism of B. burgdorferi. Alignments of IGS sequences are available on BorreliaBase.org, an online database of orthologous open reading frame (ORF) and IGS sequences in Borrelia.IMPORTANCEWhile bacterial genomes contain mostly protein-coding genes, they also house DNA sequences regulating the expression of these genes. Gene regulatory sequences tend to be conserved during evolution. By sequencing and comparing related genomes, one can therefore identify regulatory sequences in bacteria based on sequence conservation. Here, we describe a statistical framework by which one may determine how many genomes need to be sequenced and at what level of evolutionary relatedness in order to achieve a high level of statistical significance. We applied the framework to Borrelia burgdorferi, the Lyme disease agent, and identified a large number of candidate regulatory sequences, many of which are known to be involved in regulating the phase transition between the tick vector and mammalian hosts.