S. cerevisiae. SGD has long provided so called ‘not in S288C’ genes beginning with those that were genetically deﬁned. We are now expanding this to include genes that have been observed within the genome of any S. cerevisiae stain. Genes can be lost as isolated populations adapt to their environment, and the pan-genomic representation of S. cerevisiae provides ready access to these genes, in context. For example, the strain S288C, source of the ref- erence sequence, is known to be missing several well studied genes involved in sugar utilization such as SUC1 (encoding one of several invertases, sucrose hydrolyzing enzymes), MEL1 (encoding an alpha-galactosidase required for catabolic conversion of melibiose to glucose) and BIO1 (encoding a pimeloyl-CoA synthetase) [SUC family (18); MEL family (19); BIO family (20)]. In addition to the reference genome there are currently twelve other genomes with annotations at GenBank and UniProt. The growth environment of commercial yeast strains, grape juice, is harsher than that faced by labora- tory strains. The difference in growth environment is likely represented by the different repertoire of gene functions. The commercial wine yeast EC1118 (21) lacks 111 genes present in S288C and contains 34 that are not found in S288C. These 34 genes of EC1118 not present in S288C include predicted hexose transporters, drug transporters, a putative zinc-ﬁnger transcription factor, a methyltransferase, an oxidoreductase and nucleotide transporters. The genomic sequence from another wine strain, AMRI1631 (22), is reported to contain several other genes not found in S288C. The addition of these non-S288C genes to SGD will expand the catalog of func- tions that can be explored by yeast genetics. As we add additional ‘not in S288C’ genes, we will adopt a standard gene categorization for all genes in the pan-genome. Genes will be classiﬁed into one of three types: ‘core’ genes that are found in all strains, ‘frequent’ genes indicating the gene is only found in some strains, and ‘unique’ genes for those that are only observed in one stain. Because many of the genomes have not been ﬁnished, their GenBank records may contain partial coding regions, representing gene fragments or other ambiguous annotations. Thus many of these genomes will require extensive reannotation. SGD will not add a gene to the pan-genome unless it meets very stringent annotation standards. Because of these issues we cannot appropriately estimate the number of core and frequently occurring genes within the species. However, based on the lack of sequence similarity between genes in an annotated strain and S288C, we roughly estimate that 1300 genes could be considered in the ‘unique’ class of genes. Therefore, the creation of the pan-genome will not be a trivial task but it is reasonable that we will be successful. Incorporation of high-throughput data sets Expression analysis at SGD has a new powerful interface and many new data sets. The new interface uses a tool called SPELL [Serial Pattern of Expression Levels Locator, http://spell.yeastgenome.org (23)], which facili- tates the rapid identiﬁcation of the most informative data Nucleic Acids Research, 2012, Vol. 40, Database issue D703 sets and co-expressed genes based on patterns of expres- sion shared with the query gene(s) from a comprehensive collection of almost 400 data sets. The expression analysis tool can be accessed from the Locus Summary via the Expression tab and the Expression Summary histogram. Implementation of a new data pipeline and the SPELL interface was created through collaboration with the SGD Colony at Princeton University. Budding yeast has long been used as the initial system for developing new high-throughput methodologies; as a result, we have a wealth of data types to integrate with speciﬁc gene products. SGD uses the GBrowse genome browser (24) to display diverse types of genomic informa- tion, with our most recent efforts focusing on chromatin structure, including histone modiﬁcations, histone variants and nucleosome organization; chromosomal maintenance sites for meiotic recombination and origins of replication; transcription regulation, including RNA Polymerase II occupancy and transcription factor binding from ChIP-chip and ChIP-seq assays; and the transcriptome, including RNA-seq information and 30-UTR analyses. SGD’s genome browser is provided from http://browse.yeastgenome.org. New high- throughput results are frequently added to the browser, and all data sets are available for download in standard formats (currently 60 published data sets) from the SGD ﬁle download site, http://downloads.yeastgenome.org (Chan,E.T. and Cherry,J.M. Considerations for creating and annotating the budding yeast Genome Map at SGD: A progress report. submitted to Database for Biocuration 2012 virtual issue). NEW QUERY AND DISPLAY TOOLS: ACCESS TO INTEGRATED BUDDING YEAST INFORMATION All literature-based manually curated annotations, sequence and high-throughput data sets are accessible to users via multiple access points and methods. Data are integrated for display on SGD’s locus-speciﬁc pages and can be analyzed using web-based tools, or downloaded for custom use. Downloading gene-speciﬁc and data set-speciﬁc annotations Enhancements to SGD’s locus speciﬁc pages now allow download of detailed information. For example, the phenotype data is available by selecting the ‘Download Data’ button available from the Phenotype tab of the Locus pages. A tab-delimited ﬁle of all interaction data for a gene can be obtained with the ‘Download Unﬁltered Data’ or via the ‘Download options’ link, which passes the list of genes to YeastMine, where the researchers can explore SGD’s rich collection of associated data, including genetic and physical interactions. Advanced query YeastMine (http://yeastmine.yeastgenome.org; Balakrishnan, R., Park,J., Karra,K., Hitz,B.C., Binkley,G., Hong,E.L., Sullivan,J., Micklem,G. and Cherry,J.M. YeastMine – An integrated data warehouse of S. cerevisiae data as a