Nucleic Acids Research, 2007, Vol. 35, Web Server issue W153 We have now developed a new web server, named GeneHub-GEPIS, which performs digital expression analysis based on an integrated database for human and mouse genes. One distinguishing characteristic of this tool is that ESTs are mapped to pre-deﬁned gene structures along the genome. The GeneHub component of the application is designed to deﬁne gene boundaries based on mRNA transcript sequences from major databases and to establish extensive cross references for commonly used gene identiﬁers. Based on the precise genomic locations of ESTs, as determined by the GMAP algorithm (13), we link ESTs to genes for subsequent expression analysis. The new design oﬀers several major advantages. First, this genome-based approach increases the accuracy of EST mapping to genes, thus enhancing the overall reliability of the EST-based expression values. Second, the new gene- centric design makes the system more extensible, so that we could easily add a new collection of genes, such as microRNAs, to the system. Third, the integrated gene database accepts text-based searches and diverse input values. In addition to DNA sequences, the input values can be identiﬁers from common gene databases or commercial microarray platforms. Fourth, the ortholo- gous relationships stored in the GeneHub database allow easy navigation between mouse and human genes. Finally, the program provides basic information about input genes and allows direct linking to expression results from any web site. Meanwhile, we retained the useful features from the previous GEPIS application, such as the capability to draw a regional expression atlas for a given genomic region. Here, we present GeneHub-GEPIS as a new and useful tool for performing gene expression analysis across many normal and cancer tissues for both mouse and human genes. MATERIALS AND METHODS Genome-guided gene definition and cross references The genomic structures of protein-coding genes were ﬁrst deﬁned using transcripts from several reliable sources. The collection of such high-quality transcripts, which we also call the core gene set, contains mRNA sequences from RefSeq, the Known gene set of Ensembl genes, Proteome and FANTOM (mouse only) (see Supplementary Table 1 for details). Each of the core gene set sequences was mapped to their respective genome (human NCBI Build 36 and mouse NCBI Build 35) using GMAP (13), and only the genomic match with the highest percent identity and percent coverage was chosen. GMAP has been shown to provide very accurate mapping and alignment results for both mRNA transcripts and ESTs (13), but occasionally matches with lower matching percentage can be found due to low-quality sequences. As a conservative precaution, we removed transcripts (and ESTs for a later step) with 590% coverage of the entire transcript or with 590% identity as measured by GMAP. For instance, about 0.4% RefSeq sequences were ﬁltered out during this step. For any two transcripts to be clustered into one gene, we required that their exon sequences overlap, be in the same orientation and share at least one exact exon boundary or splice site. The requirement for shared exon boundary was used to limit the inclusion of antisense transcripts, since the orientation of these transcripts could be occasionally mis-annotated (14,15). Each group (or cluster) of transcripts was considered as a ‘GeneHub gene’. Using this approach, we deﬁned 31 999 non-redundant genes for the human and 34 794 for mouse. Overall, these GeneHub genes can be considered as a superset of known genes from the above data sources. After the initial collection of human and mouse genes were obtained, additional sequences were mapped to the GeneHub genes. Transcripts from GenBank and the Ensembl Novel collection were mapped to the genomes with GMAP (13) and then compared with those GeneHub genes derived above. For a transcript to be linked to a GeneHub gene, at least one of the splicing junctions was required to match perfectly with those of the GeneHub gene. We next tried to assign microarray-related probe sequences (see Supplementary Table 1 for the full list) from commonly used commercial array platforms to GeneHub genes. For Aﬀymetrix expression arrays, we used the target sequences obtained from Aﬀymetrix to link to known genes. For Agilent oligo-based expression microarrays, we directly used the 60-mer oligo-nucleotide sequences for gene linking. Using GMAP (13), we determined whether the array probe sequences overlap with the exon sequences collected above. Next, for sequences that did not overlap with any exons, we examined whether they were located in the vicinity of any GeneHub genes. We assigned a probe sequence to the closest gene in the same orientation if the probe sequence was located within 5 kb to the 30 end or 2 kb to the 50 end of the gene. Additional methods were used for both human and mouse data to link other protein or DNA identiﬁers to GeneHub genes. First, for the roughly 1% of all transcript sequences that failed to align to the genome, we compared their sequences to the core transcript set using BLASTn, and required a perfect match over at least a 60-bp region with a transcript member of the GeneHub gene. Second, protein sequences from UniProt and PDB were compared with the GeneHub DNA sequences using the BLASTx program. For a protein record to be linked to a GeneHub gene, we required 498% identity over an at least 35 amino-acid long region. The thresholds of BLAST cutoﬀs were empirically determined so that false linking was minimized without over-sacriﬁcing true signal. Third, we added links that were based on existing annotations. For example, the gene2refseq ﬁle downloaded from NCBI contains relationships between Entrez Gene records and RefSeq and GenBank accessions, so an Entrez Gene record could be linked to a GeneHub gene if a RefSeq or GenBank accession was already part of the GeneHub gene. Gene annotation and ortholog linking Once we built the GeneHub gene collection associated with gene and protein identiﬁers from various databases, it became straightforward to collect and integrate gene