The original "CropPAL1" website is now here.
Where do plant proteins go?
Proteins in crop plants have specific functions and locations within the plant cell. They generate or are themselves products important for human use. In order to improve crops, protein function and location must be known. Protein subcellular location is an important clue to function and also to how proteins interact within the metabolic household. Subcellular location can be determined by fluorescent protein tagging or mass spectrometry detection in subcellular purifications as well as by prediction using protein sequence features.
The compendium of crop Proteins with Annotated Locations (cropPAL) collates more than 648 data sets from previously published fluorescent tagging or mass spectrometry studies and eight pre-computed subcellular predictions for 12 different crop proteomes. Crops included are banana (musa acuminata), barley (hordeum vulgare), canola (brassica napus), maize (zea mays), potato (solanum tuberosum), rice (oryza sativa), sorghum (sorghum bicolor), soybean (glycine max), tomato (solanum lycopersicum), wheat (triticum aestivum), wine grape (vitis Vinifera) as displayed in the species choice below. The data collection including metadata for proteins and studies can be searched using the query builder below. The reciprocal BLAST allows the search for location data across all crop species as well as compares it to Arabidopsis data from SUBA4.
Find this resource useful? Please cite cropPAL (PubMed,
Plant Cell
Physiol).
Need large parts of the data at once? Bulk downloads available at
RDA
Choose crops below then build a query with the questions below by pressing the → buttons.
matcheswill give you access to the match syntax of MySQL, e.g. entering
+leaf –seed*in the keyword(s) box matches a description that contains leaf but that does not contain seed, seeds, or seedling etc.
Search for proteins that are (or are not) in a list of Identifiers. Enter this list of Identifiers into the box below. See here for a summary of known cross references.
You can use "wildcards" with "like" and "not like" e.g. GO:%
.
matcheswill give you access to the match syntax of MySQL, e.g. entering
+leaf –seed*in the keyword(s) box matches a title/abstract that contains leaf but that does not contain seed, seeds, or seedling etc.
Reciprocal Blast
... Arabidopsis orthologs with blast match score greater than ← must be a number and Arabidopsis consensus location inEnsemblPlants Homology Tree
... any homology with identity greater than ← must be a number and homology type of organism type and has experimentally localized (by MS/MS or GFP) it in:matcheswill give you access to the match syntax of MySQL, e.g. entering
+leaf –seed*in the keyword(s) box matches a title/abstract that contains leaf but that does not contain seed, seeds, or seedling etc.
Bit Score is log2Neff-log2(E-value)
where E-value = pval × Neff
is the p-value times the
effective search space size. The larger the bit-score the better since
pval = P(random seq having a better score) = 2-(bit-score)
. The p-value
measures the statistical significance of the match but since we tried Neff
times to find a match we need to make a correction. Multiplying by the number of possible matches
gives the e-value
or the expected number of hits with a better match just by random chance.
(See here and
here [PDF]).