A subset of the COG database (database of orthologous protein sequences) (Tatusov, et al., 2001) was used. 117 orthologous groups (COGs) were selected that contained at least 8 eukaryotic and 16 prokaryotic sequences.

An all against all comparison was carried out using BLAST (Altschul, et al., 1990) version 2.2.13 downloaded from http://www.ncbi.nlm.nih.gov/BLAST/download.shtml The BLOSUM62 matrix was used with a gap opening penalty of 11 and a gap extension penalty of 1 (default parameters). The results were stored in a compressed, tab-delimited ASCII file.

An all against all comparison was carried out using the Smith-Waterman algorithm (Smith and Waterman, 1981) as implemented in the water program of EMBOSS (Rice, et al., 2000). The program was downloaded from ftp://ftp.bioinformatics.org/pub/biobrew/. The BLOSUM62 matrix was used with a gap opening penalty of 10 and a gap extension penalty of 0.5 (default parameters). The results were stored in a compressed, tab-delimited ASCII file.

An all against all comparision was carried out using the Needleman-Wunsch algorithm (Needleman and Wunsch, 1970) as implemented in the needle program of EMBOSS (Rice, et al., 2000). The program was downloaded from ftp://ftp.bioinformatics.org/pub/biobrew/. The BLOSUM62 matrix was used with a gap opening penalty of 10 and a gap extension penalty of 0.5 (default). The results were stored in a compressed, tab-delimited ASCII file.

The Local Alignment Kernel program version 0.3 of Saigo and associates (Saigo, et al., 2004) was downloaded from http://cg.ensmp.fr/~vert/. The following run parameters were used: Default comparison matrix found in the parameters.h file. Gap opening penalty = 11 (default), Gap extension penalty = 1 (default), Scaling parameter = 0.5.

Nearest neighbour (1NN) classification is a technique whereby a query sequence is assigned to the a priori known class of the database entry that was found most similar to it in terms of a distance/similarity measure (for an introduction see Duda, et al., 2001).

The evaluation of classification performance was carried out by the standard receiver operator characteristic (ROC) analysis (for an introduction see (Duda, et al., 2000)). This method is designed to test the ranking ability of a given classifier based on a real-valued ranking parameter. In the case of nearest neighbour classification, the ranking parameter was a similarity/distance parameter calculated between an object and the nearest member of the positive training set (outlier detection). Briefly, the analysis is carried out by plotting sensitivity vs 1-specificity at various threshold values, then the resulting curve is integrated to give an “area under curve” or AUC value. These values are determined for each classification experiment. For a perfect ranking, AUC=1.0, for random ranking AUC=0.5 (Egan, 1975).