Abstract: With the advent of larger genome databases detection of horizontal
gene transfer events has been transformed into an increasingly important issue.
Here we present a simple theoretical analysis based on the in silico artificial
addition of known foreign genes from different prokaryotic groups into the
genome of Escherichia coli K12 MG1655. Using this dataset as a control, we have
tested the efficiency of four methodologies commonly employed to detect HTG
(Horizontally transferred genes), which are based on (a) the codon adaptation
index, codon usage, and GC percentage (CAI/GC); (b) a distributional profile
(DP) approach made by a gene search in the closely related phylogenetic
genomes; (c) a Bayesian model (BM); and (d) a first-order Markov model (MM).
All methods exhibit limitations although, as shown here, the BM and the MM are
better approximations. Moreover, the MM has demonstrated a more accurate rate
of detections when genes from closely related organisms are evaluated. The
application of the MM to detect recently transferred genes in the genomes of E.
coli strains K12 MG1655, O157 EDL933, and Salmonella typhimurium, shows that
these organisms have undergone a rather significant amount of HTG, most of
which appear to be pseudogenes. Few of these sequences that have undergone HGT
appear to have well defined functions and may be involved in the organism's
adaptation.
Keywords: Horizontally transferred gene, methodologies to detect HTG, first-order Markov model
