WebQTL (16), PhenoGen (17), WebGestalt (18) and Ontological Discovery Environment (http://ontological discovery.org/), have been developed for analyzing biolog- ical data of phenotypes and complex traits. However, the ethanol genetics research community has still lacked a comprehensive ethanol-related gene resource that presents and integrates cross-species and cross-platform data. Here, we present such a database of ethanol-related gene resource (ERGR, http://bioinfo.vipbg.vcu.edu/ERGR/). To the best of our knowledge, it is a unique public database for ethanol-related genes. Aiming to eﬃciently integrate and analyze all or most of the published ethanol-related gene studies, we collected and annotated the representative large-scale ethanol-related gene datasets. These data were generated by diﬀerent approaches including linkage scan, genome-wide association study, microarray expression, QTLs, retrieved from other public databases, or collected by a systematic literature search. We obtained data from the ﬁve most-studied model organisms: human, mouse, rat, ﬂy and worm. In addition to information such as dataset description, gene annotations and gene ortholog informa- tion, the ERGR also provides tools for data integration (e.g. data union and intersection) and candidate gene selec- tion based on multiple datasets or organisms. ERGR seeks to be a useful resource for the ethanol research community and a model database of data collection and integration for other complex diseases such as schizophrenia and Alzheimer’s disease. DATA SOURCE AND METHODS Data collection and curation Currently, ERGR contains ethanol-related gene data from ﬁve organisms (human, mouse, rat, ﬂy and worm). These data were collected from diﬀerent technology platforms that have been widely used in alcohol dependence or etha- nol response studies: linkage scan, genome-wide associa- tion study, microarray gene expression and QTLs, or by literature search. We collected these data by the following three approaches. First, we searched publications of large- scale alcohol dependence or ethanol response studies in NCBI PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) and then extracted and checked the data from these pub- lications. Using this approach, we collected data including alcohol-related microarray gene expression studies from human or other animals, human alcohol dependence link- age studies and genome-wide association study. Speciﬁ- cally for the linkage data, we selected linkage regions by LOD scores and obtained the physical locations of the corresponding markers from UCSC genome browser (http://genome.ucsc.edu/) (19). Then, we retrieved the genes in the linkage regions from the Ensembl database (http://www.ensembl.org/index.html). Besides the genes in alcoholism or ethanol response studies, we also included other related genes. For example, we collected an addic- tion candidate gene list in which 130 genes were selected for a haplotype-based analysis of addiction (20). Second, we extracted related data from other public databases. We obtained the mouse alcohol behavior related QTLs from the PARC (Portland Alcohol Nucleic Acids Research, 2009, Vol. 37, Database issue D841 Research Center, (http://www.ohsu.edu/parc/by_phen. shtml). Only those QTLs that were marked signiﬁcant and whose genomic locations could be identiﬁed were extracted. Then, we retrieved genes mapped in the QTL regions from the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) (21). Further, we searched alcohol-related genes in HuGE Navigator (http://www.hugenavigator.net/), a database of genetic associations and human genome epidemiology (22). We found nine alcohol-related phenotypes in HuGE Naviga- tor and extracted all the genes associated with them. Third, we performed a systematic literature search by searching the titles and abstracts of all the publications available in PubMed. We searched each protein coding gene symbol with one of the three keywords: alcohol, etha- nol and alcoholism. To reduce false-positives, we manually checked those gene symbols having fewer than three letters/ digits or having more than 100 hits of publications. For each dataset, we compiled a summary description from the original data source. The summary includes the experimental method and treatment, platform, organism, tissue and phenotype, and the publication. For each gene in a dataset, more detailed information was extracted from the data source such as gene expression fold change, P-value, and tissue type. Gene ID We used NCBI Entrez Gene ID as the central ID for cross linking and annotation. However, speciﬁc studies used diﬀerent IDs, such as gene symbol, mRNA accession number, EST accession number, clone ID, Aﬀymetrix probe ID, Ensembl ID or UniGene ID. We applied the following three approaches to convert diﬀerent IDs to gene IDs. (i) We downloaded gene2accession, gene2uni- gene, and gene_info ﬁles from NCBI FTP site (ftp:// ftp.ncbi.nlm.nih.gov/gene/DATA/) and obtained the cor- responding gene IDs for the accession numbers, UniGene IDs or gene symbols used in diﬀerent studies. (ii) We used an online tool, IDConverter (http://idconverter.bioinfo. cnio.es/IDconverter.php), to convert the original IDs in the publications to gene IDs (23). (iii) When the IDs could not be converted by the above two approaches, we manually searched the NCBI databases for the gene IDs. Gene annotation We downloaded gene annotation ﬁles from the NCBI FTP site. Then, we extracted annotation information for the genes in our database from these ﬁles. We parsed the gene_info ﬁle to retrieve the gene information such as gene symbol, alias, full name, chromosome, genetic location and gene type. We obtained gene ontology (GO) annotations from the gene2go ﬁle downloaded from the GO website (http://www.geneontology.org/) (24). The accession numbers of the reference sequences and chromosomal location of each gene was parsed from the gene2refseq ﬁle. Orthologs A cross-species gene discovery and validation scheme can provide both powerful conﬁrmation of candidate genes