Table 2. Length characteristics (average, standard deviation, and range) and experimental data (MS peptides, MS coverage, EST alignments, MS AND EST data), for sequence of each prediction type. Total Number of Sequences Length Characteristics Sequence Length, average6stdev Sequence Length, range Experimental Data Sequences with Assigned MS peptides (% of dataset) Average sequence coverage of MS peptides Sequences with Filtered EST alignments (% of dataset) Sequences with MS peptides AND EST (% of dataset) doi:10.1371/journal.pone.0003899.t002 Predicted Protein Types TigrScan 8,336 6816733 20–9,696 17% 10.7% 58% 15% TwinScan 7,588 6146743 2–13,936 19% 13.4% 62% 17% Glimmer 4,954 1,07761,168 23–14,514 27% 8.6% 75% 25% aligned sequences. This approach can group together sequences with very different lengths if the overlapping parts share more than 90% sequence identity. This clustering strategy is an obvious way to assess redundancy among the datasets, but it is also a way to examine potential splicing variability within the genome. Alterna- tive splicing events such as alternative 59 or 39 exon splice sites, skipped exons, or retained introns result in sequences that are splice variants of the same gene. Although the sequences are different, the local similarities allow them to be clustered together (Fig. 2a–c). Clustering by sequence similarity allows for an interpretation of a comprehensive set of genes and open reading frames that are predicted by various gene finder algorithms. A cluster of similar sequences (or a single sequence) is representative of a predicted gene at the genomic locus despite the fact that there may be some discrepancy in the exact structure of the gene in question due to differences in the predicted sequences. This method of clustering by local sequence similarity is a distinct analysis compared to the evaluation of identical sequence overlap produced by the gene finder algorithms. Additionally, the local sequence-similarity clustering, which encompasses all subsequent clustering analysis, was performed on the redundant set of 30,197 protein sequences because the ensuing statistics regarding the differences among the prediction methods would be artificially affected if some sequences were removed from the dataset in cases in which two or more prediction algorithms produced identical sequences. The hypothetical proteome of 30,197 sequences collapses to 14,983 non-redundant clusters with an average size of 2.02 sequences. The majority of the clusters (55%) are individual sequences (Table 4), strengthening the observation that there are many unique sequences that do not share even a common sub- sequence with other proteins and that the predicted protein datasets provide remarkably different alternatives. If the 8,281 unique sequences (singleton clusters) are removed from the original combined dataset, there are 21,916 remaining sequences that are clustered with at least one other sequence i.e., about 73% of the combined dataset is redundant, based on sequence similarity. The elimination of the singleton clusters was performed only for the aforementioned evaluation of the degree of redundancy in the dataset. The singleton clusters were included in all other analyses. The percentages of the clusters that contain a TigrScan, TwinScan or Release4 sequence are similar (53%, 47%, and 49%, respectively), while the percentages that contain a Glimmer or NR sequence are much lower (33% and 6% respectively) (Table 5). The differences for the Glimmer predictions and the NR sequences could be explained by the fact that these datasets, especially the NR, are significantly smaller then the datasets for the other three prediction methods. The small fractions of common protein predictions suggest that any single prediction dataset either covers a small portion of the theoretical T. gondii genome (,50% or less) and/or produces a very large percentage of false positives. Table 3. Number of identical sequence groups containing one, two, three, or all four types (TigrScan, TwinScan, Glimmer, Release4) of protein prediction methods. Validation of Gene Predictions Release4 7,793 7196803 50–11,862 20% 12.5% 72% 18% NR 1,526 5106676 1–12,269 49% 29.9% 85% 48% Overlap Among Prediction Methods All (4) Prediction Methods Agree (Tigr,Twin,Glimmer,Release4) Three Prediction Methods Agree Two Prediction Methods Agree Single Prediction Method doi:10.1371/journal.pone.0003899.t003 Number Of Identical Sequence Groups 101 521 2,058 22,556 unique Tigr = 7,245 unique Twin = 5,778 unique Glimmer = 4,252 unique Release4 = 5,281 PLoS ONE | www.plosone.org 4 December 2008 | Volume 3 | Issue 12 | e3899