organisms. The E. coli genome also has a much higher fraction of experimentally elucidated genes than do the genomes of other model organisms. The assessment of the number of genes whose functions were determined by each mechanism in a given database is determined by querying ‘evidence codes’, which are mechanisms for recording within a database the type of evidence that was used to determine a gene function. That is, was the function determined through a given type of laboratory experiment, or through a computational method such as a sequence similarity search? Diﬀerent databases provide somewhat diﬀerent types of evidence code mechanisms. The EcoCyc E. coli data in this table were assembled from the evidence codes stored in EcoCyc protein and RNA objects. EcoCyc contains GO terms; however, most EcoCyc GO terms currently lack evidence codes. EcoCyc evidence codes (7) are encoded using a diﬀerent and more detailed approach than used in GO. That approach is not exactly comparable to GO evidence codes; therefore, some eﬀort was required to obtain a fair comparison to the GO evidence codes. The non-E. coli data in Table 3 were assembled from the Gene Ontology (GO) (11) term assignments available for these model organisms from the GO website (12), using GO data downloaded on 6 December 2006. Because the evidence codes in EcoCyc are not precisely comparable to those for the other model organisms, Table 3 presents four alternative deﬁnitions of what it means for a gene in an organism other than E. coli to have an experimentally determined function. We believe that the EcoCyc notion of experimentally deﬁned function is most similar to Deﬁnition 2 used in Table 3 because the vast majority of EcoCyc genes with experi- mental evidence codes have speciﬁc functions. The results in Table 3 are of course dependent on the completeness of the GO annotations for each organism with respect to the biomedical literature. All four of the non-E. coli deﬁnitions that we present share the notions that the gene must not have a GO code assignment of GO:0003674 (‘unknown function’), and that the gene must have a GO code assignment supported by a GO experimental evidence code. All four deﬁnitions allow GO code assignments from any of the three GO hierarchies (molecular function, biological process and cellular location). The four deﬁnitions diﬀer along two dimensions. The ﬁrst dimension is that two deﬁnitions (Deﬁnitions 3 and 4) allow genes annotated with GO terms for general classes of function such as ‘DNA helicase activity’ or ‘protein binding’ activity, and for speciﬁc functions such as ‘pyruvate kinase activity’. General GO terms, called ‘non-leaf terms’, have child (more speciﬁc) terms in the ontology hierarchy (called ‘leaf terms’). In contrast, Deﬁnitions 1 and 2 allow only speciﬁc functions such as ‘pyruvate kinase activity’, that do not have child terms in the ontology hierarchy. The second dimension along which the deﬁnitions diﬀer is as to whether they allow all GO experimental evidence codes (Deﬁnitions 2 and 4), or whether they allow only the IDA (‘inferred from direct Nucleic Acids Research, 2007, Vol. 35, No. 22 7581 assay’) evidence code (Deﬁnitions 1 and 3). The four deﬁnitions are: (i) Strict evidence, leaf terms only: Require that the gene has a leaf GO code assignment with the IDA (Inferred from direct assay) evidence code. (ii) Loose evidence, leaf terms only: Require that the gene has a leaf GO code assignment with any of the following experimental evidence codes: IDA, IGI (‘inferred from genetic interaction’), IMP (‘inferred from mutant phenotype’), IPI (‘inferred from physical interaction’) or TAS (‘traceable author statement’). (iii) Strict evidence, all terms: Require that the gene has a leaf or non-leaf GO code assignment with the IDA (Inferred from direct assay) evidence code. (iv) Loose evidence, all terms: Require that the gene has a leaf or non-leaf GO code assignment with any of the following experimental evidence codes: IDA, IGI, IMP, IPI or TAS. The transcriptional regulatory network of E. coli Since 2001, RegulonDB (13,14) and EcoCyc have been integrated in the sense that all data entry and updating for RegulonDB occurs within EcoCyc. Regulatory data are exported from EcoCyc at each EcoCyc release, and combined with certain additional information to create a new release of RegulonDB. The information in RegulonDB that is not present in EcoCyc includes predictions of promoters, transcription factor binding sites, and operons; ribosome-binding sites; and weight matrices for some transcription factors. EcoCyc contains extensive descriptions of the transcrip- tional regulatory network of E. coli, obtained from the experimental literature. The 4460 E. coli genes are assigned to 3063 transcription units (TUs), that is, sets of genes that are transcribed in a single transcript. A total of 729 of those TUs are supported by experimental evidence; the remaining TUs were predicted computation- ally by the Pathway Tools operon predictor (15). The average TU contains 1.7 genes because although some TUs contain as many as 15 genes, the majority of TUs (2057) contain only a single gene. Figure 1 shows the length frequency of all E. coli TUs. One hundred EcoCyc genes have not been assigned to any TU, for reasons such as large overlaps between genes that may have confused our operon predictor. We are now curating those genes in EcoCyc to provide TU assignments. The TUs contain 1230 promoters, of which 816 have experimental evidence codes (we believe the number 816 is an undercount, because a number of older entries lack evidence codes). The number of promoters with experi- mental evidence can exceed the number of transcription units with such evidence because a promoter may be known without the end of the TU being established, or a promoter may have been identiﬁed upstream of a predicted TU, or because curators neglected to assign experimental evidence codes to some TUs. One thousand seven hundred and ninety transcription factor (TF) binding sites are deﬁned for the preceding