W246 Nucleic Acids Research, 2007, Vol. 35, Web Server issue the background distribution’s mean. The second score, the Fisher exact test, indicates if the proportion of genes containing the TFBS is greater than would be expected by chance. TFBS predictions situated within overlapping alternative promoters are counted only once when cal- culating over-representation in human and mouse genes. For Caenorhabditis elegans genes in operons, TFBS predictions in the upstream region of the ﬁrst gene in the operon apply to all genes in the operon. oPOSSUM combination site analysis (CSA). TFBSs do not act in isolation to initiate the transcription process. Transcriptional regulation can be viewed as mediated by arrays of cis-regulatory sequences, termed cis-regulatory modules (CRMs), which are bound by multiple TFs. In oPOSSUM2, Huang et al. (2) address the detection of over-represented sets of TFBSs in the promoters of a set of co-expressed genes. In brief, the method reduces combi- natorial complexity through an initial clustering step, which partitions similar TFBS proﬁles into groups, herein denoted ‘TFBS classes’, along with an analysis step to determine a TFBS class representative proﬁle for each TFBS class, which is used to detect over-represented sets of TFBS classes. Since each distinct, over-represented set of detected TFBS classes, herein described as a ‘TFBS class combination’, implicates the over-representation of one or more underlying TFBS proﬁle-speciﬁc combina- tions, each of these TFBS class combinations is expanded to all possible TFBS proﬁle-speciﬁc combinations (for the indicated classes) and then all combinations are analyzed for over-representation. Furthermore, given that CRMs can contain locally dense clusters of TFBSs, the system also provides for the speciﬁcation of an inter-binding site distance (IBSD) constraint to conﬁne the number of TFBS combinations that are investigated. A scoring scheme, adopted from the Fisher exact test, utilizes two sets of TFBS (class or proﬁle-speciﬁc) combination counts to compare the degree of their over-representation: (i) the number found in the promoters of the co-expressed gene set versus (ii) the number found in the promoters of genes in a background set (all genes in the database). TFBS combinations occurring in multiple alternative gene promoter regions are counted only once. Species-specific databases In addition to enhancements to the human/mouse oPOSSUM database, we introduce new species databases for studies of over-represented TFBSs in yeast and worms. While the SSA over-representation analysis remains the same for all species, diﬀerences in gene structure require that the construction of the underlying databases be particular to each species. Human/mouse. Ambiguities in ortholog assignments and the deﬁnition of TSS positions are major challenges when performing alignments for a large proportion of human and mouse genes. We have expanded the human/mouse database through (i) the discrimination of potential orthologs from predicted paralogs based on upstream sequence similarity (Figure 1), and (ii) the delineation of alternative promoters for human and mouse genes Putative TSRs for KNOWN/NOVEL transcripts and PUTATIVE transcripts with ≥5 CAGE tags Figure 2. Identiﬁcation of transcription start regions (TSRs) using a combination of EnsEMBL annotations and CAGE data. To improve our alignments, we determine putative alternative TSSs for the human and mouse genes. For each gene, the entire repertoire of transcripts from both EnsEMBL core genes and EST genes are retrieved. The TSSs for all transcripts are recorded, followed by a clustering step such that TSSs within 500 bp of one another are merged to form a transcriptional start region (TSR). For each TSR containing a transcript annotated as ‘known’ or ‘novel’, we accept the TSR as is. For TSRs based solely on EST gene transcripts, we require a minimum of 5 CAGE tags as evidence for transcription initiation. (Figure 2) to address the alignment failure observed in previous database builds. While the inclusion of promoter comparisons for candidate ortholog assignment may be controversial, the impact is marginal as 51.3% of gene pairs were derived EnsEMBL-defined human/mouse homologs (16,058) Filter by ortholog type One-to-many and many-to- many homologs (1,513) Map to UCSC whole- genome alignments Genes without a one-to-one ortholog based on upstream sequence similarity (847) One-to-many ortholog pairs (14,967) One-to-one ortholog pairs predicated from upstream sequence similarity (195) 15,162 orthologous gene pairs for TSR determination Figure 1. Determination of one-to-one orthologs for human and mouse genes. An initial set of homologs was downloaded from EnsEMBL v41 (30). All homologs annotated as ‘one2one’ are extracted. To select the closest putative ortholog pairs from homologs with ‘one2many’ or ‘many2many’ relationships, we check for upstream conservation using the whole-genome human–mouse alignments (6). We re-annotate unambiguously aligned homologs as putative one-to-one orthologs, adding 195 gene pairs to our set and bringing the total number of orthologs to 15 162. Core EnsEMBL genes (hs18/mm8) (“higher confidence”) EnsEMBL EST genes (hs18/mm8) (“lower confidence”) Fantoms3 CAGE (hs17/mm5) Liftover (UCSC) Fantoms3 CAGE (hs18/mm8) Locate TSS positions TSSs Transcription start regions (TSRs) Cluster TSSs (500 bp window) Map CAGE tags to region