New & Noteworthy
December 16, 2011
Expression analysis at SGD now offers the ability to filter datasets by condition(s) or process(es) studied. A set of controlled vocabulary (CV) terms describing various perturbations associated with microarray experiments has been constructed and defined, and these terms have been used to tag the comprehensive collection of almost 400 datasets now available in SGD’s instance of SPELL (Serial Pattern of Expression Levels Locator). In this manner, datasets displayed in search results can be filtered using tags (CV terms) such as “oxidative stress” or “sporulation.” Filtering is an option for the “New Search,” “Show Expression Levels,” and “Dataset Listing” features. The SPELL interface has been provided through a collaboration with the SGD Colony at Princeton University. Special thanks to Peter Koppstein, Lance Parsons, and Kara Dolinski for help in implementing the dataset tag filtering option for SPELL at SGD.
August 12, 2011
SGD has updated its Yeast Molecular Function and Biological Process GO Slims to include additional terms. The Yeast GO Slims are a set of GO terms that best represent the major biological processes, molecular functions, and cellular components that are found in S. cerevisiae. GO Slim terms are useful in mapping precise, “granular” gene annotations to more general “high-level” terms. These terms have been selected by SGD curators based on annotation statistics and biological significance. To complement the expanded Yeast GO Slims, we have also added “generic GO-Slim” options to our GO Slim Mapper tool. The generic GO-Slim is developed and maintained by the Gene Ontology Consortium and includes fewer and higher-level terms than those provided by the Yeast GO-Slim.
The Yeast GO Slims are available for analysis via the GO Slim Mapper tool and YeastMine. Mapping of all the yeast gene products to the Yeast GO Slims is also available as a graphical view on the Genome Snapshot page and via the go_slim_mapping.tab file on the downloads site.
June 21, 2011
The SGD full-text literature search (powered by Textpresso) has been upgraded and updated to include 58,000 papers. Textpresso now includes the ability to restrict your searches to a subset of PMIDs as well as limit the results to a specific section of a paper. The full-text literature search can be accessed via the Full-text Search link on the home page. Thanks to Arun Rangarajan and Hans-Michael Muller from Wormbase for their help upgrading the software.
June 9, 2011
We’ve added new data tracks to our GBrowse genome viewer from eight publications, including recent surveys of nucleosome occupancy and positioning (Kaplan et al., 2009; Field et al., 2008; Mavrich et al., 2008; Lee et al., 2007); ncRNAs and RNA secondary structures (Lardenois et al., 2011 and Kertesz et al., 2010); and transcription factor and RNA Pol II occupancy (Venters et al., 2011 and Mayer et al., 2010). We have also added a data track to our Pbrowse proteome browser, displaying mature N-termini of mitochondrial proteins as determined by Vögtle et al., 2009. Both GBrowse and PBrowse have been upgraded to the most recent software with several new features.
We invite authors to work with us to integrate their data into our GBrowse and PBrowse viewers pre- and/or post-publication as we move forward. Watch for the regular addition of new tracks to SGD’s GBrowse and PBrowse in the future! Please contact us if you are interested in participating or have questions and comments.
February 23, 2011
Expression analysis at SGD has a new powerful interface and many new datasets. The new interface uses a tool called SPELL (Serial Pattern of Expression Levels Locator). This analysis tool facilitates the rapid identification of the most informative datasets and co-expressed genes based on patterns of expression shared with the query gene(s). By transitioning to this tool, expression data from a comprehensive collection of almost 400 datasets are now available at SGD. The expression analysis tool can be accessed via the expression tab, the expression summary histogram, and in the functional analysis pulldown located on Locus Summary pages. These new data and the SPELL interface have been provided through a collaboration with the SGD Colony at Princeton University. Thanks to Peter Koppstein, Lance Parsons, and Kara Dolinski for help with data preparation and implementation of SPELL at SGD. SPELL was developed by the Troyanskaya lab at Princeton University (Hibbs et al. (2007) Bioinformatics 23:2692).