Bosi et al. BMC Genomics 2011, 12:403 http://www.biomedcentral.com/1471-2164/12/403 analyzed in this work (together with other information such as their size, their accession codes etc.) is available as Additional file 1. Atypical genes detection Classically, atypical genes detection has been pursued either by i) phylogenetic methods based on sequence alignment) and/or ii) by composition-based methods that involve alignment-free features such as GC% con- tent, synonymous codon usage or the frequencies of overlapping short oligomers [16]. Several in silico based methods have been conceived in the past few years to identify foreign genes that were recently acquired by chromosomes [1,17-25]. However, it is not possible to discriminate between plasmids atypical or native genes using phylogeny-oriented methods since a set of homo- logous and universally shared sequences (necessary to build a reference phylogeny) are often unavailable. Furthermore, the absence of a universally shared “core” of genes (i.e. likely not subject to extensive HGT) when analysing plasmids sequences does not allow the use of classical statistics methods (such as Markov model- based approaches), which require the presence of a set of native genes in order to identify those ORFs that have a signifcant different composition. Hence, in this work, we have developed a computational strategy (Fig- ure 1) that combines two compositional measures (GC content difference and δ*) in order to identify putative PAGs within a dataset of 1853 plasmids. Briefly, GC content is calculated as (G+C)/(A+T+G+C), where G, C, A and T is the number of guanines, cytosine, adenines and thymines, respectively. Conversely, δ* between two sequences a and b (from different organisms or from different regions of the same genome) is calculated as δ* (a,b) = (1/16)∑XY|r*XY(a)−r*XY(b)| where the sum extends over all possible XY dinucleotides and where rXY = fXY /fXfY, with fXY representing the frequency of the dinucleotide XY in the genome. Hence, for each of the 128.569 ORFs of the 1853 plas- mids (Figure 1a), we estimated both the δ* and the GC content difference (ΔGC) in respect to the correspond- ing source plasmid (Figure 1b and 1c). Since the distri- butions of these values did not follow a normal distribution (according to Kolmogorov-Smirnov test with a p-value threshold of 0.05), we used a distribu- tion-independent procedure to evaluate the probability of each point of these distributions. performing a boot- strap sampling (Figure 1d and 1e) of all the obtained values. In other words, a probability was assigned to each of the values [e.g. P(A)] of these two distributions computing it as P(A) = n(A)/N, where n(A) is the num- ber of times in which the observed value (of ΔGC and δ*, respectively) was greater than the other (128.569) values after N samplings. By doing so, two distinct p- b d GC% GC% distribution f p-value Plasmid 1 ... 1853 a ORF 1 ... ORF n vs. Plasmid 1 Page 3 of 12 c e * * distribution g p-value h Fisher’s method C.I. < 95% C.I. > 95% no PAG PAG Figure 1 Applied strategy. The overall strategy adopted in this work for the identification of plasmids atypical genes (PAGs). values (Figure 1f and 1g) were associated to each sequence of the dataset: the first accounting for the probability of a gene to be atypical in terms of δ* and the other accounting for the probability of a gene to be atypical in terms of ΔGC. Further on, these two distinct p-values were integrated in a single one according to the Fisher method [26]. In its basic form, the Fisher method is used to combine the results from two (or more) tests bearing upon the same overall hypoth- esis. In other words, Fisher’s method combines p-values into one test statistic (X2) using the formula: X2 = -2∑loge(pi), where pi is the p-value for the ith hypothesis test. Accordingly, (Figure 1h) only those sequences iden- tified at a confidence interval (CI) greater than 95% were considered PAGs. Moreover, in order to explore differ- ent CI thresholds, we also collected gene sets that were identified as atypical with lower confidence values (i.e. 70%, 80% and 90%). The whole pipeline has been imple- mented in Perl codes and is available upon request. As it might be expected, lower CI thresholds allowed the