prediction methods. These data show that none of the full genome predictions manages to identify all of the proteins that are supported by MS/MS data; each prediction method is missing 31 to 43% of the validated part of the proteome. In these calculations we assumed that if there is at least one proteolytic peptide that matches a sequence in a cluster, then all sequences are validated. However a proteolytic peptide may not have an overlap with all sequences in a cluster (Fig. 2a–c). Nevertheless for each type of protein, due to the relatively dense coverage of MS peptides in each cluster, a sequence that was present within an MS/MS supported cluster almost always had a peptide mapped directly to the sequence ($90% of the occurrences of a sequence within the MS/MS supported cluster). This indicates that there is exper- imental evidence to support each sequence within the cluster, despite differences in splicing and/or predictions among the clustered sequences. According to the various prediction algorithms, the T. gondii genome is expected to encode approximately 7,800 genes [26,27], of which 18% were estimated to be life stage specific (,1,400) [28]. This suggests that our high throughput MS/MS experiments explored at least one third of the genome of T. gondii tachyzoites (2,477 clusters). This may be an underestimate if a significant fraction of our clusters represent two or more gene products. EST mapping analysis of the hypothetical T. gondii proteome offers similar conclusions to what were generated from the MS/ MS analysis. It was possible to validate 20,123 sequences (67% of the combined dataset) with an aligned EST sequence, which corresponds to 9,242 clusters (62% of the total clusters). The TigrScan and TwinScan datasets have roughly the same percentages of sequences that are supported with an EST alignment (58% and 62%, respectively) while the Glimmer and Release4 methods have similar and higher percentages of sequences with an EST alignment (75% and 72%, respectively). The enrichment of Glimmer sequences with an EST alignment is likely a result of the Glimmer sequences being, on average, much longer then the TigrScan, TwinScan, or NR sequences. Thus, there is a greater chance that a Glimmer sequence will cover part of the genome that is also sequenced by an EST. The high percentage of Release4 sequences with an EST alignment is possibly the result of ESTs (or other genomic data) being included in the integrated data that was used to derive the Release4 sequences (Table 2). The percentages of the EST supported clusters that contain TigrScan, TwinScan, or Release4 sequences are similar (54%, 52%, and 61%, respectively) whereas the percentage that contains a Glimmer sequence is lower (41%) (Table 5). A vast majority ($96%) of the sequences within the EST supported clusters have an EST alignment, again indicating that each sequence, within the cluster, has experimental support despite possible splicing or prediction differences. The EST alignments were filtered based on requirements for minimum alignment length and sequence identity in order to differentiate cases in which the amino acid sequences were supported by a strong alignment from the cases in which the alignment indicated a paralog rather than a direct validation of the sequence by genomic data. Without filtering, there are 21,989 sequences that have EST alignments, which is only 9.3% more than the number of sequences with filtered EST alignments (20,123 sequences) and which represents only a 6% increase in the percentage of the hypothetical proteome that is supported by experimental EST evidence. The consistency in the number of amino acid sequences with filtered and non-filtered EST alignments indicates that, within the subset of sequences that are Validation of Gene Predictions supported by EST data, there are strong similarities between the protein sequences and the EST sequences. The MS proteomics data were cross-referenced with the EST genomics data. There are 5,881 sequences (19.5% of the combined dataset) that are experimentally supported by both an assigned MS/MS peptide and a filtered EST alignment, which one might consider to be the most highly validated proteins in the compiled proteome. The sequences with both types of exper- imental data correspond to 2,275 clusters (15.2% of the total clusters). The average MS/MS peptide coverage of sequences that are supported by MS and EST data or exclusively by MS data alone is 14.3% and 5.25%, respectively. The difference in peptide coverage may be a result of the proteins with EST data being more highly expressed in the T. gondii proteome compared to those without EST alignments. Clusters that are supported by both MS data and EST data have a comparable portion of Glimmer, TigrScan, TwinScan, and Release4 sequences (58%, 61%, 66%, and 69%, respectively) (Table 5). Therefore, the gene prediction algorithms exhibit a false negative rate of 31 to 42%, which is the portion of the experimentally validated coding regions (clusters) (MS and EST) that are completely missed by individual prediction methods. While it is not possible to precisely identify the false positive predictions (predicted amino acid sequences that are not real proteins) from these data, it is clear that the rate of false positive predictions must be substantial for each prediction method. Figures 2a–c illustrate some of the sequence clusters with their corresponding experimental data. The sequence cluster in the first example (Fig. 2a.) is validated with MS peptide hits as well as EST data, but there are only two predicted protein sequences, one from TigrScan (TgTigrScan_7144) and one from Relase4 (55.m10274), i.e. all other prediction methods failed to predict the protein coding region that is represented by this cluster. Moreover, the peptide coverage illustrates that the longer TigrScan prediction is either a valid splice variant of the coded protein or the Release4 prediction failed to identify both the N and C terminal segments of the protein, both of which have been validated by MS data and, in the case of the N-terminal, EST data. The second example cluster (Fig. 2b.) is composed of sequences from all four prediction methods, albeit with significant differences among the sequences. For instance, the TigrScan prediction suggests two separate proteins in this clusters, or genomic location, while MS peptides and EST evidence points to a continuous protein sequence. In addition, the N terminal segment that is unique to the Glimmer prediction (TgGlmHmm_2826) is confirmed by both an MS peptide and two EST alignments. The third example (Fig. 2c.) shows a cluster with a variety of vastly different predictions from three methods (TigrScan fails to predict a protein in this cluster). The arrows pointing to the Glimmer prediction indicate MS peptides that confirm areas of the protein sequence where other models predicted gaps (TgTwinScan_2224 and TgTwin- Scan_2226 as well as 641.m00177 and 641.m00176), suggesting that the continuous protein sequence exists at least as one splice variant. In addition, the N-terminal tail of the Glimmer prediction is confirmed with a very high coverage of MS peptides and numerous EST alignments, while this segment was missed by all other prediction methods. Functional annotation of the T. gondii proteome Each protein sequence in the combined dataset was scanned against the PFAM database [31], a collection of protein domains and families, using pfam_scan.pl version 0.5, available from ftp.sanger.ac.uk. The output was parsed to find the PFAM domain names and locations for each sequence. Phobius (version 1.01) PLoS ONE | www.plosone.org 7 December 2008 | Volume 3 | Issue 12 | e3899