subcellular localization is a key feature for characterizing physiological functions of proteins : in eukaryotes compartmentalization finalizes the sets of possible interacting molecules and therefore the biological process(es ) in which a protein is involved . to date it has been carried out only for a narrow subset of known proteins . presently only unicellular species have been extensively analyzed by means of high - throughput experiments , such as s.cerevisiae ( 1,2 ) . different approaches , such as green fluorescent protein ( gfp)-tagging ( 2 ) and immunoluminescence ( 1 ) , agree only on 75% of the annotations and this is mainly due to experimental limitations and possible interference of the tagging procedures on the normal protein trafficking ( 3,4 ) . although it is difficult to scale these techniques to more complex organisms , a partial map for mouse liver cells was recently produced ( 5 ) . curated annotations of the subcellular localization , although probably not covering all the available experimental knowledge , are contained in the swissprot database . the amount of proteins with an experimental annotation listed in swissprot is different for different species and reaches at the most half of the total amount of proteins in a genome ( see below ) . the question remains , however , as to how one can obtain a reliable annotation of subcellular localization for the rest of the proteins . although in principle a change in few residues could result in a change of the localization of a protein , in practice with very few exceptions natural proteins with a sequence identity > 30% share the same localization ( 68 ) . in the most successful cases , about two - thirds of a genome can be annotated both by experimental results and similarity search . many predictors have been developed recently [ ( 4,711 ) , a list is available at ] . a prerequisite for a predictive method is its capability of well performing when the query sequence shares very low sequence similarity to known proteins . it is therefore important to implement and adopt predictors tested with a rigorous cross - validation procedure on sets of proteins < 30% identical with respect to the sequences used for the training . another important feature to be considered in adopting a predictor is how the relative abundance of localization among different sub - compartments was treated during the training phase . indeed , with the available data it is difficult to estimate the real proportions between the proteins targeted into the different subcellular localizations ; most of the predictive methods tend in fact to overestimate localization types for which more examples are known ( 8) . it is therefore necessary to adopt predictive methods that attempt to correct bias towards one or more localization classes . they include ( i ) the results of large - scale experiments for the determination of subcellular localization in specific organisms [ ygfp ( 1 ) , for yeast ; and ormdb ( 5 ) , for mouse ] ; ( ii ) the annotations of proteins to be found in organelles [ plprot ( 12 ) , for proteins from plastids ; organelledb ( 13 ) , for proteins in different organelles ; and mitop2 ( 14 ) , for mitochondrial proteins ] . a database collecting all the annotations listed in swissprot is also available [ dbsubloc ( 15 ) ] . finally databases that implement predictors of subcellular localization based on different methods have been reported [ loctarget ( 16 ) and pa - gosub ( 17 ) ] . loctarget ( 16 ) is specific for structural genomics targets and lists some 50  000 proteins from different organisms . pa - gosub ( 17 ) contains the annotations of eukaryotic subcellular localization and protein function of different genomes and is based on homology search and bayesian artificial networks for prediction . we recently developed bacello , a well - performing balanced method for the prediction of subcellular localization , outperforming previously existing methods for the same task ( 8) . we adopt bacello to annotate whole genomes in association with methods specifically implemented for the prediction of the topology of integral membrane proteins . in this paper we present eukaryotic subcellular localization database ( esldb ) , a database of protein subcellular localization which provides an annotation for the entire proteomes of eukaryotic organisms . for each sequence our database contains the experimental localization , when available , the homology - based annotation , when feasible , and the predicted localization computed with the in - house developed machine learning based methods . by this the new database provides more features than other existing databases . to date , five proteomes were fully processed : homo sapiens , mus musculus , caenorhabditis elegans , saccharomyces cerevisiae and arabidopsis thaliana . in summary , esldb is , to our knowledge , the first database containing the available experimental and similarity - based annotations for eukaryotic proteomes listing for each protein sequence also the predicted subcellular localization . five different genomes were downloaded as specified : h.sapiens ( ensembl ncbi36 ) , m.musculus ( ensembl ncbim36 ) , s.cerevisiae ( ensembl sgd1 ) , c.elegans ( ensembl cel150 ) and a.thaliana ( tair6 ) . for each protein the corresponding swissprot entry in release 50 was found , when existing , searching for exactly matching sequences . the amount of genomic sequences that is deposited in the swissprot database ranges from 13% for both a.thaliana and c.elegans to 79% for s.cerevisiae ( table 1 ) . number of proteins with an experimental or a similarity - based annotation of the subcellular localization the sequences experimentally annotated are included among those annotated by similarity . for these proteins the experimental annotation was extracted by parsing the the annotations directly or implicitly referring to one of the following 17 classes were taken into account : nucleus , cytoplasm , mitochondrion , plastid , golgi , endoplasmic reticulum , lysosome , endosome , vesicles , peroxisome , vacuole , cell wall , secretory pathway , extracellular , cytoskeleton , membrane and transmembrane ( table 1 in supplementary data lists the keywords that have been considered for assigning the localization ) . only 22% of all the swissprot entries for the five considered species record the experimental subcellular localization . the rate of experimental annotation ranges from 46% of the s.cerevisiae proteome to < 10% for a.thaliana and c.elegans ( table 1 ) . the  experimental annotation column in table 2 lists the amount of proteins experimentally annotated in each one of the 17 types of considered localization . it is worth mentioning that the same sequence can be annotated in swissprot with two ( or rarely more ) different localizations . for example , this happens for proteins that shuttle between the nucleus and the cytoplasm . in these cases it is evident that the amount of proteins in the different localizations spans two orders of magnitude . number of sequences in the 17 different subcellular localizations as derived with experimental and similarity - based annotations the sequences experimentally annotated are included among those annotated by similarity . the best way to annotate the remaining proteins is to search for experimentally annotated sequences sharing high identity ( 68 ) . since the three eukaryotic kingdoms ( metazoa , viridiplantae and fungi ) differ in number and types of possible localizations , three kingdom - specific datasets of annotated proteins were extracted from swissprot . these dataset contains 26  192 sequences for metazoa , 6370 sequences for viridiplantae and 4023 sequences for fungi . all the sequences of the five considered genomes were searched for similar sequences in the appropriate dataset using blast ( 18 ) . when matches were found with an e - value < 10 ( roughly corresponding to an identity level > 30% ) the annotation of the best - scoring match was transferred to the query sequence . when multiple matches are found with the same best scoring e - value , similarity - based annotation column in table 2 contains the number of proteins annotated with the above described procedure in each localization ( including the sequences experimentally annotated ) . also in this case , sequences that end up with a multiple annotation are counted several times . it appears that a large portion of the sequences , ranging from 32% in h.sapiens up to 67% in a.thaliana , is not endowed with similar counterparts with an annotated localization . in this case subcellular localization can be predicted with specifically suited methods . for generating our annotation system we developed a pipeline that comprises previously described methods , all based on machine learning tools and that are proved to outperform most of the available predictors for the same task when rigorous cross - validation procedures are adopted ( 8) . the pipeline is shown in figure 1 . first of all , membrane proteins are discriminated with spep ( 19 ) and ensemble ( 20 ) : the former is a neural network based method for predicting the presence of signal peptide while the latter is a method based on neural networks and hidden markov models for the prediction of the topology of all - alpha transmembrane proteins . when a signal peptide is predicted , it is cleaved from the sequence before predicting the presence and the location of the transmembrane helices . if no transmembrane helix is found , the uncleaved sequence is analyzed using bacello ( 8) , a recently developed tool for predicting the subcellular location of eukaryotic proteins . this is based on a decision tree of support vector machines and it discriminates four localizations in metazoa and fungi ( cytoplasm , nucleus , extracellular and mitochondrion ) and five localizations in viridiplantae ( the same as before plus chloroplast ) . bacello , spep and ensemble are predictive methods described previously ( 8,17,18 ) . at the end of the pipeline up to five localizations can be discriminated in metazoa and fungi and up to six in viridiplantae . although the possible types of localization are 17 ( see above ) , the actual reduction in the number of discriminated localization is due to the lack of an adequate number of non - redundant examples for training . a novelty of bacello is that first takes into consideration that the actual proportion of proteins targeted towards each compartment remains unknown by adopting an equiprobability hypothesis and a balancing procedure ( 8) . the structure of the predictive system allows annotating the subcellular localization in a hierarchical way . first , all membrane proteins are separated from soluble ones ; the latter are then divided into intracellular and secreted . intracellular proteins are separated in nucleocytoplasmic and organellar ; the former are then separated in cytoplasmic and nuclear while the latter , in the case of viridiplantae , are further divided into mitochondrial and chloroplastic . the topology of the decision tree and the balancing procedure were adopted for maximizing the prediction performances as evaluated on testing sets independent of the training sets . the best scoring binary decisions are at the top of the tree , the worst - scoring at the bottom . the predictions are stored in the database along with the hierarchy of the decisions in the pipeline . all the proteins of a genome , also when experimental and/or homology - based annotation are possible , are annotated by means of the predictive method . in table 3 number of sequences in the six predicted subcellular localizations table 4 contains the evaluation of the coverage and the accuracy of the prediction for the proteins of h.sapiens , as compared with both the experimental and the similarity derived annotations . we considered 6444 unique proteins experimentally annotated and 25 134 unique sequences for which a similarity - based annotation is available . the coverage is computed as the fraction of correctly predicted sequences in each class over the number of proteins belonging to the class . the accuracy is the fraction of correctly predicted proteins over the total number of proteins predicted in the class . the agreement between the annotations and the prediction is good , especially when predictions are compared with the experimental annotations and the higher levels of the hierarchical prediction are considered . performance of the prediction pipeline as compared with the experimental and the similarity - based annotations the indentation of the subcellular localization names reflects the hierarchy of the prediction ( see figure 1 ) . coverage = ( no . of proteins of class i predicted as class i)/(total no . all the data are available at the website ( ) and can be accessed by protein code , as derived from the original and above - mentioned versions of the genomic databases , or by protein description , as derived from the swissprot entries . alternatively the sequence of interest can be submitted and , by means of the md5 encoding , the match engine searches for identical sequences deposited in the database . moreover , complex searches can be performed combining the different annotation methods and the different localizations . the entries matching the query keys can either be displayed or downloaded in a tabular format that contains the experimental and the similarity - derived annotation and the localization prediction for each match . pages containing more details about each one of the proteins ( figure 2 ) are linked to the protein codes and contain the sequence , the description and the link to the swissprot file when an experimental annotation exists . when the sequence can be annotated by similarity , the swissprot entry corresponding to the most similar sequence is reported together with the e - value as computed by blast ( 18 ) . in both the tabular and the detailed pages , the results of the prediction are given together with the complete path through the decision tree . this information can be useful since , as we commented in the previous section , the accuracy of the prediction lowers as the number of discriminated classes increases . this means that annotations in the macro - classes are endowed with a higher reliability . all the data contained in the database can be freely downloaded in flat file format . the database resides in a postgresql server and the web interface has been implemented using python , html 4.0 and css 2.0 languages . more available eukaryotic proteomes are currently under process and will be added to the database . moreover we plan to regularly update the database as new versions of swissprot or new releases of the considered proteomes will be available .
eukaryotic subcellular localization database collects the annotations of subcellular localization of eukaryotic proteomes . so far 
 five proteomes have been processed and stored : homo sapiens , mus musculus , caenorhabditis elegans , saccharomyces cerevisiae and arabidopsis thaliana . for each sequence , 
 the database lists localization obtained adopting three different approaches : ( i ) experimentally determined ( when available ) ; ( ii ) homology - based ( when possible ) ; and ( iii ) predicted . 
 the latter is computed with a suite of machine learning based methods , developed in house . 
 all the data are available at our website and can be searched by sequence , by protein code and/or by protein description . 
 furthermore , a more complex search can be performed combining different search fields and keys . 
 all the data contained in the database can be freely downloaded in flat file format . 
 the database is available at .