3320 Nucleic Acids Research, 2010, Vol. 38, No. 10 TGA TCC GCG AAC A-30; 50-TCG ATG TTC TCG GAT CAG AAT G-30 and 50-GAT CCA TTC TGA TCC GAG AAC A-30; All plasmids were conﬁrmed by DNA sequencing. Transfection, RNA isolation and RT-PCR ampliﬁcation The 293T cell line was cultured in Dulbecco’s modiﬁed Eagle’s medium (DMEM), supplemented with 4.5 g/ml glucose (Renium) and 10% fetal calf serum (Biological Industries). Cells were grown to 50% conﬂuence in a 10-cm culture dish, under standard conditions, at 37 C with 5% CO2. Cells were split at a 1:8 ratio to 6-well plates 24 h prior to transfection. Transfection was per- formed using 3 ml of FuGENE6 (Roche) with 1 mg of plasmid DNA. Cells were harvested 48 h post-transfection. Total cytoplasmic RNA was extracted using TriReagent (Sigma), followed by treatment with 2 U of RNase–free DNase (Ambion). Reverse transcription (RT) was performed on 2 mg of total cytoplasmic RNA for 1 h at 42 C, using an oligo(dT) and 2 U of reverse transcriptase of avian myeloblastosis virus (RT-AMV, Roche). The spliced cDNA products derived from the expressed minigenes were detected by PCR, using vector-speciﬁc primers: 50-ATC GAT CCT GCA CCT GAC TC-30 and 50-CAG CAT CAG GAG TGG ACA GA-30. Ampliﬁcation was performed for 30 cycles, consist- ing of 94 C for 30 s, 60 C for 45 s and 72 C for 1 min, using ReadyMix (Bio-Lab). The products were resolved on a 2% agarose gel. PCR products were eluted from gel and conﬁrmed by DNA sequencing after puriﬁcation (Wizard, Promega). Every result represents at least three independent experiments. The ratio of exon inclusion to exon skipping was determined using the ImageJ tool. RESULTS Overlapping SR protein putative binding sites may result in competitive inhibition between the two SR proteins for binding to their corresponding sequences. Such competi- tive inhibition might allow a delicate regulation of the splicing process. If this scenario is common, we expected functional overlapping motifs to be more abundant than expected and perhaps over-conserved. In contrast, such competitive inhibition might interfere with the proper identiﬁcation of a nearby exon–intron junction. In these cases, we could expect negative selection to act against such overlapping motifs. The putative binding sites of SR proteins tend to signiﬁcantly overlap To determine whether putative binding sites of SR proteins have a positive or negative tendency to overlap, we examined putative SR-binding sites that had been experimentally identiﬁed by SELEX (31–33); we did not analyze bioinformatically identiﬁed putative elements because were concerned they could be sequence-biased. We developed a method to examine whether two SR putative binding sites that can overlap do so. For example, the putative SRp55-binding sequence TGC AGA may overlap with the putative SF2-binding sequence CAGACGA in the context of TGCAGACGA. Brieﬂy, we calculated the expected frequency of the uniﬁed sequence (TGCAGACGA) in the human exons dataset, given the prevalence of the ﬁrst sequence (TGCAGA) and of the extension (CGA). Estimates were based on the relative frequency of the four nucleotides (A, C, G and T) in each of the possible three phases of the codon (0, 1 and 2). This was done to avoid any bias that may be introduced by protein coding constraints. The expected frequency was then compared with the observed frequency of the uniﬁed sequence and putative binding sites that were signiﬁcantly over-abundant or under-represented were identiﬁed. First, we generated a pool of all possible hexamers, heptamers and octamers previously identiﬁed as putative SR-binding sites using any of the ﬁve available matrices in the ESEﬁnder web server (SRp55, SRp40, SF2/ASF, SF2/ ASF BRCA1 and SC35; using threshold of four to maximize credibility) (34). We then calculated all the the- oretical possibilities for overlap (i.e. all possible uniﬁed sequences, 192 327 cases). Since we were interested in iden- tifying cases of potential interaction/competitive inhibi- tion between two diﬀerent SR proteins, we discarded uniﬁcations that involved two putative binding sites of the same SR protein as such cases might actually reﬂect one longer binding site for a single SR protein (for a separate analysis of these sequences see Supplementary Table S2). Next, we extracted a dataset containing 202 839 human RefSeq exons. We discarded exons for which we could not extract the deﬁnite phase (e.g. exons in intronless genes, exons in the UTR, etc.) and very short or very long exons (top and bottom 0.025%); 150 941 exons remained. We examined the coding sequence of each exon and calculated the prevalence of each of the four nucleotides (A, C, T and G) in each of the three possible codon phases (0, 1 and 2). In the next step, we calculated the expected frequency of all uniﬁed sequences. We examined the exons in the dataset to identify the prevalence of each putative binding site in each of the three phases. For example, the putative SRp55 TGCAGA sequence appeared 1086, 6503, 6046 times in phases 0, 1 and 2, respectively. Using the nucleotide frequency table in each of the phases we then calculated the expected prevalence of the uniﬁed sequence as follows: 2 X phaseS¼0 ObsðSeq1jphaseSÞ ! X Y FreqðNuc½Ej phaseNÞ E¼1 1 ð Þ where phase S is 0, 1, or 2 and Obs(Seq1|phase S) is the prevalence of Seq1 in the speciﬁc phase S; X is the number of extending nucleotides (three in the above example); E ranges from 1 to X, and represents a speciﬁc extending nucleotide (E = 1,2,3 for the extending nucleotides C,G,A, respectively); and Freq(Nuc[E]|phaseN) is the fre- quency of the extending nucleotide in position E, which is in phaseN. After calculating the expected frequency for all possible uniﬁed sequences, we analyzed the dataset of exons to retrieve observed frequencies. For each of the uniﬁed