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  • Title: Saccharomyces Genome Database
    Descriptive info: .. About.. Blog.. Download.. Site Map.. Help.. Advanced Search via.. YeastMine.. Home.. Analyze.. BLAST.. Fungal BLAST.. GO Term Finder.. GO Slim Mapper.. Pattern Matching.. Design Primers.. Restriction Mapper.. Sequence.. Genome Browser.. Gene / Sequence Resources.. Reference Genome.. Genome Snapshot.. Chromosome History.. Systematic Sequencing Table.. Original sequence papers.. Strains and species.. Align Strain Sequences.. Synteny Viewer.. Homology.. Fungal Alignment.. PDB Search.. Resources.. UniProtKB (EBI).. InterPro (EBI).. HomoloGene (NCBI).. YGOB (Trinity College).. Function.. GO.. GO Slim Mapping File.. Expression.. Biochemical Pathways.. Phenotypes.. Search for phenotypes.. Browse all phenotypes.. Interactions.. YeastGFP.. GO Consortium.. BioGRID (U.. Toronto).. Literature.. Full-text Search (Textpresso).. New Yeast Papers.. YeastBook.. Genome-wide Analysis Papers.. PubMed (NCBI).. PubMed Central (NCBI).. Google Scholar.. Community.. Colleague Information.. Find a Colleague.. Add or Update Info.. Find a Yeast Lab.. Career Resources.. Meetings.. Future.. Yeast Genetics Meetings.. Nomenclature.. Submit a Gene Registration.. Gene Registry.. Nomenclature Conventions.. Global Gene Hunter.. Methods and Reagents.. Strains and Constructs.. Reagents.. Protocols and Methods.. Find a Clone.. Historical Data.. Physical Genetic Maps.. Genetic Maps.. Gene Summary Paragraphs.. WIki.. About SGD.. The.. Saccharomyces.. Genome Database (SGD) provides comprehensive integrated biological information for the budding yeast.. Saccharomyces cerevisiae.. along with search and analysis tools to explore these data, enabling the discovery of functional relationships between sequence and gene products in fungi and higher organisms.. Upcoming Meetings.. Bay Area Yeast and Other Fungi Symposium.. March 30, 2013.. - UC Berkeley.. 11th International Yeast Lipid Conference.. May 29, 2013.. - Halifax, Nova Scotia, Canada.. Abstract deadline: April 19, 2013.. 5th Conference on Physiology of Yeast and Filamentous Fungi - PYFF5.. June 4, 2013.. -  ...   and dividing.. When.. read more >.. Ghosts of Centromeres Past.. 01/28/2013.. Every cell needs to correctly divvy up its chromosomes when it divides.. Otherwise one cell would end up with too many chromosomes, the other with too few and they’d both probably die.. Cells have developed elaborate machinery to make sure each daughter gets the right chromosomes.. One key part of the machinery is the centromere.. This is the part of the chromosome that attaches to the mitotic spindle so the chromosome gets dragged to the right.. Autophagy’s (Atg)9th Symphony.. 01/17/2013.. (Please click the musical note and listen to the music while reading.. ) The music you’re listening to starts off with a marimba.. Then a flute joins in and as the marimba fades, in comes a shamisen.. The piece progresses similarly with a harp, and then ends with the reappearance of the marimba.. A nice, jaunty little piece of music.. This song is actually a tool for learning about autophagy in the yeast S.. cerevisiae.. Autophagy.. P is for Protection (not Processing).. 01/11/2013.. Growing and dividing are dangerous work for a cell.. Making all that energy throws off free radicals that mutate DNA and wreak havoc with delicate intracellular machinery.. Given this it might seem surprising that just sitting there, not growing, is dangerous too.. And yet it looks like it is.. When a cell runs out of food and goes into a quiescent state, it creates ribonucleoprotein (RNP) complexes called processing bodies (P bodies).. In a new study.. SGD Home.. Terms of Use.. Stanford University, Stanford, CA 94305..

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  • Title: About : Saccharomyces Genome Database
    Descriptive info: Biocuration.. How to Cite SGD.. SGD Publications.. SGD Newsletter Archives.. Scientific Advisory Board.. SGD Staff Members.. Technical Specifications.. Curated Data.. Genomics.. Published Datasets.. Unpublished Data.. Database.. Video Tutorials.. Basic Navigation.. General Features.. Getting Started.. Search SGD.. Glossary.. Locus Summary Page.. Locus History.. YeastGenome App Information.. Gene/Sequence Resources.. Reference Sequence.. PDB Homologs.. All Associated Sequences.. Chromosomal Features Map.. Gene Ontology (GO).. Protein Information.. Transcription Factor Binding Sites.. Literature Guide.. Curated Paper.. Find a yeast lab.. Clone Pages.. Physical and Genetic Maps.. Genomic View.. Genome Database (SGD,.. http://www.. yeastgenome.. org.. ) is the community resource for the budding yeast.. The SGD project provides encyclopedic information about the yeast genome and its genes, proteins, and other encoded features.. Experimental results on the functions and interactions of yeast genes, as reported in the peer-reviewed literature, are extracted by high-quality manual curation and integrated within a well-developed database.. These data are combined with quality high-throughput results and provided through Locus Summary pages, a powerful query engine, and a rich genome browser.. This  ...   research community by maintaining the reference genomic chromosomal sequence, overseeing.. S.. genetic nomenclature, functioning as a central hub for researchers to share contact information, and supplying a list of yeast labs.. All of the data in SGD are freely accessible to researchers and educators worldwide via web pages designed for optimal ease of use.. Project Funding and Location.. The SGD is funded by the National Human Genome Research Institute (NHGRI), US National Institutes of Health [5U41HG001315-18].. Funding is also provided by the Gene Ontology Consortium project funded by the NHGRI [2U41HG002273-13] and the InterMOD project [5R01HG004834-04].. The SGD is part of the.. Department of Genetics.. at the School of Medicine, Stanford University and located within the Center for Genomics and Personalized Medicine.. Viewing SGD.. SGD is best viewed with a recent version of Chrome, Safari or Firefox with javascript enabled.. Contacting SGD.. Please send comments or questions to the SGD project via this.. form.. Alternatively, email us at sgd-helpdesk@lists.. stanford.. edu or call us at (650) 725-8956..

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  • Title: Blog : Saccharomyces Genome Database
    Descriptive info: Recent Posts.. Be Good, For Adaptation s Sake.. Categories.. Conferences.. Data updates.. New Data.. News and Views.. Newsletter.. Research Spotlight.. Tutorial.. Uncategorized.. Website changes.. Yeast and Human Disease.. Archives.. February 2013.. January 2013.. December 2012.. November 2012.. October 2012.. September 2012.. August 2012.. July 2012.. June 2012.. May 2012.. April 2012.. March 2012.. February 2012.. January 2012.. December 2011.. November 2011.. October 2011.. September 2011.. August 2011.. July 2011.. June 2011.. May 2011.. February 2011.. February 6, 2013.. When this happens, the cell stops the cell cycle at the next checkpoint, fixes what is wrong, and then starts the cell cycle back up again where it left off.. Scientists have learned a lot about how the keys are taken from cells, but not a whole lot about how they get them back.. Fong and coworkers.. help to rectify this situation in a new study out in.. GENETICS.. There they identified proteins key to releasing a yeast cell from its S-phase checkpoint.. If a cell’s DNA is damaged while it is growing and dividing, replication is slowed at the S-phase checkpoint.. This gives the cell a chance to fix the DNA before it is copied.. The authors found that in the absence of the.. DIA2.. gene, yeast cells had trouble getting replication up and running again.. This implies that this gene is required for yeast to overcome the S-phase checkpoint.. The cell needs.. to get its keys back.. Dia2p is an F-box protein involved in identifying certain proteins for destruction.. It is one of several interchangeable subunits that provide specificity to the.. SCF ubiquitin ligase.. complex.. The idea would be that Dia2p is important for degrading the keeper of the keys, the protein responsible for stopping the cell cycle in the S-phase.. To test whether Dia2p is important for checkpoint recovery, Fong and coworkers first activated the S-phase checkpoint by adding the DNA damaging agent MMS.. Then they removed the MMS and measured how long it took the cells to finish copying their DNA.. dia2Δ.. mutant was significantly slower than wild type.. Given that Dia2p is involved in ubiquitin-mediated degradation, the authors reasoned that it may help a cell get out of S-phase arrest by degrading a protein that was keeping it there.. To find this “keeper of the keys,” Fong and coworkers looked for mutations that rescued.. cells in the presence of high levels of MMS.. The idea is that if they knock out the gene that is keeping the.. cells arrested, then the cells could overcome the block caused by the MMS.. One of the genes that came up in the screen was.. MRC1.. To confirm that Dia2p and Mrc1p work together in releasing a yeast cell from the S-phase checkpoint, the authors constructed a double mutant carrying.. and a mutant version of.. ,.. mrc1AQ.. , that they knew was checkpoint defective.. Indeed, the double mutant behaved like wild type in their checkpoint recovery assay.. Since the mutant Mrc1-AQp could not keep cells at the checkpoint, there was no need for Dia2p to target it for degradation.. The double mutant cell never let go of its keys.. The simplest model to explain what happens in wild type is that when its DNA is damaged, a cell is prevented from progressing through S-phase by Mrc1p.. Then when the DNA is repaired, Dia2p, providing specificity to the SCF ubiquitin ligase complex, targets Mrc1p for degradation.. The cell is now released, allowing the cell cycle to continue.. The authors did a lot more work that we won’t go into here, but suffice it to say that Dia2p and Mrc1p are not the only players involved in releasing a cell from the S-phase checkpoint.. There were other genes, both identified and unidentified, that came up in their screen.. These will need to be studied as well.. And this isn’t all just interesting from a scientific standpoint.. Many cancer treatments work by damaging the cancer cell’s DNA while it is growing and dividing.. A better understanding of how cells are arrested and released may lead to better cancer treatments.. Category:.. Tags:.. DNA replication checkpoint.. >.. January 28, 2013.. A different kind of ghost may be embedded in the yeast genome.. This is the part of the chromosome that attaches to the mitotic spindle so the chromosome gets dragged to the right place.. Given how precise this dance is, it is surprising how sloppy the underlying centromeric DNA tends to be in most eukaryotes.. It is very long with lots of repeated sequences which make it very tricky to figure out which DNA sequences really matter.. An exception to this is the centromeres found in some budding yeasts like.. These centromeres are around 125 base pairs long with easily identifiable important DNA sequences.. The current thought is that budding yeast used to have the usual diffuse, regional centromeres but that over time, they evolved these newer, more compact centromeres.. Work in a new study published in PLOS Genetics by.. Lefrançois and coworkers.. lends support to this idea.. These authors found that when they overexpressed a key centromeric protein,.. Cse4p.. (or CenH3 in humans), new centromere complexes formed on DNA sequences near the true centromeres.. The authors termed these sequences CLR’s or Centromere-Like Regions.. And they showed that these complexes are functional.. When Lefrançois and coworkers kept the true centromere from functioning on chromosome 3 in cells overexpressing Cse4p, 82% of the cells were able to properly segregate chromosome 3.. This compares to the 62% of cells that pull this off with normal levels of Cse4p.. The advantage disappeared when the CLR on chromosome 3 was deleted.. A close look at the CLRs showed that they had a lot in common with both types of centromeres.. They had an AT-rich 90 base pair sequence that looked an awful lot like the kind of sequence  ...   these key components squirreled away and protected survive better than those cells where these proteins and RNAs have degraded.. As a final point, it is important to mention why this matters (besides the excitement of figuring out how things work).. Quiescent yeast cells are used as models for aging in higher eukaryotes like us.. Perhaps by understanding how to make a yeast cell better survive this non-growing state, we can learn something about how to make people live longer too.. P bodies.. quiescence.. December 17, 2012.. You may never have herded cows.. But in one way or another, you’ve certainly experienced the tragedy of the commons.. When herders get away with cheating, everyone loses.. The same thing is often true for yeast.. Image from Wikimedia Commons.. This happens when a village shares a pasture that can only feed a certain number of cows.. For the system to work, everyone has to cooperate and keep the total number of cows under that limit.. But inevitably, one cheater comes along and adds extra cows to his herd.. At no immediate cost to himself, he gets all the benefits of the extra cows.. But then tragedy kicks in as the pasture is overgrazed until no one can have any cows.. This doesn’t just happen out in the village.. We can see it in the overfishing of the oceans, the production of carbon dioxide contributing to global warming, the milking of investors on Wall Street, and many other aspects of modern life.. But perhaps surprisingly, we can even see it in cultures of the humble yeast.. In a recent issue of the Proceedings of the National Academy of Sciences,.. Waite and Shou.. set up a yeast system to look at the factors influencing the tragedy of the commons.. In human society, sometimes cheaters cause a collapse, but other times, cooperators get together and exile the cheaters from the village.. You might think that since yeast aren’t quite as smart as humans, in yeast “society” the cheaters would always win.. However, Waite and Shou found that sometimes the cheaters were marginalized or even driven out, and the cooperators thrived!.. To do these experiments, the researchers set up a very clever pasture in miniature.. They engineered three strains, each marked with a different-colored fluorescent marker so they could be distinguished from each other.. The two “cooperator” strains needed each other to survive on minimal medium: one required lysine and produced excess adenine, while the other required adenine and produced excess lysine.. The “cheater” strain required lysine but it didn’t provide any nutrients.. So the cheater needed one of the cooperators to survive, but didn’t contribute anything to the common good.. As we might expect, being cooperative has a cost.. The generous production of extra nutrients made the cooperator grow slower than the otherwise identical cheater strain.. So you would predict that if you mixed the cooperators and the cheater in equal numbers and grew them together, the cheater would take over and collapse the culture every time.. However, when the researchers mixed all three strains in a 1:1:1 ratio and grew lots of replicate cultures, they found that cheaters didn’t always prosper.. Sometimes the nice guys finished first.. Of course some of the cultures did collapse under the influence of the rapacious cheaters.. After a while these cultures stopped growing and turned out to be made up of mostly dead or dying cheater cells.. The cheaters had taken over the culture, selfishly using up the lysine until eventually there was not enough to continue growing.. But unexpectedly, other replicate cultures were growing much faster, at rates similar to cultures without any cheaters.. In these cultures, the two cooperator strains had either dominated the culture or even driven the cheaters extinct!.. To explain this, the researchers proposed that the intense selection pressure led to an adaptive race between cooperators and cheaters.. In surviving cultures, the rare cooperator with a small advantage had outcompeted the cheaters.. To confirm this, they took a close look at the winning cooperators.. They found that the fitness advantage could be inherited, so they used whole-genome resequencing to find out why the cooperators were outcompeting the cheaters.. They kept finding mutations in the same five genes.. These genes all made sense, as mutating them would help in an environment with limited amounts of lysine.. For example, most of the mutations were found in.. ECM21.. and.. DOA4.. Both of these gene products are important in pathways that break down proteins like permeases.. Knocking them out would keep the permeases around longer, making for better lysine uptake.. But this newfound advantage did not come without a price.. While cheating is usually a good short term strategy, it doesn t always work out so well in the long term.. The researchers tested directly whether the adaptive mutations improved growth in limiting amounts of lysine.. Without exception, they did.. But almost all these strains grew more slowly in abundant lysine than did their ancestor strains.. That explains why these mutant strains only became a significant proportion of the population late in the life of the culture, when lysine levels were very low.. The same mutations can arise in both cooperators and cheaters, of course.. But when cheaters become better at growing in low lysine levels, they just become that much better at making themselves extinct.. When cooperators get better at growing in low lysine levels, they are better able to keep growing and keep the cheaters at bay.. So the take-home lesson is that cooperation does pay, after all.. Especially in a constantly changing environment, cooperators can often win the adaptive race and squelch the cheaters.. Maybe we should take a hint from little.. that being kind to each other is not only a nice thing to do, it’s in all of our best interests!.. adaptation..

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  • Title: Download Data : Saccharomyces Genome Database
    Descriptive info: All data at SGD can be downloaded in multiple ways.. can be used to download custom files.. YeastMine is a powerful search and retrieval tool that allows for sophisticated queries.. Data can be downloaded in customizable user-defined formats.. Certain types of data for individual genes can be downloaded from the page you are viewing.. For example, look for the 'Download Data' link at the bottom of the phenotypes or interactions page for an individual gene.. Data in pre-defined formats can be downloaded from our Downloads server.. The major categories of data are described below.. Each page describes the contents of the categories with links to the  ...   project.. - RNA and protein encoding sequences,.. reference genome sequence release packages, genome release liftOver files and other.. strains and.. Saccharomyces senso stricto.. species genomic sequences.. - genome-wide and proteome-wide comparative analysis results.. - microarray experiment data files and datasets that populate the SGD expression analysis tool SPELL (Serial Pattern of Expression Levels Locator).. - collected datasets and supplemental data files from published work that have been curated at SGD, including data from high-throughput studies such as, but not limited to transcription and nucleosome occupancy.. - historical data from retired yeast tools and unpublished analyses.. - SQL schema definition files that specify the SGD Oracle database..

    Original link path: /download-data
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  • Title: Site Map : Saccharomyces Genome Database
    Descriptive info: The SGD site map provides links to the major resources and tools in SGD..

    Original link path: /site-map
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  • Title: Help : Saccharomyces Genome Database
    Descriptive info: SGD maintains help documentation for tools and data types.. Help pages for individual tools can be accessed via the question mark symbol found in the upper right section of that page.. Help pages are context dependent; the link will point to documentation for the page you are viewing.. Tutorials.. - short tutorials describing specific aspects of various SGD tools and features, including Biochemical Pathways, the YeastMine data search tool,  ...   features.. - getting started in SGD, finding information about your favorite genes, what's known about the.. genome, glossary of terms.. - DNA or protein sequences, function annotation, primers, restriction maps.. - find and retrieve, compare strains and species, chromosome history, reference genome.. - interactions, phenotypes, localization, expression, pathways.. - search full text, find new papers, genome-wide analysis papers.. - colleagues, job postings, meetings, reserve gene names, genetic maps, and more..

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  • Title: S. cerevisiae WU-BLAST2 Search
    Descriptive info: WU-BLAST2 Search.. Datasets updated: December 6, 2011.. This form allows BLAST searches of.. sequence datasets.. To search multiple fungal sequences, go to the.. Fungal BLAST search form.. Query Comment (optional, will be added to output for your use):.. NOTE: If the input sequence is less than 30 letters you should change the default Cutoff Score value to something less than 100 or you can miss matches.. or.. Upload Local TEXT File: FASTA, GCG, and RAW sequence formats are okay.. WORD Documents do not work unless saved as TEXT.. Type or Paste a Query Sequence : (FASTA or RAW format, or No Comments, Numbers are okay).. Choose the Appropriate BLAST Program:.. BLASTN - nucleotide query to nucleotide db.. BLASTP - protein query to protein db.. BLASTX - translated (6 frames) nucl.. query to protein db.. TBLASTX - transl.. (6 frames) nucl.. query to transl (6) nt db.. TBLASTN - protein query to translated (6 frames) nt db.. Choose one or more Sequence Datasets:.. Select or unselect multiple datasets by pressing the Control (PC) or Command (Mac) key while clicking.. Selecting a category label selects all datasets in that category.. REFERENCE (S288C) GENOMIC SEQUENCE.. Nuclear chromosomes (DNA).. Mitochondrial chromosome (DNA).. 2-micron plasmid (DNA).. GENES: PROTEIN ENCODING (S288C).. Open Reading Frames (DNA or Protein).. Genomic (Coding and Introns) Sequences of defined ORFs (DNA).. Open Reading Frames + 1000 bp Up Downstream (DNA).. GENES: ncRNA (S288C).. RNA Coding (DNA).. RNA Genomic (coding and introns) (DNA).. RNA Genomic + 1000 bp Up Downstream (DNA).. NON-GENIC SEQUENCES (S288C).. Intergenic Genomic DNA between ORFs, RNA genes, LTRs Tys (DNA).. OTHER PUBLIC SEQUENCES.. Yeast public sequences from GenBank and UniProt (DNA  ...   (DNA or Protein).. cerevisiae strain CBS7960_cds (DNA or Protein).. PK113-7D_cds (DNA or Protein).. cerevisiae strain CLIB215_cds (DNA or Protein).. cerevisiae strain CLIB324_cds (DNA or Protein).. cerevisiae strain CLIB382_cds (DNA or Protein).. cerevisiae strain EC1118_cds (DNA or Protein).. cerevisiae strain EC9-8_cds (DNA or Protein).. cerevisiae strain FL100_cds (DNA or Protein).. cerevisiae strain FostersB_cds (DNA or Protein).. cerevisiae strain FostersO_cds (DNA or Protein).. cerevisiae strain JAY291_cds (DNA or Protein).. cerevisiae strain Kyokai7_cds (DNA or Protein).. cerevisiae strain LalvinQA23_cds (DNA or Protein).. cerevisiae strain M22_cds (DNA or Protein).. cerevisiae strain PW5_cds (DNA or Protein).. cerevisiae strain RM11-1a_cds (DNA or Protein).. cerevisiae strain Sigma1278b_cds (DNA or Protein).. cerevisiae strain T73_cds (DNA or Protein).. cerevisiae strain T7_cds (DNA or Protein).. cerevisiae strain UC5_cds (DNA or Protein).. cerevisiae strain Vin13_cds (DNA or Protein).. cerevisiae strain VL3_cds (DNA or Protein).. cerevisiae strain W303_cds (DNA or Protein).. cerevisiae strain Y10_cds (DNA or Protein).. cerevisiae strain YJM269_cds (DNA or Protein).. cerevisiae strain YJM789_cds (DNA or Protein).. cerevisiae strain YPS163_cds (DNA or Protein).. cerevisiae strain ZTW1_cds (DNA or Protein).. Options:.. For descriptions of BLAST options and parameters, refer to the.. BLAST documentation at NCBI.. Output format :.. gapped alignments.. nongapped alignments.. Comparison Matrix :.. BLOSUM62.. BLOSUM100.. PAM40.. PAM120.. PAM250.. Cutoff Score (S value) :.. default.. 30.. 50.. 70.. 90.. 110.. Word Length (W value) :.. 15.. 14.. 13.. 12.. 11.. 10.. 9.. 8.. 7.. 6.. 5.. 4.. 3.. 2.. Default = 11 for BLASTN, 3 for all others.. Expect threshold (E threshold) :.. 0.. 0001.. 01.. 1.. 100.. 1000.. Number of best alignments to show :.. 25.. 200.. 400.. 800.. Filter options :.. On.. Off.. DUST file for BLASTN, SEG filter for all others..

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  • Title: Fungal Genomes Search using WU-BLAST2
    Descriptive info: Fungal Genomes Search.. using WU-BLAST2.. Datasets updated: Feb 02, 2011 via.. SeqHound tools from DogBoxOnline.. This form allows BLAST searches of multiple fungal sequence datasets.. To restrict your search to S.. cerevisiae with additional BLAST search options, go to the.. BLAST search form.. Both DNA and protein datasets are available, except when noted.. With a protein sequence query, use the TBLASTN program in addition to the BLASTP program for comprehensive results, since some protein datasets are incomplete (see.. Help documentation.. ).. - - - - - - - - - - - - - Ascomycetes - - - - - - - - - - - - - - -.. Alternaria brassicicola.. Ashbya gossypii.. Aspergillus clavatus.. Aspergillus flavus.. Aspergillus fumigatus.. Aspergillus nidulans.. Aspergillus niger.. Aspergillus oryzae.. Aspergillus parasiticus.. Aspergillus terreus.. Blastocladiella emersonii.. Botrytis cinerea.. Candida albicans.. Candida dubliniensis.. Candida glabrata.. Candida tropicalis.. Chaetomium globosum.. Clavispora lusitaniae.. Coccidioides immitis.. Coccidioides posadasii.. Cryphonectria parasitica.. Debaryomyces hansenii.. Fusarium sporotrichioides.. Fusarium virguliforme.. Gibberella moniliformis.. Gibberella zeae.. Hebeloma cylindrosporum.. Histoplasma capsulatum.. Hypocrea jecorina.. Kazachstania exigua.. Kluyveromyces delphensis.. Kluyveromyces lactis.. Kluyveromyces marxianus.. Lachancea thermotolerans.. Lodderomyces  ...   - - - - - - - - - - - Basidiomycetes - - - - - - - - - - - - - - -.. Coprinopsis cinerea.. Cryptococcus neoformans var.. grubii.. neoformans.. Cryptococcus gattii.. Phakopsora pachyrhizi.. Phanerochaete chrysosporium.. Pleurotus ostreatus.. Ustilago maydis.. - - - - - - - - - - - - Chytridiomycota - - - - - - - - - - - - - - -.. Batrachochytrium dendrobatidis.. Spizellomyces punctatus.. - - - - - - - - - - - - Glomeromycota - - - - - - - - - - - - - - - -.. Glomus intraradices.. - - - - - - - - - - - - Microsporidia - - - - - - - - - - - - - - - -.. Antonospora locustae.. Encephalitozoon cuniculi.. Encephalitozoon intestinalis.. - - - - - - - - - - - - - Other Public Sequences - - - - - - - - - -.. Fungal Mitochondrial Genomes (DNA only).. Fungal Sequences Not in the above datasets..

    Original link path: /cgi-bin/blast-fungal.pl
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  • Title: Web Primer: DNA and Purpose Entry
    Descriptive info: Web Primer: DNA and Purpose Entry.. Sequences of.. primer sets.. available to the community.. DNA Source.. [info].. Locus.. : Enter a standard gene name or systematic ORF name (i.. e.. ACT1, YKR054C).. OR.. Enter the DNA Sequence (numbers are OK, but comments should be removed).. Purpose: PCR or Sequencing.. PCR.. or.. SEQUENCING..

    Original link path: /cgi-bin/web-primer
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  • Title: Gene/Sequence Resources
    Descriptive info: Gene/Sequence Resources.. Try.. Yeastmine.. for flexible queries and fast retrieval of chromosomal features, sequences, GO annotations, interaction data and phenotype annotations.. The video tutorial.. Template Basics.. describes how to quickly retrieve this type of information in YeastMine.. To find a comprehensive list of SGD's tutorials describing the many other features available in YeastMine and how to use them, visit SGD's.. YeastMine Video Tutorials.. page.. This resource allows retrieval of a list of options for accessing biological information, table/map displays, and sequence analysis tools for.. a named gene or sequence,.. a specified chromosomal region, or.. a raw DNA or protein sequence.. Enter a  ...   - YSCHELI.. GenBank AccNo.. - L00683.. GenBank GI - gi:171655.. Clone - 70353.. If available.. , add flanking basepairs.. upstream.. and downstream.. Use the reverse complement.. Pick a chromosome:.. Number.. I.. II.. III.. IV.. V.. VI.. VII.. VIII.. IX.. X.. XI.. XII.. XIII.. XIV.. XV.. XVI.. Mito.. Then enter coordinates (optional):.. to.. The entire chromosome sequence will be displayed if no coordinates are entered.. Note:.. Enter coordinates in ascending order for the Watson strand and descending order for the Crick strand.. Type or Paste a.. DNA.. Protein.. Sequence:.. The sequence.. MUST.. be provided in.. RAW format.. , no comments (numbers are okay)..

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  • Title: Saccharomyces cerevisiae Genome Snapshot/Overview
    Descriptive info: Genome Snapshot/Overview.. This page provides information on the status of the.. genome.. The Genome Inventory table is updated in real time.. The bar graphs for GO data are updated once a day.. All the data displayed on this page are available in one or more files (SGD_features.. tab, gene_associations.. sgd, go_slim_mapping.. tab) at.. SGD's FTP site.. tool can also be used to retrieve chromosomal features that match specific criteria.. Contents.. Graphical View of Protein Coding Genes.. Genome Inventory.. Summary of GO annotations.. Distribution of Gene Products by Process, Function, and Component.. Graphical View of Protein Coding Genes (as of Feb 10, 2013).. 4991 ORFs, 75.. 54%.. 829 ORFs, 12.. 55%.. 787 ORFs, 11.. 91%.. Genome Inventory (as of Feb 10, 2013).. This table reports the number and types of features annotated in the SGD, per chromosome.. To get a list of all features of a certain type (e.. g.. Verified ORF, tRNA, etc.. ), select that feature type.. To access more information on the individual chromosome (e.. sequence, a listing of all features on that chromosome, etc.. ), select the Roman numeral for that chromosome.. Feature Type.. Total.. Chromosome number.. Nuclear genome.. Mitochondrial genome.. (Q).. 2-micron plasmid (R).. Total ORFs.. 6607.. 117.. 456.. 183.. 836.. 324.. 141.. 583.. 321.. 241.. 398.. 348.. 578.. 505.. 435.. 598.. 511.. 6575.. 28.. Verified ORFs.. 4991.. 75.. 346.. 132.. 658.. 235.. 99.. 450.. 232.. 172.. 304.. 280.. 414.. 386.. 330.. 462.. 395.. 4970.. 17.. Uncharacterized ORFs.. 829.. 56.. 27.. 80.. 40.. 26.. 46.. 38.. 49.. 33.. 86.. 67.. 64.. 61.. 827.. Dubious ORFs.. 787.. 54.. 24.. 98.. 16.. 63.. 43.. 31.. 45.. 35.. 78.. 52.. 41.. 69.. 55.. 778.. Long_terminal_repeat.. 383.. 22.. 20.. 36.. 44.. 32.. ARS.. 337.. 18.. 19.. 21.. tRNA.. 299.. 275.. Transposable_element_genes.. 89.. snoRNA.. 77.. Retrotransposon.. X_element_core_sequence.. Telomere.. Telomeric_repeat.. X_element_combinatorial_repeats..  ...   does not include annotations to the three "root" terms representing lack of knowledge at this time, i.. "molecular function", "biological process", or "cellular component".. Ontology.. Details of Annotations.. Total Number of Annotations.. Number of Gene Products Annotated to root terms.. Graphical View.. Molecular Function.. 4353.. 1967.. Go to Molecular Function Graph.. Biological Process.. 5140.. 1180.. Go to Biological Process Graph.. Cellular Component.. 5593.. 727.. Go to Cellular Component Graph.. All Ontologies.. 15086.. 3874..  .. Distribution of Gene Products by Process, Function, and Component (as of Feb 10, 2013).. These graphical views representing the GO annotation state of the entire genome are provided using a GO Slim (a high-level subset of Gene Ontology terms that allows grouping of genes into broad categories such as "DNA replication", "protein kinase activity", or "nucleus") tailored to yeast biology.. GO Slim terms representing broad categories from a single aspect are listed for each graph, along with the percentage of.. gene products annotated to a specific term that maps up the ontology to the GO Slim term.. Note that some gene products may be represented more than once, if they are annotated to one or more GO terms that map to more than one GO Slim term.. Only the distribution of "known" Molecular Functions, Biological Processes, and Cellular Components are included in these graphs; annotations to the "root" terms "molecular function," "biological process," or "cellular component," which represent lack of knowledge at this time, are excluded from these graphs.. More information on GO and GO Slim can be found in SGD's.. GO help page.. To obtain the GO data summarized in these graphs, please use the.. or the go_slim_mapping.. tab file from.. Distribution of Gene Products among Molecular Function Categories.. Distribution of Gene Products among Biological Process Categories.. Distribution of Gene Products among Cellular Component Categories..

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