New & Noteworthy
SGD Help Videos: Working with Lists in YeastMine
July 28, 2015
Understanding lists and knowing how to work with them is crucial to getting the most out of YeastMine. This set of short videos explains everything you need to know.
YeastMine allows you to save objects in lists. Typically, these objects are genes, but you can also make lists of other objects such as Gene Ontology terms or PubMed IDs. One way to create a list in YeastMine is to run a query and save the results in a list. Another way is to type in or upload your own list.
Whenever you create a list in YeastMine, you’re immediately presented with information about the genes in the list, such as Gene Ontology and pathway enrichment, interactions, orthologs, and more. This can help you decide what kind of further analysis you’d like to do.
And what if you create a list but then realize that you forgot to include a gene? No worries. It’s easy to edit your saved lists.
Once you have a list of genes, you can feed it into any template query whose name begins with “Gene” to get results for all of the genes in the list. This powerful feature lets you run successive queries to narrow down your results. For example, you could make a list of all the proteins in a given size range, then query that list to see which ones are located in the nucleus, and finally ask how many of these nuclear proteins have human homologs.
And finally, once you’ve created and saved lists you can do a lot of different things with them: combine them, find their intersection, find genes that are not shared between two lists, or find genes that are in one list but not another. This provides a powerful way to combine or compare results from different YeastMine queries.
As always, please contact us if you have any questions about YeastMine. We’re happy to help!
Saving Search Results as a List
Creating and Using Gene Lists
Adding Objects to a Saved List
Feeding Lists into Templates
List Operations
Categories: Tutorial
Two Tales of Two Tails
July 22, 2015
Tails let animals sense and interact with their environment at a distance from their bodies. It turns out that some proteins use their “tails” in a similar way. Except they take it one step further.
Some mythical creatures have tails that coil around each other. Septin proteins are not all that different! Image via Wikimedia Commons
In addition to sensing the environment, they can also use their tails as sort of fishing poles to catch proteins they need to interact with. And they do this with great specificity…it is like having that perfect lure that always catches one type of fish.
A great example of this is the septin family of proteins which includes many members that are “tailed” proteins. Septins are highly conserved proteins that typically have a globular GTP-binding domain adjacent to an elongated C-terminal extension.
Septins form structures that act as the boundaries between different cellular parts. In budding yeast cells, they form the septum that separates the mother and bud, and recruit the cytokinesis machinery that allows the daughter cell to separate from the mother cell. In larger animals, they can be found in places such as the dendritic spines of neurons, sperm flagella, or cilia. And in humans, septin mutations have been linked to cancer and neurological diseases.
Until now, the details of how septins recruit other proteins to boundary sites have been elusive. But in two new papers in GENETICS, Finnigan and coworkers in the Thorner lab at Berkeley dove into this question and gained real insight into the lures these proteins use.
In their first paper they reported an extremely comprehensive genetic analysis to dissect the functions of two of the least characterized septins, Shs1 and Cdc11. In the second paper they used both genetic and physical methods to show how these septins recruit myosin to the septum to form the contractile ring that pinches off the bud from the mother.
The bottom line: their C-terminal tails are extremely important. They intertwine with other proteins’ tails like love-struck seahorses. And their specificity comes from these same tails—certain tails only coil around other tails.
The S. cerevisiae genome encodes a family of septins that assemble with each other to form octameric rods that consist of four different septins. The rods have both end-to-end and side-to-side interactions with each other, forming a ladder-like superstructure.
The septins Cdc11 and Shs1 are the most closely related members of the septin family, and the most recently evolved. They cap the ends of the septin rods. In otherwise wild-type cells, Cdc11 is essential for life while Shs1 is not.
Because SHS1 can be deleted without causing a major phenotype, the first step in investigating its function was to find genetic conditions under which its function becomes more obvious. The authors created four different genetic backgrounds in which the function of other septins was compromised by different mutations. Cells that had mutations in both SHS1 and in other septin genes had obvious problems, such as elongated buds or the inability to grow at high temperatures.
Now Finnigan and colleagues were set to do a detailed genetic analysis to figure out what different parts of Shs1 do by testing mutant versions in these different backgrounds. We can’t possibly recapitulate all the results here, but we’ll do our best to cover the highlights.
Almost all septins, whether in yeast or mammals, end with a tail: a long stretch called the C-terminal extension (CTE) that contains sequence patterns characteristic of a coiled-coil structure. The researchers found that the coiled coil regions of Shs1 and Cdc11were essential to their functions. (And no, they didn’t create any mutations by writing their names in the coiled coil sequence!)
Finnigan and colleagues tried swapping CTEs between different septins. When Cdc11 carried the Shs1 CTE and vice versa, the cells grew just fine. However, this swappability didn’t extend to other septins that are positioned internally in the septin rods. The CTEs of the end subunits Cdc11 and Shs1 could be exchanged for each other, but these CTEs only worked when they were on the ends of the rods.
Since coiled coils are often involved in interactions between proteins, Finnigan and colleagues wondered whether the essential function of the Cdc11 and Shs1 CTEs might be to recruit other proteins to the bud neck.
To test this, they searched published data to identify proteins that are well-known to be localized to the bud neck at the time in the cell cycle when septins are present. They found 30 such proteins, and overexpressed GFP-tagged versions of each in a strain where both Cdc11 and Shs1 lacked their CTEs.
Of the 30 proteins, only overexpressed Bni5 suppressed the growth defect of this strain. To test directly whether binding to Bni5 is a critical function of Shs1, Finnigan and colleagues fused the two genes to each other, so that Bni5 replaced the CTE of Shs1. This fusion protein could compensate for the lack of both Cdc11 and Shs1 CTEs.
To confirm that the important function of the CTEs is to hold Bni5 in the right place, they came up with an alternative test using a “nanobody”, which is a very small, very high-affinity single-chain antibody. They replaced the CTE of either Cdc11 or Shs1 with a nanobody that recognized GFP, and expressed a GFP-Bni5 fusion in these strains. In both cases, tethering Bni5 to the septin via the nanobody obviated the need for the CTE.
Finally, the authors asked why it is important for Bni5 to be located on the septin rods. Previous work had suggested that Bni5 recruits Myo1 (myosin), an important component of the contractile ring at the bud neck. They used the same nanobody constructs to test this, simply expressing GFP-Myo1 in the strains where the nanobody replaced the CTEs of Cdc11 or Shs1. Sure enough, tethering Myo1 to the terminal septins eliminated the need for Bni5.
So we now know that tails are absolutely essential for the functions of the alternative terminal septins Shs1 and Cdc11. These fishing poles let them hold on to the Bni5 “bait,” which in turn catches Myo1 to provide the muscle for cytokinesis to occur. Since septins are so highly conserved, it’s probable that these results will be directly applicable to higher organisms: there are mammalian septins that also occupy the end positions of septin rods, analogous to Cdc11 and Shs1. And that’s no fish story!
Not only septins use their tails to fish.
by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD
Categories: Research Spotlight
Tags: cytokinesis, Saccharomyces cerevisiae, septins
SGD Help Video: Gene Name Reservation
July 13, 2015
The eminent Drosophila geneticist Michael Ashburner famously said: “Biologists would rather share their toothbrush than share a gene name.” It’s true that assigning names to genes is often a sticky subject.
In the Saccharomyces cerevisiae community we’re very lucky to have well-defined guidelines for genetic nomenclature, an established system for reserving gene names, and criteria for making them “standard,” or official, names. This system was agreed upon by yeast researchers nearly two decades ago and has served the community well.
Take a look at this video to get an overview of how the gene naming system works. And as always, please contact us with any questions or suggestions.
Categories: Tutorial
Tags: gene nomenclature, Saccharomyces cerevisiae, video
The Gift for the Man Who Has Everything
July 08, 2015
Gifts can be hard to buy for some people. They have everything they need and not many outside interests. What to do?
Having trouble finding that personal gift for that impossible to buy for person? How about a vanity protein with their name written right into the amino acid sequence? Image by D. Barry Starr
You could name a star after them or get them some knick knack they don’t need. Or you could design a personalized protein that has their name in it, solve the structure and present them with the picture.
This is what Deiss and coworkers did to celebrate the 50th birthday of their colleague Andrei N. Lupas, a key figure in studying coiled-coil proteins. They created a personalized protein based on Gcn4 from Saccharomyces cerevisiae. And of course Gcn4 is a coiled-coil protein!
Coiled-coil proteins are the perfect clay for biosculpting a personalized protein. They follow a relatively simple set of rules which makes it easy to predict how they will fold. There isn’t much of the “protein folding problem” with these user-friendly proteins.
Basically these proteins consist of repeated 7 amino acid motifs that each form an alpha helix. They have hydrophobic residues down one face of the helix so that they will tend to oligomerize with each other to keep the hydrophobic residues away from the water. These helices spontaneously coil up like a rope (hence their name).
The 7 amino acids of a repeat are usually represented as a–b–c–d–e–f–g and are arranged in the pattern hxxhcxc, with h being hydrophobic residues, c being charged residues and x being most any other amino acid. So a and d must be hydrophobic, and e and g charged. That’s pretty much it!
Deiss and coworkers used the name Andrei N. Lupas to create a personalized coiled coil. They replaced 12 amino acids in Gcn4 with the amino acids represented by the letters in his name. Well, they were able to do that for most of the letters.
First off, they had to Roman things up a bit and turn the U into a V (there is no amino acid with the single amino acid code U). So here is the amino acid sequence they used and how they lined it up with the 7-amino acid repeats:
In this arrangement, the hydrophobic residues are asparagine, isoleucine, and valine, and the charged residues are aspartic acid, glutamic acid, proline, and serine. Obviously the last two are not optimal, especially the proline. Proline has an especially rigid conformation and is known to wreak havoc with alpha helices.
When the authors analyzed the protein, they found that as predicted, the proline disrupted the part of the alpha helix with which it was associated. But not enough to completely destroy the coiled coil structure. X-ray diffraction showed that this protein was still able to trimerize properly. They had created a distorted but functional personalized protein. What other kind would anyone want!
And it isn’t as if proline is completely absent from the heptad repeats of coiled-coil proteins. A quick search by the authors found two viral fusion proteins, 1ZTM and 3RRT, that could form a trimer even though they too had prolines. In both of these proteins the proline is in the f position.
They also found 4 dimers with a proline in a heptad repeat. In these cases the proline is at b or c. So no known natural coiled-coil proteins have a proline at the e position. Talk about personalized!
How cool is all of this, and who wouldn’t want a protein of their very own? Unfortunately, not everyone can easily have one.
For example, President Barack Obama would have real trouble since there are no amino acids designated with a B or an O and there is no obvious way to transform these letters into ones that are present in the single letter code. Jeb Bush is out too, but maybe we can do something with Hillary Clinton. Let’s see if we can line up the amino acids of her first name to create a personalized Gcn4 just for her.
“HILLARY” isn’t too bad by itself. All the letters are amino acids (yay) and a and d are hydrophobic (isoleucine and alanine). Aspartic acid works very well for e and while probably not perfect, histidine isn’t too bad for g. The tyrosine at position f is not ideal either but is way better than a proline. This thing might replace one heptad repeat in Gcn4 without causing too many problems.
So what about your name? Can you turn yours into a heptad repeat to create your own personalized Gcn4?
by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics
Categories: Research Spotlight
Tags: coiled coil, Saccharomyces cerevisiae
Network Maintenance at SGD on July 15, 2015
July 07, 2015
The SGD website (www.yeastgenome.org) and all its resources (Download Server, GBrowse, SPELL, YeastMine, Pathway Tools, and Textpresso) will be unavailable on Wednesday, July 15, 2015 from 2:30-4:30 pm PDT (5:30-7:30pm EDT, 9:30-11:30pm GMT, 6:30-8:30am Japan) for network maintenance. We will make every effort to minimize any downtime associated with this maintenance. We apologize for any inconvenience this may cause, and thank you for your patience and understanding.
Categories: Maintenance
SGD Help Video: YeastMine is Awesome!
July 07, 2015
If you’re not already using YeastMine to answer all your questions about the Saccharomyces cerevisiae genome and the gene products it encodes…you should be!
This versatile tool lets you slice and dice data from SGD in any way you choose. You can ask questions like “How many proteins between 25 and 35 kDa in size are integral to the nuclear membrane?” or “Which genes can mutate to confer oxidative stress resistance, and what biological processes are they involved in?”
Start with this video to see a quick sample of three cool features in YeastMine.
Categories: Tutorial
Where’s That Protein?
July 01, 2015
Waldo will always be hard to find, but we now know exactly where to find more than 4,000 S. cerevisiae proteins, thanks to new methods and an analysis pipeline. Image by William Murphy via Wikimedia Commons
You might be familiar with the Where’s Waldo book series, especially (but not necessarily) if you have kids. They challenge the reader to find Waldo within huge, intricately drawn groups of people. Even though Waldo has his distinctive characteristics—glasses and a striped shirt and hat—he can be very hard to find.
Now imagine that the drawings shift under different conditions, so that Waldo could be in any of several places at different times. And imagine that you’re not just looking for Waldo, but also for thousands of other unique individuals—all tagged in the same way. This is the challenge faced by researchers who want to know where each protein in a cell is located and how its location and abundance respond to different environments.
But, as genetic, robotic, microscopic, and computational tools get more and more sophisticated, it’s becoming possible to pinpoint Waldo and his companions even as they move around within the jam-packed yeast cell.
In two new papers, scientists from the University of Toronto describe a huge effort that entailed over 9 billion quantitative measurements to find the location and measure the abundance of more than 4,000 S. cerevisiae proteins. Chong and colleagues wrote in Cell about the approach and experimental methods, while Koh and colleagues published in G3 about the computational methods and the database that houses all the data, called CYCLoPs for Collection of Yeast Cells and Localization Patterns.
This work couldn’t have been done without a valuable resource that was created some years ago: the yeast GFP collection. It’s a set of strains, each with the green fluorescent protein gene fused to the 3’ end of one open reading frame to express a GFP fusion protein from the ORF’s native promoter. Not every yeast protein can be detected this way: some are expressed too weakly, while others may actually be destabilized by their GFP tags. Still, more than 4,100 of these fusion genes—71% of the proteome—give a visible GFP signal in the cell.
The researchers started with these ~4,100 strains and transformed each with a plasmid expressing red fluorescent protein. This allowed them to visualize the boundaries of each cell. Then they got to work, taking pictures of at least 200 cells of each strain and developing an automated pipeline to analyze them. They ended up analyzing 300,000 micrographs of more than 20 million cells, beating the few dozen Where’s Waldo books by a long shot!
The scientists looked at each protein in wild type, in a mutant strain, and in the presence of two drugs. The mutant strain they studied was deleted for RPD3, which encodes a lysine deacetylase that regulates the stability and interactions of histones and other proteins. The drug treatments were done with several different concentrations of rapamycin (an inhibitor of the TORC1 complex, which is an important regulator of cell growth) or hydroxyurea (a DNA replication inhibitor).
The end result was an enormous collection of data, now stored in the CYCLoPs database, that shows the abundance of each protein in each of 16 cellular compartments under all of these different conditions. These data are much more quantitative and consistent than any protein abundance or localization data that had been obtained before. They are stored in such a way that measurements within single cells can be accessed, and the database can be searched by patterns of changes in localization or abundance as well as for data on a particular protein.
The authors came up with some innovative methods for visualizing this immense dataset to get a high-level overview. One of their most surprising findings was just how many proteins localize to multiple places. We tend to think of the cell as a tidy place where each protein has one particular location, but Chong and colleagues found that it’s extremely common for proteins to be in several spots.
Most often, when proteins are present in more than one place, those places are the nucleus and the cytoplasm. Some proteins had already been shown in small-scale studies to be present in both compartments, or to shuttle between them. But the authors saw an astounding 1,029 proteins localizing to both the nucleus and cytoplasm under standard conditions in wild-type cells.
Not counting the proteins in the nucleus and cytoplasm, another 511 proteins localized to more than one place. Some were seen in up to five different subcellular compartments.
The proteins with multiple locations, as a group, were more likely than the average protein to be phosphorylated. This made sense, because phosphorylation of proteins is known to regulate their localization. And many of these proteins themselves had regulatory roles, controlling processes such as cell division.
The fact that data were collected from single cells means that we can use them to uncover the dynamics of protein movement. For example, if a protein was scored as localizing to both the nucleus and the cytoplasm, does that mean there’s a pool of it in both places at all times, or does it move back and forth? The single-cell data for two representative proteins, Mcm2 and Whi5, showed clearly that any one cell has each of these proteins in either the nucleus or cytoplasm, but not both. But some other proteins hang out in both places at once. And the dynamics of still more roving proteins are just waiting to be revealed.
Researchers will be mining the CYCLoPs resource to find detailed information about specific proteins, pathways, and processes for years to come. The data gathered in the rpd3 mutant and under rapamycin and hydroxyurea treatment served as proof of principle that the system can be used to assess the effects of a variety of mutations and drugs.
So this study puts a spotlight on Waldo in each picture and makes it simple to find him and his friends. This mass of data on where proteins are and how they move around has far-reaching implications for yeast systems biology, and the methodology can now be applied to cells of other organisms as well. In the coming weeks, we’ll make it even simpler for you to access these data from SGD, by adding links for individual proteins to the CYCLoPs database.
by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD
Categories: Research Spotlight
Tags: protein abundance, protein localization, Saccharomyces cerevisiae
New SGD Help Video: Yeast-Human Functional Complementation Data
June 30, 2015
Yeast and humans diverged about a billion years ago, but there’s still enough functional conservation between some pairs of yeast and human genes that they can be substituted for each other. How cool is that?! Which genes are they? What do they do?
This two-minute video explains how to find, search, and download the yeast-human functional complementation data in SGD. You can find help with many other aspects of SGD in the tutorial videos on our YouTube channel. And as always, please be sure to contact us with any questions or suggestions.
Categories: Homologs, New Data, Tutorial
Tags: video, yeast model for human disease
The Sounds of Silencing
June 17, 2015
For centuries, we thought of the universe as an empty, eerily silent place. Turns out we were dead on when it came to the emptiness, not so much when it came to the silence.
Despite more and more powerful equipment, SETI has yet to find any meaningful radio signals coming from the stars. Yeast research is in a better position: new techniques applied to telomeric gene expression now make sense of the signals. Image by European Southern University (ESO) via Wikimedia Commons
Once we invented devices that could detect electromagnetic radiation—starting with the Tesla coil receiver in the 1890s—we began to realize what a noisy place the universe really is. And now with modern radio telescopes becoming more and more sensitive, we know there is a cacophony of signals out there (although the Search for Extraterrestrial Intelligence has yet to find any non-random patterns).
The ends of chromosomes, telomeres, have also long thought to be largely silent in terms of gene expression. But a new paper in GENETICS by Ellahi and colleagues challenges that idea.
Much like surveying the universe with a high-powered radio telescope, the researchers used modern techniques to make a comprehensive survey of the telomeric landscape–and saw that the genes were not so silent. Their work revealed that there’s a lot more gene expression going on at telomeres than we thought before.
It also gave us some fascinating insights into the role of the Sir proteins, founding members of the conserved sirtuin family that is implicated in aging and cancer.
Telomeres are special structures that “cap” the ends of linear chromosomes to protect the genes near the ends from being lost during DNA replication, something like aglets, those plastic tips that keep the ends of your shoelaces from fraying. They have characteristic DNA sequence elements that we don’t have space to describe here (but you can find a short summary in SGD).
Classical genetics experiments in Drosophila fruit flies showed that telomeres had a silencing effect on the genes near them, and early work in yeast seemed to confirm this. Reporter genes became transcriptionally silenced when they were placed near artificial constructs that mimicked telomere sequences.
This early work was solid, but had a few limitations. The artificial telomere constructs were, well, artificial; some of the reporter genes encoded enzymes that had an effect on overall cellular metabolism, such as Ura3; and the studies tended to look at just one or a few telomeres.
To get the whole story, Ellahi and colleagues decided to look very carefully at the telomeric universe of S. cerevisiae. First, they used ChIP-seq to look at the physical locations of three proteins, Sir2, Sir3, and Sir4, on chromosomes near the telomeres.
These proteins, first characterized and named Silent Information Regulators for their role in silencing yeast’s mating type cassettes, had been seen to also mediate telomeric silencing. Scientists had hypothesized that they might be present at telomeres in a gradient, strongly repressing genes close to the chromosomal ends and petering out with increasing distance from the telomere.
Ellahi and coworkers re-analyzed recent ChIP-seq data from their group to find where the Sir proteins were binding within the first and last 20 kb regions of every chromosome. These 20 kb regions included the telomere and the so-called subtelomeric region where genes are thought to be silenced. They found all three Sir proteins at all 32 natural telomeres.
However, the Sir proteins were not uniformly distributed across the telomeres, but rather occupied distinct positions. Typically, all three were in the same position, as would be expected since they form a complex. And they were definitely not in a gradient along the telomere.
Next the researchers asked whether gene expression was truly silenced in that subtelomeric region. They used mRNA-seq to measure gene expression from the ends of chromosomes in wild type or sir2, sir3, or sir4 null mutants.
They found that contrary to expectations, there is actually a lot of transcription going on near telomeres, even in the closest 5 kb region. The levels are lower than in other parts of the genome, but that can be partly explained by the fact that open reading frames are less dense in these regions. And only 6% of genes are silenced in a Sir-dependent manner.
The sensitivity of mRNA-seq allowed Ellahi and colleagues to uncover new patterns of gene expression in this work. They were able to detect very low-level transcription from some of the telomeric repetitive elements. Also, because the SIR genes are involved in mating type regulation, the mRNA-seq data from the sir mutants revealed a whole new set of genes that are differentially expressed in different cell types (haploids of mating types a and α, or a/α diploids).
The researchers point out that their work raises the question of why the cell would use the Sir proteins to repress transcription of a few subtelomeric genes. Wouldn’t it be more straightforward if these genes just had weaker promoters to keep their expression low?
They hypothesize that Sir repression could actually be part of a stress response mechanism, allowing a few important genes to be turned on strongly when needed. This idea could have intriguing implications for the role of Sir family proteins in aging and cancer in larger organisms.
So, neither the universe nor the ends of our chromosomes are as silent as we thought. But unlike the disappointed SETI researchers, biologists studying everything from yeast to humans can now build on this large quantity of meaningful data from S. cerevisiae telomeres.
by Maria Costanzo, Ph.D., Senior Biocuration Scientist, SGD
Categories: Research Spotlight
Tags: Saccharomyces cerevisiae, silencing, telomere
Yeast-Human Functional Complementation Data Now in SGD
June 10, 2015
Yeast and humans diverged about a billion years ago. So if there’s still enough functional conservation between a pair of similar yeast and human genes that they can be substituted for each other, we know they must be critically important for life. An added bonus is that if a human protein works in yeast, all of the awesome power of yeast genetics and molecular biology can be used to study it.
To make it easier for researchers to identify these “swappable” yeast and human genes, we’ve started collecting functional complementation data in SGD. The data are all curated from the published literature, via two sources. One set of papers was curated at SGD, including the recent systematic study of functional complementation by Kachroo and colleagues. Another set was curated by Princeton Protein Orthology Database (P-POD) staff and is incorporated into SGD with their generous permission.
As a starting point, we’ve collected a relatively simple set of data: the yeast and human genes involved in a functional complementation relationship, with their respective identifiers; the direction of complementation (human gene complements yeast mutation, or vice versa); the source of curation (SGD or P-POD); the PubMed ID of the reference; and an optional free-text note adding more details. In the future we’ll incorporate more information, such as the disease involvement of the human protein and the sequence differences found in disease-associated alleles that fail to complement the yeast mutation.
You can access these data in two ways: using two new templates in YeastMine, our data warehouse; or via our Download page. Please take a look, let us know what you think, and point us to any published data that’s missing. We always appreciate your feedback!
Using YeastMine to Access Functional Complementation Data
YeastMine is a versatile tool that lets you customize searches and create and manipulate lists of search results. To help you get started with YeastMine we’ve created a series of short video tutorials explaining its features.
Gene –> Functional Complementation template
This template lets you query with a yeast gene or list of genes (either your own custom list, or a pre-made gene list) and retrieve the human gene(s) involved in cross-species complementation along with all of the data listed above.
Human Gene –> Functional Complementation template
This template takes either human gene names (HGNC-approved symbols) or Entrez Gene IDs for human genes and returns the yeast gene(s) involved in cross-species complementation, along with the data listed above. You can run the query using a single human gene as input, or create a custom list of human genes in YeastMine for the query. We’ve created two new pre-made lists of human genes that can also be used with this template. The list “Human genes complementing or complemented by yeast genes” includes only human genes that are currently included in the functional complementation data, while the list “Human genes with yeast homologs” includes all human genes that have a yeast homolog as predicted by any of several methods.
Downloading Functional Complementation Data
If you’d prefer to have all the data in one file, simply visit our Curated Data download page and download the file “functional_complementation.tab”.
Categories: New Data, Yeast and Human Disease