Each increase by one doubles the required database size 2 bit-score. Bit-score does not depend on database size. The bit-score gives the same value for hits in databases of different sizes and hence can be used for searching in an constantly increasing database. Search this site. Report abuse. Page details. Page updated. Google Sites. This site uses cookies from Google to deliver its services and to analyze traffic. DNA Res. Drummond, D. Why highly expressed proteins evolve slowly. Das, S. Variation of gene expression in plants is influenced by gene architecture and structural properties of promoters.
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The remaining authors declare no competing interests. Proteins correlate more strongly between tissues than transcripts. Turquoise squares mark examples for morphologically highly similar tissues. Tissues are coloured as in Fig. Evidence level: 1 protein evidence; 2 transcript evidence; 3 homology; 4 predicted; and 5 uncertain.
Proteins also identified in a previous study 7 are projected into the same plot. Right, distribution of proteins for which phosphorylated S, T or Y residues were identified. Right, Venn diagram comparing P-site localization confidence between class I sites identified in this study and the low and high confidence datasets reported in a previous publication 8. Source data. X denotes the amino acid after the start codon. Because trypsin was used for protein digestion, the frequencies for Arg and Lys residues could not be determined n.
The top and bottom quartile ranges are shown as whiskers. The number of proteins is indicated for each tissue. Right, number of multiple isoforms of the same gene distinguished at the peptide level. The normalized spectral contrast angle SA was used as a similarity metric Methods.
These data are reported in Supplementary Data 3. The spectra pointing upwards were collected from tissue digests; those pointing downwards were collected from synthetic peptides.
OM, orders of magnitude. Note that for lower abundance transcripts, fewer proteins were detected. Protein abundance spans six orders of magnitude, whereas transcript abundance only spans four i. In addition, note that phosphorylation was detected across the entire protein abundance range. Numbers below the x axis denote the number of genes for these protein classes in the A. See Methods for the definition of these categories.
In brief, there are very few transcripts and proteins that are only expressed in a single tissue. The quantities of the shared transcripts or proteins can differ vastly between tissues b. Right, clustering of z -scored protein intensities showing distinct quantitative expression differences between flower organs.
Tissues are coloured according to tissue groups as in Fig. The five most abundant transcripts and proteins are listed in descending order for each tissue. These are generally not the same. In addition, note that the characteristics of the plots are not the same for all tissues. In flower, the protein line rises more quickly than the transcript line. The opposite is true for pollen and a more even characteristic is observed in seed. Relatively few proteins and transcripts are found together on the list of the most abundant transcripts and proteins.
This demonstrates that the quantitative differences in transcript and protein expression are more important in defining a tissue than the qualitative expression of transcripts or proteins. This shows that strong qualitative and quantitative expression differences exist between tissues. The PCA separates tissues into photosynthetically active versus inactive tissues component 1 and separates pollen from all other tissues component 2 , indicating that the molecular composition of pollen is particularly different from all other tissues.
The comparison of photosynthetically active and inactive tissues shows that most of the protein content in photosynthetically active tissues is contained in the plastids, whereas most protein is found in the cytosol for photosynthetically inactive tissues. Proteins with only one single subcellular compartment annotation were selected for the plot and the proportion of their iBAQ intensities were averaged for each tissue group.
Predicted protein abundance levels were obtained from the best fitting feature selection model for each tissue Methods. The number of genes used for the correlation analysis is indicated for each tissue.
Violin shapes show the kernel density estimation of the data distribution and the median as white dot. Thick black bars denote the interquartile range. Whiskers denote 1. Outliers were omitted from the plot for clarity. Outliers were omitted from the plot for clarity but were included in the statistical tests below. Germination was completely inhibited by CHX and partially inhibited by MG, showing that the drug treatments were effective.
Bar plot shows the MAD range segmented into five quantiles, each containing the same number of genes coloured bars and dashed lines. Most genes have reasonably stable PTRs across tissues. There is also more variation in the protein levels across tissues for low abundant proteins.
This may in part be due to technical limitations as low abundance proteins can generally be less accurately quantified. Again, this may in part be due to technical limitations as P-site quantification is performed on a peptide level and does not benefit from aggregating multiple peptide quantifications into one value for protein quantification.
Colours denote the log 10 -normalized STRING scores of individual gene pairs as a measure of known or predicted direct physical or indirect functional associations. Strong co-expression of transcripts or proteins or both are more strongly related physically or functionally than transcripts and proteins that are not. Randomly selected gene pairs are shown as a control. Medians are given and displayed as dotted lines. Each of these cluster is intended to represent a taxonomic unit of a bacteria species or genus depending on the sequence similarity threshold.
Columns usually represent samples and rows represent genera or species specific taxonomic units OTUs. OTU resolution depends on the 16S approach which has some limits in distinguishing at the species level, for example,. Escherichia coli and Shigella spp. Alternative approaches are developed to achieve higher resolution up to strain level by considering larger or complete sets of genes.
Multilocus sequence typing MLST , housekeeping genes sub-species resolution.
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