| Title: | Yeast Experimental Data |
|---|---|
| Description: | A collection of different sets of experimental data from yeast. |
| Authors: | R. Gentleman |
| Maintainer: | R. Gentleman <[email protected]> |
| License: | GPL |
| Version: | 0.58.0 |
| Built: | 2026-10-10 11:00:40 UTC |
| Source: | https://github.com/bioc/yeastExpData |
The data are 2885 yeast genes, common to a number of different experiments. The original data were reported by Cho et al. and were processed to
data(ccyclered)data(ccyclered)
A data frame with 2885 observations on the following 11 variables.
The cluster number the gene was assigned to.
The distance from the cluster center?
The name of the gene, using standard yeast nomenclature.
The Stanford yeast genome database identifier for the gene.
The common name for the gene.
The chromosome the gene is located on.
The start of the gene, in bases, most likely from the 3' end.
The end of the gene.
The number of introns.
The location of the exons.
A description of the gene.
Cho, et al. discuss the k means clustering of 2885 Saccharomyces genes into 30 clusters with measurements taken over two synchronized cell cycles.
http://arep.med.harvard.edu/network_discovery
Cho, et al. (1998) A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell, 2, 65-73.
Tavazoie, et al. (1999) Systematic determination of genetic network architecture. Nature Genetics, 22, 281-285.
data(ccyclered)data(ccyclered)
Abundance of yeast proteins measured using flow cytometry and GFP tagging
data(fcabundance)data(fcabundance)
A data frame with 4159 observations on the following 6 variables.
yORFa factor denoting yeast Open Reading Frames with
levels YAL001C, YAL002W, YAL005C, etc
gene_namea factor denoting corresponding gene names,
with levels 37164, AAC1, AAC3, etc
YEPD.meana numeric vector, giving average abundance in rich (YEPD) media
YEPD.errora numeric vector, giving corresponding (standard?) error
SD.meana numeric vector, giving average abundance in minimal (SD) media
SD.errora numeric vector, giving corresponding error
http://www.nature.com/nature/journal/v441/n7095/extref/nature04785-s03.xls
"Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise" (15 June 2006). John R. S. Newman, Sina Ghaemmaghami, Jan Ihmels, David K. Breslow, Matthew Noble, Joseph L. DeRisi and Jonathan S. Weissman Nature 441, 840-846
data(fcabundance) plot(YEPD.mean ~ SD.mean, fcabundance, log = "xy") plot(SD.error ~ SD.mean, fcabundance, log = "x")data(fcabundance) plot(YEPD.mean ~ SD.mean, fcabundance, log = "xy") plot(SD.error ~ SD.mean, fcabundance, log = "x")
This data frame contains data concerning the localization and abundance of various yeast proteins.
data(gfp)data(gfp)
A data frame with 6234 observations on the following 33 variables.
orfida numeric vector of identifiers
yORFa factor representing yeast ORF names, with
levels YAL001C, YAL002W, etc. These are also the
row names of the data frame.
gene_namea factor representing corresponding yeast
gene names, with levels AAC1, AAC3, etc.
GFP_taggeda factor with levels not tagged and
tagged, indicating whether or not the ORF was GFP tagged
GFP_visualizeda factor with levels not
visualized and visualized, indicating whether or not GFP
fluoresence was visualized
TAP_visualizeda factor with levels TAP
visualized and not TAP visualized, indicating success of
TAP tag
abundancea numeric vector, giving estimated abundance in units of molecules per cell
errora numeric vector of estimated errors in
abundance for a subset of proteins, in the same units as
abundance (see details below)
localization_summarya factor with levels ,
ER, ER to Golgi, ER,ambiguous,
ER,ambiguous,bud, etc. Summarizes the information
contained in the subsequent columns.
The following columns indicate whether or not the protein was localized in the specific region of the cell. A protein can be localized in more than one region.
ambiguousa logical vector
mitochondriona logical vector
vacuolea logical vector
spindle_polea logical vector
cell_peripherya logical vector
punctate_compositea logical vector
vacuolar_membranea logical vector
ERa logical vector
nuclear_peripherya logical vector
endosomea logical vector
bud_necka logical vector
microtubulea logical vector
Golgia logical vector
late_Golgia logical vector
peroxisomea logical vector
actina logical vector
nucleolusa logical vector
cytoplasma logical vector
ER_to_Golgia logical vector
early_Golgia logical vector
lipid_particlea logical vector
nucleusa logical vector
buda logical vector
Explanation for missing abundance values are given by
missingAbundancea factor with levels
low signal, not visualized and
technical problem
The information on abundance is available in three columns.
abundance gives (where available) absolute protein abundances
determined by quantitative Western blot analysis of TAP-tagged
strains. Abundances that have a non-NA error value were
done in triplicate with serial dilutions of purified TAP-tagged
standards included in each gel, which substantially reduces the
measurement error. In addition, for these strains, the tagged genes
were confirmed to rescue the loss of function phenotype of the
corresponding deletion strain. For rows where abundance is
missing (NA), the missingAbundance column gives the
reason. Possible reasons are:
"not visualized"Either the tagging was unsuccessful or no signal was detected.
"low signal"The tagging was successful, but the signal was not sufficiently high above background to permit accurate quantitation (about 50 molecules/cell).
"technical problem"The protein was detectable but could not be quantitated because it did not migrate as a single band or comigrated with the internal standards in the gel.
Replicate analysis for a subset of tagged strains found a linear correlation coefficient of R = 0.94, with the pairs of proteins having a median variation of a factor of 2.0. This error analysis does not account for potential alterations in the endogenous levels of the proteins caused by the the fused tag, which may be particularly disruptive for small proteins.
The data were obtained from http://yeastgfp.ucsf.edu/, which contains a lot more information as well as raw image data. This data frame was specifically generated from http://yeastgfp.ucsf.edu/allOrfData.txt
For the Localization data: Huh, et al., Nature 425, 686-691 (2003) – http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=14562095&dopt=Abstract
For the Protein abundance data: Ghaemmaghami, et al., Nature 425, 737-741 (2003) – http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=14562106&dopt=Abstract
data(gfp) keep <- names(which(table(gfp$localization_summary) > 50)) if (require(lattice)) { bwplot(reorder(localization_summary, abundance, median, na.rm = TRUE) ~ log2(abundance), gfp, varwidth = TRUE, subset = localization_summary %in% keep) } else { opar <- par(las = 2, mar = par("mar") + c(3.5, 0, 0, 0)) gfp._sub <- subset(gfp, localization_summary %in% keep) gfp._sub$localization_summary <- gfp._sub$localization_summary[, drop = TRUE] boxplot(log2(abundance) ~ reorder(localization_summary, abundance, median, na.rm = TRUE), data = gfp._sub, varwidth = TRUE) rm(gfp._sub) par(opar) }data(gfp) keep <- names(which(table(gfp$localization_summary) > 50)) if (require(lattice)) { bwplot(reorder(localization_summary, abundance, median, na.rm = TRUE) ~ log2(abundance), gfp, varwidth = TRUE, subset = localization_summary %in% keep) } else { opar <- par(las = 2, mar = par("mar") + c(3.5, 0, 0, 0)) gfp._sub <- subset(gfp, localization_summary %in% keep) gfp._sub$localization_summary <- gfp._sub$localization_summary[, drop = TRUE] boxplot(log2(abundance) ~ reorder(localization_summary, abundance, median, na.rm = TRUE), data = gfp._sub, varwidth = TRUE) rm(gfp._sub) par(opar) }
These two data objects represent protein/gene interactions as reported in Ge et al.
data(litG) data(y2hG)data(litG) data(y2hG)
The data are stored as instances of the graphNEL class. Each
has 2885 nodes, named using yeast standard names. Interactions either
represent literature curated interactions, or Y2H interactions.
The data were reported and used in Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae, Nature Genetics, 2001, H. Ge and Z. Liu and G. M. Church and M. Vidal.
See the package vignette for more details.
data(litG) data(y2hG)data(litG) data(y2hG)
In comparing the yeast cell cycle data to the protein-protein interaction data a node permutation distribution is suggested. The output here are the counts of the number of common edges for 500 permutations.
data(nPdist)data(nPdist)
Five hundred counts of nodes in the intersection of two graphs.
The seed was 123 and we called the function nodePerm
with litG and cg1 as arguments. See the vignette in
this package for the explicit computations.
data(nPdist)data(nPdist)
A data frame which details 33 properties of proteins in the Yeast Genome
data(proteinProperties)data(proteinProperties)
A data frame with 6718 observations on the following 33 variables.
yORF a factor representing yeast ORF names, with
levels Q0010, Q0017, etc.
SGDIDa factor representing SGD IDs
molwta numeric vector giving Molecular Weight in Daltons
pia numeric vector denoting the theoretical isoelectric point(pI), the pH at which the protein carries no net charge
caia numeric vector denoting Codon Adaptation Index
lengtha numeric vector denoting length of the protein (number of amino acids)
nterma factor representing N Term Sequence with
levels MAAACIC MAAAPWY, etc.
cterma factor representing N Term Sequence with
levels AAAAMLL AAADKKT, etc.
codonBiasa numeric vector denoting Codon Bias
The next set of columns, designated by amino acids, is the number of
times that particular residue appears in the protein sequence. For
example, if the ALA column is 2, then the protein contains 2 alanines.
These columns (should) add up to the length column.
ALAa numeric vector
ARGa numeric vector
ASNa numeric vector
ASPa numeric vector
CYSa numeric vector
GLNa numeric vector
GLUa numeric vector
GLYa numeric vector
HISa numeric vector
ILEa numeric vector
LEUa numeric vector
LYSa numeric vector
METa numeric vector
PHEa numeric vector
PROa numeric vector
SERa numeric vector
THRa numeric vector
TRPa numeric vector
TYRa numeric vector
VALa numeric vector
The remaining columns are:
fopFOP score, a numeric vector, denoting Frequency of Optimal Codons
gravyGravy score, a numeric vector denoting Hydropathicity of Protein
aromaticityAromaticity score, a numeric vector denoting Frequency of aromatic amino acids: Phe, Tyr, Trp
typeFeature type, a factor with levels
ORF|Dubious ORF|Uncharacterized ORF|Verified
ORF|Verified|silenced_gene pseudogene
transposable_element_gene
This data frame is downloaded directly from SGD. It contains 33 characteristics for 6714 open reading frames (ORFS). From the SGD README:
“Contains basic protein information about each ORF in SGD. This file does not include information on deleted or merged ORFs. Note, however, that it includes ORFs of all other classifications (Verified, Uncharacterized, and Dubious).”
For more details see http://www.yeastgenome.org/help/protein_page.html.
ftp://genome-ftp.stanford.edu/pub/yeast/protein_info/protein_properties.tab. This file is updated weekly (Saturday). The version used here was downloaded on 2009-12-03.
data(proteinProperties) pairs(proteinProperties[, c("molwt", "pi", "cai", "gravy", "aromaticity")], pch = ".", col = as.numeric(proteinProperties$type))data(proteinProperties) pairs(proteinProperties[, c("molwt", "pi", "cai", "gravy", "aromaticity")], pch = ".", col = as.numeric(proteinProperties$type))