Nucleic Acids Research, 2007, Vol. 35, Web Server issue W351 divergent sequences of the same apparent type into one model class (3), we have taken a diﬀerent, perhaps more conservative approach of making smaller, more phylogenetically restricted models, in the hopes that this will allow us to study the evolution and diversiﬁcation of tRNA identity determinants. Our ultimate aim is to model and understand the constellation of identity determinants that actually can or could function together in the same cellular context. In TFAM 1.0 we release a model of lysylated isoleucine tRNAs based only on proteobacterial data. Earlier versions of TFAM were only available for standalone compilation and installation on UNIX-like platforms and required complicated installations of prerequisites. The new web-based interface, besides obviating the installation burden, provides additional functionality over the standalone interface including color visualization and sorting of TFAM scores. Installation of the standalone version has also been simpliﬁed by the removal of dependencies. MODELS Both the Web Server and standalone interfaces to TFAM version 1.0 provide new models for tRNA classiﬁcation. The bacterial model of earlier versions (earlier called ‘MSDB’ for ‘Modiﬁed Sprinzl DataBase’ and provided with TFAM versions 0.2 and 0.3, henceforth ‘bacterial TFAM model 0.1’) has been complemented and corrected by genomic tDNA sequences from 46 proteobacterial species classiﬁed on the basis of clustering to known methionine elongator, unmodiﬁed isoleucine elongator, initiator and lysylated isoleucine (kIle) elongator tRNAs. This data, generously provided by Paul Higgs and available here as Supplementary Data to this article, is an extension to the dataset analyzed in (9). These sequences and their classiﬁcations are a consistent subset of those provided in an independently made TFAM model (3). A full description of how we used these proteobacterial sequences to modify bacterial TFAM model 0.1 from (4) is provided as Supplementary Data here and at the TFAM Web Server. The model presented here is called ‘bacterial TFAM model 0.2’. Bacterial TFAM model 0.2 also provides the ﬁve selenocysteine sequences from the year 2000 release of the Sprinzl Search Server http://www.uni-bayreuth.de/ departments/biochemie/trna/, that were removed in bac- terial TFAM model 0.1. A leave-one-out cross-validation of bacterial TFAM model 0.2 without selenocysteine tRNAs misclassiﬁed 16 of 759 sequences (2.1%) of which 2 of 50 kIle tRNAs were misclassiﬁed (96% sensitivity) and 2 of 50 sequences classiﬁed as kIle were not kIle (96% speciﬁcity). These results were identical when the Sel-Cys model sequences were included, but 2 of the 5 selenocysteine tRNAs were misclassiﬁed, underscoring the need to make this model larger and increase its generality. In TFAM 1.0 we introduce ﬁrst-cut versions of eukaryotic and archaeal TFAM models, each given version numbers 0.1, with the main purpose of enabling convenient and automated initiator tRNA identiﬁcation in all phylogenetic domains. Initiator tRNA determinants are highly conserved within phylogenetic domains (10). Both of these models were created from data downloaded on February 23, 2006 from the Sprinzl 2000 tRNA Search Server http://www.uni-bayreuth.de/departments/ biochemie/trna/. For the eukaryotic TFAM model 0.1, redundant sequences were removed and no corrections were made to the identity annotations. The sensitivity of eukaryotic TFAM model 0.1 for detecting known initiator tRNAs in Drosophila melanogaster was veriﬁed to be perfect. Initiator tRNA predictions with eukaryotic TFAM model 0.1 in twelve Drosophila genomes have been released on the web at http://www.bioinf.manchester. ac.uk/bergman/data/ncRNA/tRNA/. Resubstitution ana- lysis showed that all training data initiators from the Sprinzl 2000 dataset were correctly classiﬁed, including those from yeast, worm, ﬂies, vertebrates and plants. For the archaeal TFAM model 0.1, redundant sequences were removed and the identity annotations of three tRNAs were changed as described in Supplementary Data. The initiator tRNA sequences were veriﬁed to be consistent with other archaeal initiator identity determi- nants as described (11). We also veriﬁed the perfect performance of this model on two experimentally char- acterized archaeal initiator tRNA genes not included and diﬀering in sequence from those in the model: those of Pyrodictium occultum (12) and Pyrococcus abyssi (13). The accuracy of the eukaryotic and archaeal TFAM models 0.1 for other tRNA types has not been veriﬁed or studied in any way. RESULTS AND DISCUSSION A user can input tRNA or tDNA sequences in multi-fasta format to the TFAM Web Server by cut-and-paste or ﬁle- upload, select a model with which to classify the input sequences, and then push a button to run the server. The browser window will wait while the computation takes place, after which the output is loaded in tabular format. A computation on 514 human tRNA sequences from the Genomic tRNA Database (GtRDB, http://lowelab.ucsc. edu/GtRNAdb/) took 50 s. The maximum input size is currently set to 200 KB, which accommodates more than 1300 tRNA sequences at once. A sample output on bacterial tRNA sequences is shown in Figure 1. Scores are visualized with a color scheme increasing from dark purple for negative numbers of large magnitude to bright red for large positive numbers. The table can be sorted by any column making it easy to ﬁnd sequences with properties of interest. In addition, TFAM can indicate when its prediction conﬂicts with an annotated function. To get this functionality, the user may either name sequences with identiﬁers from which the annotated sequence can be parsed by TFAM, or TFAM can auto-detect anticodons and use these to annotate identity. A table of recognized sequence identiﬁer formats is shown in Supplementary Data as well as in the Web-Server documentation. TFAM recognizes the format of identiﬁers from the GtRDB and the Sprinzl database (14) from which sequences may be pasted directly.