Dataset Information

CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.

ABSTRACT: CELLO2GO (http://cello.life.nctu.edu.tw/cello2go/) is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization. Herein, we describe how this platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches. Given a query protein sequence, CELLO2GO uses BLAST to search for homologous sequences that are GO annotated in an in-house database derived from the UniProt KnowledgeBase database. At the same time, CELLO attempts predict at least one subcellular localization on the basis of the species in which the protein is found. When homologs for the query sequence have been identified, the number of terms found for each of their GO categories, i.e., cellular compartment, molecular function, and biological process, are summed and presented as pie charts representing possible functional annotations for the queried protein. Although the experimental subcellular localization of a protein may not be known, and thus not annotated, CELLO can confidentially suggest a subcellular localization. CELLO2GO should be a useful tool for research involving complex subcellular systems because it combines CELLO and BLAST into one platform and its output is easily manipulated such that the user-specific questions may be readily addressed.

SUBMITTER: Yu CS

PROVIDER: S-EPMC4049835 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.

Yu Chin-Sheng CS Cheng Chih-Wen CW Su Wen-Chi WC Chang Kuei-Chung KC Huang Shao-Wei SW Hwang Jenn-Kang JK Lu Chih-Hao CH

PloS one 20140609 6

CELLO2GO (http://cello.life.nctu.edu.tw/cello2go/) is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization. Herein, we describe how this platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches. Given a query protein sequence, CELLO2GO uses BLAST to ...[more]

PMID: 24911789

Similar Datasets

Project description:BACKGROUND: Gene Ontology (GO) annotation, which describes the function of genes and gene products across species, has recently been used to predict protein subcellular and subnuclear localization. Existing GO-based prediction methods for protein subcellular localization use the known accession numbers of query proteins to obtain their annotated GO terms. An accurate prediction method for predicting subcellular localization of novel proteins without known accession numbers, using only the input sequence, is worth developing. RESULTS: This study proposes an efficient sequence-based method (named ProLoc-GO) by mining informative GO terms for predicting protein subcellular localization. For each protein, BLAST is used to obtain a homology with a known accession number to the protein for retrieving the GO annotation. A large number n of all annotated GO terms that have ever appeared are then obtained from a large set of training proteins. A novel genetic algorithm based method (named GOmining) combined with a classifier of support vector machine (SVM) is proposed to simultaneously identify a small number m out of the n GO terms as input features to SVM, where m <<n. The m informative GO terms contain the essential GO terms annotating subcellular compartments such as GO:0005634 (Nucleus), GO:0005737 (Cytoplasm) and GO:0005856 (Cytoskeleton). Two existing data sets SCL12 (human protein with 12 locations) and SCL16 (Eukaryotic proteins with 16 locations) with <25% sequence identity are used to evaluate ProLoc-GO which has been implemented by using a single SVM classifier with the m = 44 and m = 60 informative GO terms, respectively. ProLoc-GO using input sequences yields test accuracies of 88.1% and 83.3% for SCL12 and SCL16, respectively, which are significantly better than the SVM-based methods, which achieve < 35% test accuracies using amino acid composition (AAC) with acid pairs and AAC with dipedtide composition. For comparison, ProLoc-GO using known accession numbers of query proteins yields test accuracies of 90.6% and 85.7%, which is also better than Hum-PLoc (85.0%) and Euk-OET-PLoc (83.7%) using ensemble classifiers with hybridization of GO terms and amphiphilic pseudo amino acid composition for SCL12 and SCL16, respectively. CONCLUSION: The growth of Gene Ontology in size and popularity has increased the effectiveness of GO-based features. GOmining can serve as a tool for selecting informative GO terms in solving sequence-based prediction problems. The prediction system using ProLoc-GO with input sequences of query proteins for protein subcellular localization has been implemented (see Availability).

Project description:The Gene Ontology (GO) vocabulary has been extensively explored to analyze the functions of coexpressed genes. However, despite its extended use in Biology and Medical Sciences, there are still high levels of uncertainty about which ontology (i.e. Molecular Process, Cellular Component or Molecular Function) should be used, and at which level of specificity. Moreover, the GO database can contain incomplete information resulting from human annotations, or highly influenced by the available knowledge about a specific branch in an ontology. In spite of these drawbacks, there is a trend to ignore these problems and even use GO terms to conduct searches of gene expression profiles (i.e. expression + GO) instead of more cautious approaches that just consider them as an independent source of validation (i.e. expression versus GO). Consequently, propagating the uncertainty and producing biased analysis of the required gene grouping hypotheses. We proposed a web tool, Onto-CC, as an automatic method specially suited for independent explanation/validation of gene grouping hypotheses (e.g. coexpressed genes) based on GO clusters (i.e. expression versus GO). Onto-CC approach reduces the uncertainty of the queries by identifying optimal conceptual clusters that combine terms from different ontologies simultaneously, as well as terms defined at different levels of specificity in the GO hierarchy. To do so, we implemented the EMO-CC methodology to find clusters in structural databases [GO Directed acyclic Graph (DAG) tree], inspired on Conceptual Clustering algorithms. This approach allows the management of optimal cluster sets as potential parallel hypotheses, guided by multiobjective/multimodal optimization techniques. Therefore, we can generate alternative and, still, optimal explanations of queries that can provide new insights for a given problem. Onto-CC has been successfully used to test different medical and biological hypotheses including the explanation and prediction of gene expression profiles resulting from the host response to injuries in the inflammatory problem. Onto-CC provides two versions: Ready2GO, a precalculated EMO-CC for several genomes and an Advanced Onto-CC for custom annotation files (http://gps-tools2.wustl.edu/onto-cc/index.html).

Project description:BackgroundAlthough many computational methods have been developed to predict protein subcellular localization, most of the methods are limited to the prediction of single-location proteins. Multi-location proteins are either not considered or assumed not existing. However, proteins with multiple locations are particularly interesting because they may have special biological functions, which are essential to both basic research and drug discovery.ResultsThis paper proposes an efficient multi-label predictor, namely mGOASVM, for predicting the subcellular localization of multi-location proteins. Given a protein, the accession numbers of its homologs are obtained via BLAST search. Then, the original accession number and the homologous accession numbers of the protein are used as keys to search against the Gene Ontology (GO) annotation database to obtain a set of GO terms. Given a set of training proteins, a set of T relevant GO terms is obtained by finding all of the GO terms in the GO annotation database that are relevant to the training proteins. These relevant GO terms then form the basis of a T-dimensional Euclidean space on which the GO vectors lie. A support vector machine (SVM) classifier with a new decision scheme is proposed to classify the multi-label GO vectors. The mGOASVM predictor has the following advantages: (1) it uses the frequency of occurrences of GO terms for feature representation; (2) it selects the relevant GO subspace which can substantially speed up the prediction without compromising performance; and (3) it adopts an efficient multi-label SVM classifier which significantly outperforms other predictors. Briefly, on two recently published virus and plant datasets, mGOASVM achieves an actual accuracy of 88.9% and 87.4%, respectively, which are significantly higher than those achieved by the state-of-the-art predictors such as iLoc-Virus (74.8%) and iLoc-Plant (68.1%).ConclusionsmGOASVM can efficiently predict the subcellular locations of multi-label proteins. The mGOASVM predictor is available online at http://bioinfo.eie.polyu.edu.hk/mGoaSvmServer/mGOASVM.html.

Dataset Information

CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.

Publications

CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets