New & Noteworthy

New fungal homolog data at SGD

September 15, 2014

Have you ever wondered about the role played by the homolog of a particular yeast gene in other fungal species? SGD’s advanced search tool, YeastMine, can now be used to find homologs of your favorite Saccharomyces cerevisiae genes in the pathogenic yeast, Candida glabrata. There are now 25 species of pathogenic and non-pathogenic fungi in YeastMine, including S. cerevisiae.

The fungal homologs of a given S. cerevisiae gene can be found using the template called “Gene –> Fungal Homologs.” Fungal homology data comes from various sources including FungiDB, the Candida Gene Order Browser (CGOB), the Yeast Gene Order Browser (YGOB), the Candida Genome Database (CGD), the Aspergillus Genome Database (AspGD) and PomBase, and the results link directly to the corresponding homolog gene pages in the relevant databases.

A results table is generated after each query and the identifiers and standard names for the fungal homologs are listed in the table. As with other YeastMine templates, results can be saved as lists for further analysis. You can also create a list of yeast gene 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 homologs can then be made against the custom gene list.

All of the new templates that query fungal homolog data can be found on the YeastMine Home page under the “Homology” tab. This template complements the template “Gene → Non-Fungal and S. cerevisiae Homologs” that retrieves homologs of S. cerevisiae genes in humans, rats, mice, worms, flies, mosquitos, and zebrafish.

We invite you to watch SGD’s YeastMine Fungal Homologs video tutorial (also available below) for tips on accessing Fungal Homolog data at SGD. You can view all Video Tutorials for YeastMine here.

New Sequence, Chromosome, and Contig pages

August 25, 2014

New Sequence pages are now available in SGD for virtually every yeast gene (e.g., HMRA1 Sequence page), and include genomic sequence annotations for the Reference Strain S288C, as well as several Alternative Reference Genomes from strains such as CEN.PK, RM11-1a, Sigma1278b, and W303 (more Alternative References coming soon). Each page includes an Overview section containing descriptive information, maps depicting genomic context in Reference Strain S288C (as shown below) and Alternative Reference strains, as well as chromosomal and relative coordinates in S288C.

The sequence itself includes display options for genomic DNA, coding DNA, or translated protein.

Also available on each Sequence page are links to redesigned S288C Chromosome pages, links to new Contig pages for Alternative Reference Genomes, and a Downloads menu for easy access to DNA sequences of several other industrial strains and environmental isolates. The new Sequence, Chromosome, and Contig pages make use of many of the features you enjoy on other new or redesigned pages at SGD, including graphical display of data, sortable tables, and responsive visualizations. The Sequence pages also provide seamless access to other tools at SGD such as BLAST and Web Primer. Please explore these new pages, accessible via the Sequence tab on your favorite Locus Summary page, and send us your feedback.

Shared Domains and Phosphorylation Sites on Protein Pages

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) .

Left: Proteins (gray circles) that share domains (colored squares) with Fas1p (yellow circle). Right: an example of some of the phosphorylation sites in Swe1p (red residues).

Proteins that share domains with Fas1p

Swe1p protein sequence and phophorylation sites highlighted in red.

Explore a Large New Chemogenomics Dataset Via SGD

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.

Links from SGD lead to multiple ways of exploring the full chemogenomics dataset.

« Previous Page
Next Page »