BMC Genomics 2008, 9:122 http://www.biomedcentral.com/1471-2164/9/122 ties of the conservation filter. The term 'tORF' is used here to collectively refer to tORFM and tORFX. the stop codon, which can have a significant effect on effi- ciency of termination of translation [9]. This early stage in the analysis, after tORF extraction but before the conservation filter, already yielded surprising results: tORFs beginning with methionine (tORFM) were found to be ~10-fold rarer in frame -1 than in frame +1 (Fig. 3A). This effect was also seen for tORFx, but in a much less pronounced way (Fig. 3B). The pattern was mir- rored by human codon usage frequencies (Fig. 3C): stop codons are more frequent in frame -1 than frame +1, resulting in fewer frame -1 tORFs. In addition, ATG codons are ~5-fold rarer in frame -1 compared to +1, accounting further for the observed scarcity of frame -1 tORFM. In the ARF study [8], the difference between the two frames was echoed in simulated alignments. The importance of this apparent suppression of tORFM may be part of a mechanism to minimize the impact of transla- tion initiation errors. Why there should be a difference between frames +1 and -1, however, is unclear. Perhaps an alternative mechanism already exists to prevent erroneous frame +1 translation initiation. Examination of amino acid sequences in the parent frame sheds some light on how stop codons can occur in other frames. If, for instance, a tyrosine (codons TAT or TAC) is followed by an aspartic acid (codons GAT or GAC), the stop codon TGA occurs in frame -1 if, and only if, the codon used for tyrosine is TAT. Overall, codon TAT is used for tyrosine in 45% of the cases in human genes. However, when tyrosine is directly followed by aspartic acid in a coding sequence, TAT occurs in 75% of the cases to code for tyrosine (Table 1). Thus, the codon which leads to a stop codon in frame -1 is favoured. In order to explore whether this observation can be gener- alized, we calculated a 64 by 64 bi-codon usage matrix for a set of human coding sequences (see Additional file 1). Table 1 shows as an example the combinations of two consecutive amino acids (bi-codons), where the second amino acid is aspartic acid (both codons for aspartate begin with AG, therefore the occurrence of a stop codon in frame -1 depends entirely on the first codon). Interest- ingly, there is a trend for codons to be favored which lead to a stop codon in the frame -1, in some cases leading to highly significant probability values (Table 1). The same effect can be observed when the second amino acid in the bi-codon is glutamic acid (Additional file 1). In contrast, if the second amino acid is lysine or asparagine, the oppo- site trend appears (Additional file 1). This may be due to interacting effects on the complementary nucleotide strand, and illustrates the complexity of bi-codon effects on the appearance of stop codons in frame -1. Note that our analysis did not take into account the base following Conservation filter Having extracted all tORFs, the conservation filter was then applied. Within each ortholog triplet, all human frame +1 and -1 tORFs were compared in pairwise global alignments to all those computed from mouse and from rat. Well-conserved tORF triplets which passed the length similarity and identity criteria were retained for further analysis as candidate matreshkas. Matreshkas derived from tORFM are denoted here as matreshkaM, those derived from tORFX are denoted matreshkaX, while a lack of subscript refers to both matreshka sets collectively. The breakdown of tORF and matreshka statistics by frame is given in Table 2. Of the 7679 matreshkaX (beginning with any amino acid), 1853 are at least partially redundant with the matreshkaM (beginning with methionine only), of which there are 1793 in total. The matreshkaM total (1793) is smaller than the number of redundant matreshkaX (1853), because the longer matreshkaX can contain several of the shorter matreshkaM. Frame -1 tORFs were filtered out more effec- tively than frame +1 (Table 2): the proportion of frame -1 tORFX dropped from 45% to 25% of the total after conser- vation filtering, and from 9% to 6% for tORFM. The greater stringency of the conservation filter on frame -1 tORFs was expected, and is a consequence of the variation in the third base of each parent codon: this can be demon- strated in a simple exercise by combining all possible nucleotide triplet pairs. For a given amino acid in frame 0, on average ~5 different amino acids can potentially be coded for in frame +1, while ~15 can be coded for in frame -1 (data not shown). The scarcity of frame -1 tORFM, combined with the greater efficacy of the conser- vation filter, resulted in very few (104) matreshkaM in frame -1. For reference, Alex lies in frame +1. No matreshkas were found corresponding to Alex/XL- alpha-s [4], INK4A/ARF [1] or 4E-BP3/MASK [2] because they were not represented in the ortholog triplets (prima- rily because a RefSeq sequence was not available for all three organisms, human, mouse and rat) and in the case of IGF1 [3] because the region which is translated in two frames is short (16aa, the matreshka length filter being 50aa). As recovery of Alex-like entities but not Alex itself was our primary goal, we used stringent conservation fil- ters that Alex could not have passed: human Alex is only 53% and 55% identical at the amino acid level to mouse and rat Alex, respectively [4], while our conservation threshold was set at 60%. This relatively high threshold was necessary partly because the exploratory nature of the project made clear-cut examples necessary, but was also based on separate benchmark studies we made on the Page 4 of 16 (page number not for citation purposes)