New & Noteworthy

Browse Metabolic Pathways at SGD

August 02, 2019

Did you know that SGD has over 200 curated biochemical pathways for you to explore? SGD’s YeastPathways is a database of metabolic pathways and enzymes in Saccharomyces cerevisiae. YeastPathways enables you to visualize yeast metabolism from large metabolic networks to individual pathways, and from biochemical reactions down to individual metabolites. Search tools and click-to-browse features in YeastPathways enable quick navigation and intuitive exploration of yeast metabolism.

As the first major content update to YeastPathways since 2012, we have recently updated 62 pathways with expertly-curated summaries on pathway biochemistry, genetics, regulation, and more. Compounds that were previously missing a structure have also now been updated, along with the stoichiometry and scheme of many pathway reactions. In addition to content updates, YeastPathways has also received a major software upgrade that provides new tools, pages, and visual aids.

With new content and software, YeastPathways is better than ever. Users new to YeastPathways will find that it’s easy to get started—simply access YeastPathways through the Function menu at SGD, then run a search for a pathway, compound, enzyme, or reaction on the YeastPathways homepage. You can also access YeastPathways through gene pages at SGD. Just run a search for your favorite metabolic enzyme at SGD (example: TPI1) and find the Pathways section on the locus summary page. Any pathways in which the protein is involved will be listed and linked to YeastPathways.

Check out YeastPathways and be sure to contact us if you have any questions or feedback!

Categories: Data updates

Sequence Variant Tracks Added to JBrowse

June 12, 2019

We are excited to announce that 50 new “Variants” data tracks are now available for use in our genome browsing tool JBrowse. Utilizing whole-genome sequencing data published by Song et al. (2015), these data tracks visualize how the sequences of 25 S. cerevisiae strains differ from that of the reference genome strain, S288C.

Two data tracks are available for each of the 25 strains: a track that indicates single nucleotide polymorphisms (SNPs) relative to strain S288C, and a track that shows insertions or deletions (“indels”) relative to S288C.

Accessing these new data tracks is easy—just enter JBrowse and click on the “Select tracks” tab on the upper-left hand part of the page. Then, select the “variants” category. You can also download the variants, annotation, and sequence files on these strains for use in your own analyses.

If you’re new to JBrowse, don’t miss out—getting started takes no time at all. For information on how to use this tool, be sure to check out the JBrowse playlist on the SGD YouTube Channel or visit the JBrowse help page. If you have any questions or feedback about the new “Variants” data tracks or about our genome browsing tool, please don’t hesitate to contact us.

Table of strains with “Variants” data tracks in JBrowse, along with links to download their respective dataset:

Strain (link to Variants tracks in JBrowse)File Download Link
BC187BC187_Stanford_2014_JRII00000000.zip
BY4741BY4741_Stanford_2014_JRIS00000000.zip
BY4742BY4742_Stanford_2014_JRIR00000000.zip
CEN_PK2-1CaCEN.PK2-1Ca_Stanford_2014_JRIV01000000.zip
D273-10BD273-10B_Stanford_2014_JRIY00000000.zip
DBVPG6044DBVPG6044_Stanford_2014_JRIG00000000.zip
FL100FL100_Stanford_2014_JRIT00000000.zip
FY1679FY1679_Stanford_2014_JRIN00000000.zip
JK9-3dJK9-3d_Stanford_2014_JRIZ00000000.zip
K11K11_Stanford_2014_JRIJ00000000.zip
L1528L1528_Stanford_2014_JRIK00000000.zip
RedStarRedStar_Stanford_2014_JRIL00000000.zip
RM11-1ARM11-1A_Stanford_2014_JRIP00000000.zip
SEY6210SEY6210_Stanford_2014_JRIW00000000.zip
Sigma1278b-10560-6BSigma1278b-10560-6B_Stanford_2014_JRIQ00000000.zip
SK1SK1_Stanford_2014_JRIH00000000.zip
UWOPS05_217_3UWOPS05-217-3_Stanford_2014_JRIM00000000.zip
W303W303_Stanford_2014_JRIU00000000.zip
X2180-1AX2180-1A_Stanford_2014_JRIX00000000.zip
Y55Y55_Stanford_2014_JRIF00000000.zip
YJM339YJM339_Stanford_2014_JRIE00000000.zip
YPH499YPH499_Stanford_2014_JRIO00000000.zip
YPS128YPS128_Stanford_2014_JRID00000000.zip
YPS163YPS163_Stanford_2014_JRIC00000000.zip
YS9YS9_Stanford_2014_JRIB00000000.zip

Categories: New Data

SGD Newsletter, Spring 2019

June 03, 2019

About this newsletter:

This is the Spring 2019 issue of the SGD newsletter. The goal of this newsletter is to inform our users about new features in SGD and to foster communication within the yeast community.

Contents:

  1. 25th Anniversary of the SGD Website
  2. JBrowse: S288C Transcriptome and New Data Tracks
  3. Proteome-wide Abundance Data
  4. Alliance of Genome Resources: 2.1 Release
  5. SGD at Biocuration 2019
  6. Recent publications from SGD Staff
  7. SGD Biocurators Out and About
  8. What else have we been up to lately?
  9. Upcoming Meetings

25th Anniversary of the SGD Website

SGD members in front of the 150-foot-diameter Stanford Dish.

SGD members in front of the 150-foot-diameter Stanford Dish.

Wednesday, May 8th, marked the 25th year that the SGD website has been live. Although we celebrated the 25th anniversary of the database last year, the actual website wasn’t online until the following year (when the “World Wide Web” had only been in existence for about 22 months). Starting in 1994, you could simply access SacchDB from our old server genome-www.stanford.edu to find information on your favorite model organism. We’d like to express our gratitude to all of our users, collaborators, advisors, staff, and anyone else who has supported SGD over the last 25 years. Without such a great community behind us, SGD would not be the fantastic resource it is today.

SGD staff celebrated the day by taking a tour around the Stanford campus and enjoying the beautiful California spring weather. Did you catch our throwback page for CDC6?

JBrowse: S288C Transcriptome and New Data Tracks

Access SGD’s “S288C Transcriptome Data Tracks in JBrowse” and other video tutorials on our YouTube Channel

We have recently equipped our genome browsing tool JBrowse with 9 new Transcriptome data tracks, making JBrowse an even more powerful way to explore the vast heterogeneity of the S288C transcriptome. These information-rich data tracks visualize RNA transcripts from the TIF-seq dataset published by Pelechano et al. (2013), enabling quick and easy viewing of the position, length, and abundance of transcript isoforms sequenced in the study.

SGD has also updated our JBrowse with an additional 157 new data tracks related to genome-wide experiments and omics data for you to explore. The categories added include: Transcription & Transcriptional Regulation; Histone Modification; Chromatin Organization; RNA Catabolism; Transposons; DNA Replication, Recombination, and Repair.

Proteome-wide Abundance Data

SGD has now incorporated proteome-wide protein abundance data obtained from a comprehensive meta-analysis by Ho et al., 2018. The authors normalized and combined 21 different S. cerevisiae protein abundance datasets—including data from both untreated cells and cells treated with various environmental stressors—to create a unified protein abundance dataset where all values are in the intuitive units of molecules per cell. Normalized abundance measurements and associated metadata from untreated and treated cells are displayed in tabular form in the experimental data section of protein-tabbed pages (e.g. CDC28). Several different controlled vocabularies have been employed to standardize the metadata display. In addition, calculated median abundance and median absolute deviation (MAD) values are displayed in the protein section of Locus Summary pages (e.g. PHO85).

Two new YeastMine templates have been created to provide access to these data: Gene → Protein Abundance and Gene → Median Protein Abundance.

Alliance of Genome Resources: 2.1 Release

In March, the Alliance released version 2.1. The release showcases the combined effort from SGD and the other core Alliance members. Notable improvements and new features include:

  • Disease Associations file on Downloads page
  • Gene Descriptions files on Downloads page
  • Updated Interactions table, including link outs to MOD pages
  • Updated Gene Ontology and Expression ribbon displays
  • Related Data links in Search results

SGD at Biocuration 2019

From April 7th-10th, PI Mike Cherry, Principal Biocuration Scientist Stacia Engel, Senior Biocuration Scientist Edith Wong, Biocuration Scientist Suzi Aleksander and Software Developer Felix Gondwe attended the International Society for Biocuration’s 12th International Biocuration Conference in Cambridge, UK. Several of our staff presented posters, while Edith also gave a great talk on her recent Database publication: Integration of Macromolecular Complex Data into the Saccharomyces Genome Database. Below are the posters and the talk SGD staff presented at Biocuration 2019. Click on any of the links to download the presentation.

Presentation

Presenter Presentation Title
Edith Wong “Integration of Macromolecular Complex Data into the Saccharomyces Genome Database”

Posters

Presenter Poster Title
Suzi Aleksander “In the Know About GO: A Newly Redesigned Website for the Gene Ontology”
Felix Gondwe “Downloading Data from SGD”
Edith Wong “Integration of Macromolecular Complex Data into the Saccharomyces Genome Database”

Recent publications from SGD Staff

  • Wong ED, Skrzypek MS, Weng S, Binkley G, Meldal BHM, Perfetto L, Orchard SE, Engel SR, Cherry JM; SGD Project (2018). Integration of Macromolecular Complex Data into the Saccharomyces Genome Database. Database (Oxford). 2019 Jan 1; 2019. doi: 10.1093/database/baz008 PMID:30715277
  • Howe DG, Blake JA, Bradford YM, Bult CJ, Calvi BR, Engel SR, Kadin JA, Kaufman TC, Kishore R, Laulederkind SJF, Lewis SE, Moxon SAT, Richardson JE, Smith C (2019). Model organism data evolving in support of translational medicine. Lab Anim (NY). 2018 Oct; 47(10):277-289. doi: 10.1038/s41684-018-0150-4 PMID:30224793

SGD Biocurators Out and About

You might see some of our SGD members at these upcoming events:


Senior Biocuration Scientist Rob Nash will be conducting a workshop at the annual Yeast Genetics & Genomics course at Cold Spring Harbor Laboratory July 23 – August 12, 2019.




SGD will be attending ICYGMB2019, the 29th International Conference on Yeast Genetics and Molecular Biology in Göteborg, Sweden August 18-22, 2019. If you’re going, be sure to attend the SGD Workshop on the afternoon of Day 4, Wednesday August 21!


What Else Have We Been Up To Lately?

Kevin MacPherson telling the SGD classic about 'why did the yeast cross the road?' at the Fungal Genetics Conference.[Photo by Matt Sachs, GSA]

Kevin MacPherson telling the SGD classic about ‘why did the yeast cross the road?’ at the Fungal Genetics Conference. [Photo by Matt Sachs, GSA]

  • Biocuration Scientist Kevin MacPherson gave tutorials at the Fungal Pathogen Genomics course at the Wellcome Genome Campus in May. He taught students how to use tools and resources at both SGD and the Candida Genome Database.
  • PI Mike Cherry, Principal Biocuration Scientist Stacia Engel, Senior Biocuration Scientist Edith Wong, Biocuration Scientist Suzi Aleksander and Software Developer Felix Gondwe attended the 2019 Gene Ontology Consortium Meeting in Cambridge, UK, in early April.
  • In March, Senior Biocuration Scientist Barbara Dunn and Biocuration Scientist Kevin MacPherson attended the Genetics Society of America’s 30th Fungal Genetics Conference in Pacific Grove, CA. They both presented posters, which are available to download in the table below.
Presenter Poster Title
Barbara Dunn “Associating Yeast Genes with Human Disease-related Genes at SGD”
Kevin MacPherson “Comparative Genomics at the Saccharomyces Genome Database”

Upcoming Meetings

Categories: Newsletter

Explore the S288C Transcriptome in JBrowse

April 25, 2019

We have recently equipped our genome browsing tool JBrowse with 9 new Transcriptome data tracks, making JBrowse an even more powerful way to explore the vast heterogeneity of the S288C transcriptome. These information-rich data tracks visualize RNA transcripts from the TIF-seq dataset published by Pelechano et al. (2013), enabling quick and easy viewing of the position, length, and abundance of transcript isoforms sequenced in the study.

You can easily access these new tracks by entering JBrowse and clicking on the left-hand “Select tracks” tab. They are located in the Transcriptome category. In addition to viewing the data in JBrowse, you can also download the .gff3 and .bw files for these tracks for use in your own analyses.

Check out our video tutorial from the SGD YouTube channel at the top of this page for a quick overview of the new transcriptome data tracks and how to access them. More information about these tracks and how SGD created them can also be found on our Genome Browser help page.

If you have any questions or feedback about the new Transcriptome data tracks or about our genome browser, please don’t hesitate to contact us.

Data tracks that visualize transcript isoforms that fully overlap a gene coding region:

Data Track Title Description
longest_full-ORF_transcripts_ypd This track contains the longest transcript overlapping each individual ORF completely for WT cells grown in glucose (ypd) media.
longest_full-ORF_transcripts_gal This track contains the longest transcript overlapping each individual ORF completely for WT cells grown in galactose (gal) media.
most_abundant_full-ORF_transcripts_ypd This track contains the most abundant transcript overlapping each individual ORF completely for WT cells grown in glucose (ypd) media.
most_abundant_full-ORF_transcripts_gal This track contains the most abundant transcript overlapping each individual ORF completely for WT cells grown in galactose (gal) media.
unfiltered_full-ORF_transcripts This track contains all transcripts that overlapped individual open reading frame (ORF) completely for WT cells grown in either glucose (ypd) or galactose (gal) media.

Data tracks that quantify the number of transcripts that cover a given nucleotide in the S288c genome:

Data Track Title Description
plus_strand_coverage_ypd For WT cells grown in glucose media (ypd), the amount of transcripts covering each position on the plus strand is represented in this track.
plus_strand_coverage_gal For WT cells grown in galactose media (gal), the amount of transcripts covering each position on the plus strand is represented in this track.
minus_strand_coverage_ypd For WT cells grown in glucose media (ypd), the amount of transcripts covering each position on the minus strand is represented in this track.
minus_strand_coverage_gal For WT cells grown in galactose media (gal), the amount of transcripts covering each position on the minus strand is represented in this track.

 

Categories: Tutorial, New Data

29th ICYGMB Early registration deadline extended to May 1

April 04, 2019


icygmb logo

The 29th International Conference on Yeast Genetics and Molecular Biology (ICYGMB) will be held at the the Swedish Exhibition & Congress Centre, Gothenburg, Sweden, from August 18-22, 2019.

The ICYGMB brings together scientists from all around the globe to present and discuss cutting-edge research on yeast. Described as the “most important event in yeast research“, the conference facilitates an environment where the international yeast community can freely exchange information, strike collaborations, and build new projects and alliances.

The 2019 meeting is enriched with over 50 speakers and a program that aims to provide an up-to-date overview of the most exciting topics in yeast research. The program includes lectures and workshops that discuss topics such as cell signaling, evolutionary genetics, aging and disease models, yeast biotechnology, and more.

Registration and abstract submission are now open. The early registration deadline has been extended to May 1. The abstract submission deadline is May 15. Special conference rates for accommodation are guaranteed only until June 1.

Categories: Conferences

Proteome-wide abundance data

March 11, 2019

SGD has now incorporated proteome-wide protein abundance data obtained from a comprehensive meta-analysis by Ho et al., 2018. The authors normalized and combined 21 different S. cerevisiae protein abundance datasets—including data from both untreated cells and cells treated with various environmental stressors—to create a unified protein abundance dataset where all values are in the intuitive units of molecules per cell. The original datasets were initially obtained using different methodologies (mass spectrometry, fluorescence microscopy, flow cytometry, and TAP-immunoblot), allowing Ho et al. to evaluate the strengths and weaknesses of these methods in addition to providing the community with a comprehensive reference map of the yeast proteome.

Normalized abundance measurements and associated metadata from untreated and treated cells are displayed in tabular form in the experimental data section of protein-tabbed pages (e.g. CDC28). Several different controlled vocabularies have been employed to standardize the metadata display. In addition, calculated median abundance and median absolute deviation (MAD) values are displayed in the protein section of Locus Summary pages (e.g. PHO85). Two new YeastMine templates have been created to provide access to these data: Gene -> Protein Abundance and Gene -> Median Protein Abundance

Special thanks to Brandon Ho and Grant Brown for generating this comprehensive reference map of protein abundance, and for their help in making this data available to the larger community.

Categories: New Data

Apply Now for the 2019 Yeast Genetics and Genomics Course

March 05, 2019

yeast_course_panorama

For almost 50 years, the legendary Yeast Genetics & Genomics course has been taught each summer at Cold Spring Harbor Laboratory. (OK, the name didn’t include “Genomics” in the beginning…). The list of people who have taken the course reads like a Who’s Who of yeast research, including Nobel laureates and many of today’s leading scientists. The application deadline is April 1st, so don’t miss your chance! Find all the details and application form here. This year’s instructors – Grant Brown, Greg Lang, and Elçin Ünal – have designed a course (July 23 – August 12) that provides a comprehensive education in all things yeast, from classical genetics through up-to-the-minute genomics. Students will perform and interpret experiments, learning about things like:

  • Finding and Analyzing Yeast Information Using SGD
  • Isolation and Characterization of Mutants
  • Yeast Transformation, Gene Replacement by PCR, and Construction and Analysis of Gene Fusions
  • Tetrad Analysis
  • Genome-scale Screens Using Synthetic Genetic Array (SGA) Methodology    
  • Deep Sequencing Applied to SNP Mapping and Deep Mutational Scanning
  • Exploring Synthetic Biology with CRISPR/Cas9-directed Engineering of Biosynthetic Pathways
  • Computational Methods for Data Analysis
  • Modern Cytological Approaches Including Epitope Tagging and Imaging Yeast Cells Using Fluorescence Microscopy

Techniques have been summarized in a completely updated course manual, which was recently published by CSHL Press.

IMG_2185

There’s fierce competition between students at CSHL courses in the Plate Race, a relay in which teams carry stacks of 40 Petri dishes (used, of course).

Scientists who aren’t part of large, well-known yeast labs are especially encouraged to apply – for example, professors and instructors who want to incorporate yeast into their undergraduate genetics classrooms; scientists who want to transition from mathematical, computational, or engineering disciplines into bench science; and researchers from small labs or institutions where it would otherwise be difficult to learn the fundamentals of yeast genetics and genomics. Significant stipends (in the 30-50% range of total fees) are available to individuals expressing a need for financial support and who are selected into the course.

Besides its scientific content, the fun and camaraderie at the course is also legendary. In between all the hard work there are late-night chats at the bar and swimming at the beach. There’s a fierce competition between students at the various CSHL courses in the Plate Race, which is a relay in which teams have to carry stacks of 40 Petri dishes (used, of course). There’s also a sailboat trip, a microscopy contest, and a mysterious “Dr. Evil” lab!

The Yeast Genetics & Genomics Course is loads of fun – don’t miss out!

Categories: Announcements

Register for the 29th International Conference on Yeast Genetics and Molecular Biology (ICYGMB)

January 24, 2019


icygmb logo

The 29th International Conference on Yeast Genetics and Molecular Biology (ICYGMB) will be held at the the Swedish Exhibition & Congress Centre, Gothenburg, Sweden, from August 18-22, 2019.

The ICYGMB brings together scientists from all around the globe to present and discuss cutting-edge research on yeast. Described as the “most important event in yeast research“, the conference facilitates an environment where the international yeast community can freely exchange information, strike collaborations, and build new projects and alliances.

The 2019 meeting is enriched with over 50 speakers and a program that aims to provide an up-to-date overview of the most exciting topics in yeast research. The program includes lectures and workshops that discuss topics such as cell signaling, evolutionary genetics, aging and disease models, yeast biotechnology, and more.

Registration and abstract submission are now open. The early registration deadline is April 1. The abstract submission deadline for oral presentations is May 15, whereas July 31 is the deadline for abstracts that will be included in the Conference Abstract book.

Categories: Conferences

New Data Tracks added to JBrowse

January 15, 2019


SGD has updated our JBrowse genome browser with 157 new data tracks related to genome-wide experiments and omics data for you to explore. You can easily access these new tracks, which visualize data from the twenty publications listed below, by entering JBrowse and clicking on the left-hand “Select tracks” tab. Then, search for the PMID associated with the reference of interest.

Note that some references appear more than once, as they have multiple data tracks associated that belong to different categories in JBrowse.

For more information on using JBrowse, be sure to check out our playlist of JBrowse video tutorials on YouTube. If you have any questions or feedback about the new tracks or about our genome browser, please don’t hesitate to contact us.

Transcription & Transcriptional Regulation

Reference PMID Description in JBrowse
Baptista et al. (2017) 28918903 ChEC-seq to map the genome-wide binding of the SAGA coactivator complex in budding yeast.
Castelnuovo et al. (2014) 24497191 Genome-wide measurement of whole transcriptome versus histone modified mutants
El Hage et al. (2014) 25357144 Genome-wide distribution of RNA-DNA hybrids identifies RNase H targets in tRNA genes retrotransposons and mitochondria.
Freeberg et al. (2013) 23409723 Mapped regions of untranslated, polyadenylated transcriptome bound by RNA-binding proteins (RBPs)
Kang et al. (2015) 25213602 Genome-wide transcript profiling by paired-end ditag sequencing
Lee et al. (2018) 29339748 ChIP-Seq, mRNA-seq, ATAC-seq, and MNase-seq samples in wild-type (WT) and various mutants were prepared using Saccharomyces cerevisiae.
Park et al. (2014) 24413663 Simultaneous mapping of RNA ends by sequencing (SMORE-seq) to identify the strongest transcription start sites and polyadenylation sites genome-wide
Rossbach et al. (2017) 28924058 Authors utilized the Calling Cards Ty5 retrotransposon insertion method to identify binding sites of cdc7kd, cdc7kdΔcterm and Gal4 transcription factor within the yeast genome.
Schaughnency et al. (2014) 25299594 Genome-wide identification of transcription termination sites; pA pathway and non-polyadenylation pathway in strains missing Sen1p or Nrd1p

Histone Modification

Reference PMID Description in JBrowse
Castelnuovo et al. (2014) 24497191 Genome-wide measurement of whole transcriptome versus histone modified mutants
Hu J. et al. (2015) 26628362 ChIP-seq and MNase-seq to determine how histone modifications and chromatin structure directly regulate meiotic recombination. Identified acetylation of histone H4 at Lys44 (H4K44ac) as a new histone modification
Joo et al. (2017) 29203645 Next-Generation-Sequecing (NGS)-derived genome-wide occupancy of TAF (Taf1) compared with other basal initiation components (TBP and TFIIB), histones (H3, H4, Htz1 and H4 acetylation) and histone regulator complexes (Swr1, Bdf1) in S. cerevisiae
Kniewel et al. (2017) 28986445 ChIP-seq to determine the whole-genome enrichment of Mek1 targeted histone H3 threonine 11 phosphorylation (H3 T11ph) during Saccharomyces cerevisiae meiosis.
Lee et al. (2018) 29339748 ChIP-Seq, mRNA-seq, ATAC-seq, and MNase-seq samples in wild-type (WT) and various mutants were prepared using Saccharomyces cerevisiae.
Weiner et al. (2018) 25801168 Examining chromatin dynamics through genome-wide mapping of 26 histone modifications at 0 4 8 15 30 and 60 minutes after diamide addition using MNase-ChIP

Chromatin Organization

Reference PMID Description in JBrowse
Chereji et al. (2014) 29426353 Genome binding/occupancy profiling of single nucleosomes and linkers by high throughput sequencing
Gutierrez et al. (2017) 29212533 Authors sought to correct sequence bias of MNase-Seq with a method based on the digestion of naked DNA and the use of the bioinformatic tool DANPOS
Hu Z. et al. (2014) 24532716 Genome-wide measurement of nucleosome occupancy during cell aging
Hu J. et al. (2015) 26628362 ChIP-seq and MNase-seq to determine how histone modifications and chromatin structure directly regulate meiotic recombination. Identified acetylation of histone H4 at Lys44 (H4K44ac) as a new histone modification
Joo et al. (2017) 29203645 Next-Generation-Sequecing (NGS)-derived genome-wide occupancy of TAF (Taf1) compared with other basal initiation components (TBP and TFIIB), histones (H3, H4, Htz1 and H4 acetylation) and histone regulator complexes (Swr1, Bdf1) in S. cerevisiae
Lee et al. (2018) 29339748 ChIP-Seq, mRNA-seq, ATAC-seq, and MNase-seq samples in wild-type (WT) and various mutants were prepared using Saccharomyces cerevisiae.

RNA Catabolism

Reference PMID Description in JBrowse
Geisberg et al. (2014) 24529382 Half-lives of 21,248 mRNA 3_ isoforms in yeast were measured by rapidly depleting RNA polymerase II from the nucleus and performing direct RNA sequencing throughout the decay process.
Smith et al. (2014) 24931603 Identification of genome-wide transcripts; looking at nonsense-mediated RNA decay pathway

Transposons

Reference PMID Description in JBrowse
Lee et al. (2018) 29339748 ChIP-Seq, mRNA-seq, ATAC-seq, and MNase-seq samples in wild-type (WT) and various mutants were prepared using Saccharomyces cerevisiae.
Michel et al. (2017) 28481201 Genome-wide examination of protein function by using transposons for targeted gene disruption
Rossbach et al. (2017) 28924058 Authors utilized the Calling Cards Ty5 retrotransposon insertion method to identify binding sites of cdc7kd, cdc7kdΔcterm and Gal4 transcription factor within the yeast genome.

DNA Replication, Recombination, and Repair

Reference PMID Description in JBrowse
Mao et al. (2017) 28912372 Map of N-methylpurine (NMP) lesion alkalation damage across the yeast genome

 

Categories: New Data

Disease Pages at SGD: Linking Yeast Genetics and Human Disease

December 22, 2018


neurodegenerative_disease

SGD’s Disease Ontology page for neurodegenerative disease

To promote the use of yeast as a catalyst for biomedical research, SGD utilizes the Disease Ontology (DO) to describe human diseases that are associated with yeast homologs. Disease Ontology annotations to yeast genes are now available through SGD’s new Disease pages. Each page corresponds to a Disease Ontology term, such as amyotrophic lateral sclerosis, and lists out all yeast genes annotated to the term by SGD.

Yeast genes with one or more human disease associations will also have a new Disease Summary tab (example: MIP1), accessible from the genes’ respective locus pages. The Disease summary tab shows all manually curated, high-throughput, and computational disease annotations for the yeast gene. Additionally, these pages feature a network diagram that depicts shared disease annotations for other yeast genes and their human homologs.

network_diagram_disease

The shared disease annotations diagram for MIP1

For more information, check out SGD’s Disease Ontology help page. Explore the new Disease pages and features, and be sure to let us know if you have any feedback or questions.

Categories: New Data

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