Validation of Gene Predictions Figure 1. Frequency distribution of sequence lengths (amino acid residues per sequence) for TigrScan (dark blue), TwinScan (green), Glimmer (red), Release4 (light blue), and NR (grey) sequences. The frequencies are normalized to the total number of sequences in each dataset. The large graph shows the distributions for sequence lengths of 0–3,000 amino acids. The inset shows the same distributions but for sequence lengths of 0–500 amino acids. doi:10.1371/journal.pone.0003899.g001 representative sample of the T. gondii proteome. Rather, the predominance of shorter sequences could be a consequence of the greater ease of studying these proteins experimentally, especially in the past. The substantial differences in the length characteristics of the predicted proteins suggest that the algorithms either predict sequences from vastly different genes and coding regions or that there are significant disparities in the prediction of the structure and splicing of the exons and introns within similar gene coding regions. Comparing protein sequences obtained from different prediction methods Identical or nearly identical proteins in the hypothetical proteome were grouped in order to explore the overlap and differences among the computational gene prediction methods. Each gene finder was employed on the same genome and, consequently, the four methods ideally should have predicted identical genes. Of course, conversely, the algorithms may have also predicted different splicing among predicted exons of the same open reading frame, predicted different open reading frame initiation or termination points, or added and/or missed entire open reading frames, thus generating some vastly different proteins. If each gene prediction method (TigrScan, TwinScan, Glimmer, and Release4) produced the same predicted sequence, those sequences would be clustered together. Grouping identical proteins in the combined dataset results in 25,908 clusters (86% of the original set), of which, only 101 contain predicted sequences from all four methods (Table 3). The numbers of identical sequence groups with a sequence from one (22,556 groups), two (2,058), three (521), or all four (101) prediction methods confirms the observation that the gene prediction methods produce very different results, with many unique sequences for each prediction method. The percentage of unique sequences that is produced by each prediction method is roughly similar for the TigrScan, TwinScan, and Glimmer methods (76–87% of all predictions), with Release4 having a slightly smaller percentage of unique sequences (68%) (Table 3). In general, any two prediction methods share less than 12% (and as low as 1.4%) identical predicted genes in a head-to-head comparison. The above analysis does not insinuate that any one gene finder algorithm is superior to the others. However, the results provide an initial basis for the effort of examining all available sequences from each computational annotation of the genome when examining the proteome. Clustering predicted proteins by sequence similarity Discrepancies in protein sequences that are transcribed from similar genomic locations, whether the result of prediction differences or splicing variability, likely account for most of the disparities among the protein datasets. Therefore, in order to assess the fraction of sequences that are not identical yet share a common part and, thus, may have been derived from the same genomic location, the hypothetical proteome of 30,197 sequences was clustered with a 90% sequence identity threshold requirement. The clusters were constructed by aligning each sequence with the longest sequence in a cluster, which is called the ‘‘representative sequence’’ (see Methods), while allowing large gaps in the alignment. The number of identical amino acid residues within regions of sequence overlap was normalized over the length of the shorter sequence to determine the sequence identity between the PLoS ONE | www.plosone.org 3 December 2008 | Volume 3 | Issue 12 | e3899