W154 Nucleic Acids Research, 2007, Vol. 35, Web Server issue annotation information from multiple databases. Useful information such as gene description, accession, name and synonyms were extracted for each GeneHub gene and stored in a common database ﬁeld for text searching and gene characterization purposes (Supplementary Figure 1). The ortholog linkings between human and mouse GeneHub genes were based on the hmlg_ftp.txt ﬁle from HomoloGene (www.ncbi.nlm.nih.gov/Homolo Gene/) Release 50.1. We used the orthologous Entrez Gene pairs of human and mouse if they were established by reciprocal best match between three or more organ- isms, or reciprocal best match, or sequence similarity with match identity 470%. We were able to link 15 868 human GeneHub genes to their mouse counterpart. EST data collection and cleansing EST data were downloaded from NCBI (http:// www.ncbi.nlm.nih.gov/dbEST/) and processed following a previous protocol to retain only non-normalized, non- subtracted usable cancer and normal libraries (7). From the 11 July 2006 release of dbEST, we collected 4 175 880 human ESTs from all usable libraries, including 1 912 573 ESTs in 1 995 normal libraries and 2 263 307 ESTs in 3 812 cancer libraries. This represents a 35% increase in total usable EST counts from 2 years ago. For mouse, we collected 1 879 315 total ESTs, including 1 461 621 from 285 normal libraries and 417 694 from 37 cancer libraries. See Supplementary Table 2 for total number of ESTs in each category and tissue type. EST-based expression analysis We ﬁrst mapped EST sequences to their genomic locations using GMAP (13), followed by a secondary ﬁltering step as described earlier (490% identity and 490% coverage). To avoid ambiguity, we discarded the ESTs (1.3% of total) that were mapped to multiple genomic loci with identical percent identity and coverage. We also elimi- nated those ESTs (5.5% of total) with near identical matches (with 52% diﬀerence in percent identity or coverage) to multiple genomic loci. The genomic coordi- nates were compared with the gene structure information (intron/exon boundary) of GeneHub genes. An EST was considered to be the product of a gene if the two entities were mapped to the same locus and share at least one exon (with minimum overlap of 30 bp). Multiple EST reads from the same clone were reduced to a single read if clone information was available. The digital expression unit (DEU) for a given gene in each tissue category is deﬁned as the number of matching EST clones from a normalized library size of 1 million. The DEU values were calculated iteratively for each tissue type to proﬁle expression levels across all tissues. The Z-test was applied to determine whether DEUs in two samples were statistically diﬀerent using a method described previously (7). Comparisons were made between normal and cancer samples from the same tissue, and across diﬀerent types of tissues. To improve the eﬃciency of the GeneHub-GEPIS program, the expression result for each of GeneHub genes was pre-computed for fast access. For 1000 randomly selected human genes, we compared how ESTs were mapped to genes by BLAST ( 98% identity over 460 bp region) or by the above method. For 74% of tested genes, BLAST alone would identify at least one incorrect EST, as judged by its genomic location. By design, the new method disallows this type of erroneous mapping. It is worth noting that the median DEU level for human genes in normal tissues is 50.0 based on our new method, as compared to a median level of 70.0 derived from BLAST-based EST-mapping approach (7). The average number of ESTs mapped to a gene also reduced from 128.7 to 107.5 (or a 16% reduction). The signiﬁcant reduction of ESTs mapped to genes reﬂects the high accuracy of EST mapping by GMAP (13) and the rejection of ESTs with promiscuous matches. Manual review of EST mapping results for randomly sampled genes also conﬁrmed the much improved mapping quality. As a conﬁrmation step, we compared our results with a set of EST-gene mapping data independently generated by the UCSC Genome Browser team, and we found a concordance of 497%. The UCSC data are based on BLAT (16) and a series of ﬁltering steps, and are used for the EST alignment track in the UCSC genome browser. MicroRNA expression analysis is based on the observa- tion that miRNA precursor sequences can be found among ESTs (17,18), and that pre-miRNA expression levels correlate with mature miRNA expression levels (19). Given this relationship, we could use EST data to approximate miRNA expression levels in various tissues. We ﬁrst collected genomic locations of the miRNA stem- loop sequences from version 9.0 of miRBase (20), and then obtained all EST sequences that had any overlap with the miRNA stem-loops for expression analysis as described earlier. The Regional GEPIS Atlas, which depicts the expres- sion level of all genes in selected tissues for a given genomic window, was created in the similar fashion as described previously (7) but with the exception that we stored the genomic coordinates for all genes in a MySQL database instead of in plain text ﬁles. Application implementation The web front-end was written in HTML, javascript, CSS and Perl CGI. The Perl template module (HTML::Template) was used to achieve consistent look across diﬀerent web pages. The Ajax technology was used to make the application more interactive so that text searches could be performed without leaving the input web page. We used the Prototype Javascript library (http://www.prototypejs.org/) to implement AJAX calls. This library supports AJAX interactions and provides utility functions for accessing page components and DOM manipulations. A MySQL database was used for data storage and retrieval (Supplementary Figure 1). For text- based searching, the query string can be a record identiﬁer (e.g. accession) or gene name. The program queries DBXREF, GENE and GENE_SYNONYMS tables in sequential order to ﬁnd a best match from the selected target species for the given query, regardless of the species