BMC Genomics 2009, 10:490 http://www.biomedcentral.com/1471-2164/10/490 blastpgp can only be used on protein sequences and not nucleotides. 4) Some cDNAs span genes on different chromosome strands and is another way RBH results could be affected (E.g. cDNA DY691296). However, even with these differences, the results were very surprising because of other research [36] and considering the -s F option is a fast approximation of the SW algorithm (see methods for details). One thing to bear in mind is an e-value of 1e-5 and soft filtering were employed in this work, which in itself, would remove many spurious matches otherwise found using the default BLAST heuris- tic. A further investigation into this revealed that the genes successfully found with each method did not totally over- lap. The heuristic BLAST alignment method predicted 62 functional homologs with RBH that SW did not find and the SW alignment method predicted 63 that failed with the BLAST heuristic. The intersection of results totally con- curred. The questions to answer here were; why did one algorithm fail RBH, whilst the other method was success- ful? And of these differences, which algorithm produced the more believably evolutionary related RBH successes? To investigate this finding further and to decide on which method to use in this work, a manual inspection of sequence alignments was needed in a systematic way to avoid the problem of biased cherry picking. This was done using a random number generator to pick 12 sequences to investigate (see methods), 6 from each algorithm RBH success. So the investigation was to see if the results were believable for each algorithm. The results found that ~50% (additional file 3) of results suggested the default BLAST option found the more likely evolutionary related sequence as it found higher similar matches but at shorter alignment lengths, which is believable as SW is good at finding distant homology. However, this was not always the case and the different sequence alignment algorithms were correct for different sequences. As a consequence it was not possible to automate the choice without manu- ally viewing the results. Therefore, in this work the results generated using the default BLAST heuristic was used for the final data. However, it is recommended for other researchers using these methods on small datasets to use the intersection of both methods or the SW alone (based on previous research [36]). This is not a problem for small numbers of sequences but for Next Generation Sequence data, for example, it is recommended the default heuristic be used as it will take weeks to compute SW alignments. All but one cold induced angiogenic gene were found with both algorithms and users of this data can be confident in the ortholog assignments of these genes. The exception was the plasminogen gene PLG (cDNA CA965299). Taking advantage of a closely related vertebrate Zebrafish are a model teleost organism used in the eluci- dation of vertebrate development, molecular, genetic, genomic and evolutionary biology [37]. As such its genome has been sequenced with extensive annotation and currently in the sequence databases there are over 6 million ESTs and 28 thousand Refseq proteins. Carp, on the other hand, currently has ~20 thousand ESTs and no genome sequence. It was possible to take advantage of this annotation because of the close evolutionary relationship carp has to zebrafish in that they diverged from their last common ancestor only 50 million years ago [38]. Previ- ous groups have shown that close genome evolutionary relationships can aid ortholog identification [39,40]. In the present study, zebrafish was also utilised as a stepping stone approach to ortholog identification. Figure 1, labelled route 2, shows a Stepped Reciprocal Best Hit (SRBH) approach where RBH analysis is carried out twice, once between carp and zebrafish and then between zebrafish and human. If both were successful, then a carp- human ortholog was assigned. Using the SRBH analysis gained a further 8% (8,145 sequences) successfully assigned orthologs. Teleost genome duplication and SRBH Although there is a discrepancy as to when it happened, there is much evidence for a genome duplication event in teleosts [22,41-47]. A good example in zebrafish is the paralogs Ets1 and Etsrp which both lie next to fli1a and fli1b, respectively, but on different chromosomes. These genes are important for vasculogenesis and angiogenesis [48,49]. From a SRBH analysis, zebrafish genes ets1 and etsrp best match the human gene ETS1 and fli1a, and fli1b best matches the human FLI1, suggesting a genome dupli- cation event occurred in zebrafish. Because of duplicate genes, the best and second best hits to zebrafish and carp genes were used to attain a successful SRBH. The impact of genome duplication in ortholog searching can be seen in the following example. The zebrafish peptide NP_958883 was the best hit to CA969258. However, the peptide NP_958883 best matched human NP_003002. Blasting NP_003002 back against zebrafish revealed two very sim- ilar sequences, NP_958876 and NP_958883. NP_958876 was the best hit and therefore failed SRBH (additional file 4). This is due to genome duplication of the zebrafish genome and thus both genes in zebrafish can be consid- ered orthologs to the same human gene. RBH and a comparison with Homologene Homologene is a database of orthologous groups defined with BLAST pairwise alignments and phylogenetic trees [32,33]. Since CSRBH included a zebrafish and human ortholog RBH analysis, a comparison was made between Homologene and this RBH step to see if the zebrafish to human ortholog assignments from this work agreed. Page 5 of 17 (page number not for citation purposes)