New & Noteworthy
June 24, 2014
We have redesigned the Protein page to include a new tabular display of protein domains. This table provides the identifier for each domain and illustrates the respective locations of the domains within the protein. In addition to this new table, the domains are displayed in an interactive network diagram that presents the proteins that share these domains with your protein of interest (see figure below, left).
Another new feature on the Protein page is the display of phosphorylation sites within the protein’s sequence (as curated by BioGRID). This feature is available for both the reference strain S288C and other commonly used S. cerevisae strains, using the pull-down to select the desired strain view (see figure below, right) .
March 26, 2014
What happens when you cross two comprehensive deletion mutant collections with a library of more than 1800 structurally diverse chemicals? HIP HOP happens. Not the music, but a whole lot of very informative phenotype data – over 40 million data points!
The response of S. cerevisiae mutant strains to a chemical can tell us a lot about which pathways or processes the chemical affects. This is not only interesting for yeast biologists, but also has important implications for human molecular biology and disease research. So a group at The Novartis Institutes of Biomedical Research decided to test the sensitivity of nearly 6,000 mutant yeast strains to a panel of about 1,800 compounds.
Hoepfner and colleagues have published these results and have also generously offered them to SGD. They used the HIP and HOP methods (HIP, HaploInsufficiency Profiling, using diploid heterozygous deletion mutant strains; HOP, HOmozygous deletion Profiling, using diploid homozygous deletion mutant strains) that have proven very useful in yeast since the creation of the systematic deletion mutant collections.
To do this mammoth series of experiments they obviously needed to set up an automated pipeline. These sorts of experiments have been done before, but in this study Hoepfner et al. improved on existing procedures in many ways: the physical techniques, the controls and replicates included, and the methods for data analysis.
Phenotype annotations in SGD. We’ve incorporated a subset of these results into SGD as mutant phenotype annotations. Why a subset? Some of the chemicals that were used in these experiments are un-named proprietary compounds, so the individual phenotypes would not be very informative in the context of SGD. We’ve added the phenotypes that involve named chemicals to SGD – more than 5,500 annotations. These may be viewed on Phenotype Details pages for individual genes (see example), retrieved as a set using Yeastmine, or downloaded along with all SGD mutant phenotype annotations in our phenotype data download file.
Easy access to the full dataset and analyses. We’ve also added a new set of links to SGD that take you directly from your favorite gene to the authors’ website, which provides full access to all of the data and interesting ways to look at it (see below). When you click on a “HIP HOP Profile” link from the Locus Summary page or the Phenotype Details page of a gene in SGD, the landing page at the authors’ website allows you to explore data for mutants in that gene or for chemicals affecting that mutant strain. You can see which chemicals had the greatest effects, which other mutant strains have a similar range of phenotypes, and much more. And if a chemical that has interesting effects is proprietary, don’t worry; Hoepfner and colleagues have stated that they “encourage future academic collaborations around individual compounds used in this study.”
Information about mutant strains. In the course of this study, the authors also generated some very useful data about particular mutant strains in the deletion collection. Some of them were hypersensitive to more than 100 different chemicals. Others turned out to be carrying additional background mutations that could affect the phenotypes of the mutant strain. We are planning to display this kind of information (from this and other studies) directly on SGD Phenotype Details pages in the future.
We thank Dominic Hoepfner and colleagues for sharing these data with SGD and for helping us to incorporate the data. And we encourage you to explore this new resource and contact us with any questions or suggestions.
March 13, 2014
Towards the goal of compiling datasets to produce a complete transcriptome of yeast (the set of all RNA molecules produced in a single cell or population of cells), we have loaded a defined set of transcripts, based primarily on data from Pelechano, et al, but supported by other datasets, into SGD’s flexible search tool, YeastMine. The representative set includes transcripts which Pelechano et al. identified by simultaneous determination of the 5’ and 3’ ends of mRNA molecules whose end coordinates are supported by datasets from other laboratories.
The transcript data can be accessed in YeastMine using the ‘Gene -> Transcripts’ template, which allows you to specify a gene name or list of gene names and return the list of all associated transcripts based on the collection of data described above. The results include the start and end coordinates for each transcript, the number of counts observed for each transcript in glucose and galactose, notes, and references for the relevant datasets.
March 4, 2014
You can now use SGD’s advanced search tool, YeastMine, to find the human homolog(s) of your favorite yeast gene and their corresponding disease associations. Or, begin with your favorite human gene or disease keyword and retrieve the yeast counterparts of the relevant gene(s). As an example, you can search for the S. cerevisiae homologs of all human genes associated with disorders that contain the keyword “diabetes” (view search).
We have recently loaded data from OMIM (Online Mendelian Inheritance in Man) into our fast, flexible search resource, YeastMine, and provided 3 predefined queries (templates) that make it simple to perform the above searches. Newly updated HomoloGene, Ensembl, TreeFam, and Panther data sets are used to define the homology between S. cerevisiae and human genes. The results table provides identifiers and standard names for the yeast and human genes, as well as OMIM gene and disease identifiers and names. As with other YeastMine templates, results can be saved as lists and analyzed further. You can also now create a list of human names and/or identifiers using the updated Create Lists feature that allows you to specify the organism representing the genes in your list. The query for yeast homologs can then be made against this list.
In addition to human disease homologs, we have incorporated fungal homolog data for 24 additional species of fungi. You can now query for the fungal homologs of a given S. cerevisiae gene using the template “Gene –> Fungal Homologs.” This fungal homology data comes from various sources including FungiDB, the Candida Gene Order Browser (CGOB), and PomBase, and the results link directly to the corresponding gene pages in the relevant databases, including Candida Genome Database (CGD) and Aspergillus Genome Database (AspGD).
All of the new templates that query human and fungal homolog data can be found on the YeastMine Home page under the new tab “Homology.” These templates complement the template “Gene → Non-Fungal and S. cerevisiae Homologs” that retrieves homologs of S. cerevisiae genes in human, rat, mouse, worm, fly, mosquito, and zebrafish.
Watch the Human Disease & Fungal Homologs in SGD’s YeastMine tutorial (below) to learn how to find and use these new templates.