BMC Genomics 2006, 7:73 http://www.biomedcentral.com/1471-2164/7/73 SFpigacuerrein8teraction network Spacer interaction network. Spacer interaction network highlighting promiscuity within the set of self-recombining spacers (red) and the set of non-self recombining spacers (green). The recombination reaction A 20 µL in vitro recombination reaction was set up with approximately 300 ng of supercoiled LE/pUC19 plasmid, 30 ng of double stranded RE PCR product, 200 ng of CRE Recombinase (BD Bioscience) and 1x (final concentra- tion) Cre Recombinase Buffer (BD Biosciences). The reac- tion was incubated overnight at room temperature and the desired 1.8 kbp recombination products were agarose gel purified and amplified by PCR using the RE_NotI_Forward and RE_NotI_Reverse primers (Table 2). Sequencing recombination reaction products The amplified recombination products were digested with NotI, agarose gel purified and re-circularized with T4 DNA ligase to generate a library of paired loxP recombination products in pUC19 vector. A total of 5,670 clones were sequenced with M13 Reverse sequencing primer using previously described methods [21]. Vector sequences were removed and reads were quality trimmed to the Q20 standard [26]. Identifying successful recombinants Sequence data was analyzed using Perl v5.6.1 and MySQL v10.8 Distribution 3.23.21-beta (for pc-linux-gnu). A Perl script termed pair_count4.pl (See Additional file 3) iden- tified recombinants within sequence traces that contained two loxP sites and applied the recombination model (Fig- ure 3) to infer the sequence of the input oligonucleotides. Successful recombinants contained serial loxP sites with one containing the lox66 a nd lox71 inverted repeat mutants and another with the wild-type inverted repeats. The inverted repeats flanked an 8 bp spacer region. All analysis details and scripts are provided online (see Addi- tional file 5). Properties of recombinant spacers Spacer sequences from the sequencing reads and the inferred input sequences were stored in a MySQL database (see Additional file 5). From these tables, all unique input spacers were identified. For each unique spacer, the number of self-recombining reactions, non-self recom- bining reactions, and total interactions were stored in MySQL tables. All successful recombinant spacer sequences were used to build a sequence logo represent- ing the preferred nucleotide for Cre recombinase at each position in the spacer. A local Java implementation of the "sequence logo" algorithm [24] was used to generate a graphic representing the Cre recombinase binding site profile. Information content of each base is defined as I = sum × frequency (nt) × log(2) (4 × frequency (nt)). There- fore the height (H) of each base in the logo is: H = fre- quency (nt) × I. The effect of varying bases at each particular position in the spacer was evaluated by a Perl script that calculated the percent of the maximum recom- bination achieved by each polymorphism (observed/ total) (see Additional file 5). This was calculated for all single nucleotide combinations flanking the axis of sym- metry (positions 4,5) that were present in our data. Cyto- scape, a software package for visualizing biomolecular interaction networks [25], was used to map out the pro- miscuity profile of each self-and non-self recombining spacer. Authors' contributions PIM conceived of the study, contributed to study design, performed bioinformatics and statistical analysis of the data and wrote the manuscript. DES contributed to study design and performed high throughput sequencing. RAH contributed to study design, performed the in vitro recom- bination and library construction work, provided direc- tion to data analysis, and edited the manuscript. All authors approved the final manuscript. Page 12 of 13 (page number not for citation purposes)