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A tool for the analysis of subcellular proteomics data, based on the use of standardized lists of subcellular markers. MetaMass analyzed data from 11 studies using MetaMass, mapping the subcellular location of 5,970 proteins. Our analysis revealed large variations in the performance of subcellular fractionation protocols as well as systematic biases in protein annotation databases. The Excel and R versions of MetaMass should enhance transparency and reproducibility in subcellular proteomics.
Predicts the amino acid sequence-based human protein subcellular location to cover human subcellular localizations. The sequences are represented by multi-view complementary features, i.e., context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. The major updates include: i) taking into consideration feature correlation and the hierarchical structure of GO terms; ii) extracting residue features from different segments of N and C-terminals; and iii) use of the latest versions of gene ontology, conserved domain database and SWISS-PROT database. Hum-mPLoc is designed to predict subcellular localization of human proteins.
An extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. WoLF PSORT not only provides subcellular localization prediction with competitive accuracy, but also provides detailed information relevant to protein localization to help users to form their own hypotheses.
Determines nine sub-cellular localizations. SubCons employs a Random Forest (RF) classifier to combine four predictors. It can generate Position-Specific Scoring Matrixes (PSSMs) and offers users the option to submit entire proteomes. This tool is helpful to understand the localization of a protein, in particular as it scales to complete genomes. It provides state of the art predictions, a confidence score rates the reliability of a prediction in order to evaluate the reliability of the prediction.
PLPD / Protein Localization Predictor based on D-SVDD
Predicts protein localization. PLPD can detect the likelihood of specific localization for a protein by using the Density-induced Support Vector Data Description (D-SVDD). D-SVDD is extended for this algorithm to run the prediction of protein subcellular localization. It utilizes three measurements for the assessment and to refine the protein localization predictor. PLPD approach is complimentary to other method such as the nearest neighbor or the discriminate covariant method.
Predicts five classes of subcellular localization of proteins in eukaryotes: secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast. BaCelLo is a method for subcellular localization prediction that adopts a balancing procedure by assuming a uniform a priori probability for the classes. This software distinguishes between animal and fungal organisms. It can be used for large scale analysis of whole genomes to produce an estimate and annotation of the protein content in each subcellular compartment.
A protein subcellular localization prediction pipeline aiming to deal with the small sample size learning and multi-location proteins annotation problems. Five semi-supervised algorithms that can make use of lower-quality data were integrated, and a new multi-label classification approach by incorporating the correlations among different organelles in cells was proposed. The organelle correlations were modeled by the Bayesian network, and the topology of the correlation graph was used to guide the order of binary classifiers training in the multi-label classification to reflect the label dependence relationship.
FUEL-mLoc / Feature-Unified Prediction and Explanation of multi-Localization of cellular proteins in multiple organisms
Interprets the prediction decisions by unified features, which are more clear, self-evident and structured. FUEL-mLoc is a web-server which not only predicts single- and multi-label proteins from almost all of the common organisms, but also provides interpretable information to explain why the prediction decisions are made. Compared to existing interpretable web-servers, FUEL-mLoc uses unified feature information for more clear, self-evident and structured interpretation. Besides, FUEL-mLoc performs better than other subcellular-localization predictors.
Predicts the subcellular localization of eukaryotic proteins. DeepLoc is a well assembled protein collection with reliable subcellular localization information. This online resource can differentiate between 10 different localizations: Nucleus, Cytoplasm, Extracellular, Mitochondrion, Cell membrane, Endoplasmic reticulum, Chloroplast, Golgi apparatus, Lysosome/Vacuole and Peroxisome. This model outperforms current state-of-the-art algorithms, even those relying on homology information.
Predicts protein sub-chloroplastic localization, based on targeting signal detection and membrane protein information. SChloro is based on the recognition of sequence signals that define target specificity (chloroplast and thylakoid targeting signals) as well as on the prediction of the potential type of interaction with chloroplast membranes (single-pass, multi-pass and peripheral interaction). SChloro significantly outperforms the available state-of-the-art methods, both in single and multi-label settings.
Finds the subcellular localization of human proteins. pLoc-mHum aims to improve its absolute true and absolute false rates. It can be applied to the multiple locations of human proteins. This tool is useful for the annotation of the subcellular locations of human proteins. It can serve for the following location sites: centrosome, cytoplasm, cytoskeleton, endoplasmic reticulum, endosome, extracellular, Golgi apparatus, Lysosome, microsome, mitochondrion, nucleus, peroxisome, plasma membrane, and synapse.
AtSubP / Arabidopsis Subcellular localization Predictor
Localizes gene products at the subcellular level will substantially advance Arabidopsis gene annotation. AtSubP is based on the combinatorial presence of diverse protein features, such as its amino acid composition, sequence-order effects, terminal information, Position-Specific Scoring Matrix, and similarity search-based Position-Specific Iterated-Basic Local Alignment Search Tool information. AtSubP outperformed all the existing tools currently being used for Arabidopsis proteome annotation.
Organelle-specific proteomic studies have started to delineate its various subproteomes, but sequence-based prediction software is necessary to assign proteins subcellular localizations at whole genome scale. Unfortunately, existing tools are oriented toward land plants and tend to mispredict the localization of nuclear-encoded algal proteins, predicting many chloroplast proteins as mitochondrion targeted. PredAlgo predicts intracellular localization of those proteins to one of three intracellular compartments in green algae: the mitochondrion, the chloroplast, and the secretory pathway.
Predicts the subcellular localization by combining sequence-based and text-based information. SherLoc2 is based on a support vector machine (SVM) prediction system. It uses seven sub-classifiers to predict all 11 main eukaryotic locations (nucleus, cytoplasm, mitochondrion and more). This software can predict 9 or 10 locations for each animal, plant, and fungal proteins categories. It integrates also annotated gene ontology (GO) terms and textual information from Swiss-Prot keywords and PubMed abstracts.
Predicts mitochondrial protein as well as their submitochondrial location. SubMitoPred is based on the combined use of Pfam domain information and support vector machine (SVM) based prediction methods. The software can simultaneously predict whether the query protein is mitochondrial or not, and if it is mitochondrial, in which part of the mitochondria the protein will go. It can also be used for proteome scale annotation of mitochondrial as well as sub-mitochondrial proteins in eukaryotic organisms.
A hybrid method for prediction of eukaryotic protein subcellular localization. EuLoc incorporates the hidden Markov model (HMM) method, homology search approach and the support vector machines (SVM) method by fusing several new features into Chou's pseudo-amino acid composition. The proposed SVM module overcomes the shortcoming of the homology search approach in predicting the subcellular localization of a protein which only finds low-homologous or non-homologous sequences in a protein subcellular localization annotated database. The proposed HMM modules overcome the shortcoming of SVM in predicting subcellular localizations using few data on protein sequences. Several features of a protein sequence are considered, including the sequence-based features, the biological features derived from PROSITE, NLSdb and Pfam, the post-transcriptional modification features and others. The overall accuracy and location accuracy of EuLoc are 90.5 and 91.2 %, respectively, revealing a better predictive performance than obtained elsewhere.
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