BMC Genomics 2009, 10:490 http://www.biomedcentral.com/1471-2164/10/490 Conclusion We provide evidence in this study that genes of biological interest in a non-model species, without a sequenced genome, can be discovered by combining re-analysis with a fast and efficient method of finding orthologs between fish and mammals. With the advent of next generation sequencing we envisage this approach will be useful to researchers doing similar studies in other organisms or for other biological processes. Programs using the methods from this work can be downloaded from http:// www.cbrg.ox.ac.uk/~jherbert/ and can be used to run these analyses locally. Methods Bioinformatics analyses - sequence collection and contigs assembly On the 23rd March 2007 the total number of cDNA sequences represented in the CarpBASE 3.0 [102,103] database publicly available for download was 17,825. Additional sequences from other sources were found using Entrez [104] to search Genbank (release 158, Febru- ary 2007) for all carp ESTs and mRNAs. A total of 19,995 EST sequences were collected and clustered using the CAP3 [27] (Version Date: 04/15/05) software to create overlapping contigs of the same carp genes. All the default settings were employed except for a stringent overlap per- cent identity cut-off of 98%. Combining the singleton EST counts with the clustered contigs gave a total 16,650 sequences for use in ortholog searches. All the cDNA accession numbers are in additional file 1 (note: AJ577601 was removed at the author's request) and a key to which sequences were contained in the contiguous sequences is in additional file 2. Unbiased selection for evaluation of results A systematic method of selecting results to analyse was required in this study to counteract any cherry picking bias. This was achieved by choosing random numbers from the RANDOM.ORG website [105], which generates random numbers based on atmospheric noise. These numbers determined the particular results that were looked at, and in most cases, 10 random results were cho- sen. Ortholog identification RBH, SRBH and CSRBH The carp contigs were BLASTX searched with the stand alone NCBI BLAST [106,107] in all six frames against the human Refseq protein database [108]. The best hit was recorded and assigned as a putative ortholog. To increase the quality of the data, a reciprocal TBLASTN was per- formed which took the human ortholog protein and searched back against the carp contigs sequences. This was called the reciprocal best hit (RBH) [23,29,109] and was regarded as successful if the contig found in the RBH search represented the same human protein queried (fig- ure 1, route 1). The default BLAST algorithm uses a heuristic approach where it searches for small words (sequence regions) in the query and the subject that are exactly the same. It then attempts to extend out the matching words until a score threshold is reached to produce longer alignments. The Smith-Waterman algorithm [110], on the other hand, uses a dynamic programming method to produce an opti- mal local alignment [111,112]. This can be utilised in BLAST with -s T [113] options for BLASTP and TBLASTN. Different BLAST filtering performance were tested (-F F, -F "m S", -F T and -s T) on eukaryotic data to compare those results of prokaryotes [29]. All BLAST searches employed an e-value of 1e-5 as done by Woods et al. 2005 [45] All orthologs that failed this step were removed. A Stepped Reciprocal Best Hit (SRBH) was a two-stepped process where an RBH between carp and zebrafish was first performed and then for the successes, a second RBH was carried out between zebrafish and human. Only those sequences successful for both RBH searches were assigned orthologs (figure 1, route 2). Finally, a Conditional Stepped Reciprocal Best Hit (CSRBH) approach was performed using a Perl program to post-process BLAST results. Carp sequences were BLAST searched against both human and zebrafish Refseq pro- teins and the best hit determined the route to ortholog identification (figure 1). Angiogenic genes Four different methods were used to identify which genes are, or potentially are, angiogenic: 1) The in-house method involved collecting genes based on the authors own angiogenesis research, combined with genes on the SABiosciences [52] commercial angiogenic array and those listed in the literature [51]. 2) Gene data was downloaded from Genbank [114] and Perl programs, in conjunction with PubMed [53], were used to find article abstracts that contained one or more of the following angiogenic keywords: angiogenic, angio- genesis, neovascularization, neovascularisation, vasculo- genesis, hypoxia, endoth and VEGF. 3) Similarly, Gene Ontology data was downloaded from Genbank [114] and Perl programs were used to search the Gene Ontology process data for each protein. The same keywords were used as in the previous method. 4) Any genes differentially or specifically expressed in ECs are potential angiogenic genes. Therefore, an in-silico pre- Page 12 of 17 (page number not for citation purposes)