dbRES: a web-oriented database for annotated RNA editing sites

Although a large amount of experimentally derived information about RNA editing sites currently exists, this information has remained scattered in a variety of sources and in diverse data formats. Availability of standard collections for high-quality experimental data will be by of great help for systematic studying of RNA editing, especially for developing computational algorithm to predict RNA editing site. dbRES () is a public database of known RNA editing sites. All sites are manually curated from literature and GenBank annotations. dbRES version 1.1 contains 5437 RNA editing sites of 251 transcripts, covering 96 organisms across plant, metazoan, protozoa, fungi and virus. dbRES provides comprehensive annotations and data summaries, including (but not limited to) transcript sequences, RNA editing types, editing site locations, amino acid changes, organisms, subcellular organelles (if available), cited references, etc. A user-friendly web interface is developed to facilitate both retrieving data and online display of RNA edit site information

Identifying Human Kinase-Specific Protein Phosphorylation Sites by Integrating Heterogeneous Information from Various Sources

Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by in vitro or in vivo experiments is time consuming and expensive; in silico prediction can provide functional candidates and help narrow down the experimental efforts. Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (avaiable at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates

Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

BACKGROUND: Knowing the submitochondria localization of a mitochondria protein is an important step to understand its function. We develop a method which is based on an extended version of pseudo-amino acid composition to predict the protein localization within mitochondria. This work goes one step further than predicting protein subcellular location. We also try to predict the membrane protein type for mitochondrial inner membrane proteins. RESULTS: By using leave-one-out cross validation, the prediction accuracy is 85.5% for inner membrane, 94.5% for matrix and 51.2% for outer membrane. The overall prediction accuracy for submitochondria location prediction is 85.2%. For proteins predicted to localize at inner membrane, the accuracy is 94.6% for membrane protein type prediction. CONCLUSION: Our method is an effective method for predicting protein submitochondria location. But even with our method or the methods at subcellular level, the prediction of protein submitochondria location is still a challenging problem. The online service SubMito is now available at

Predicting human protein subcellular locations by the ensemble of multiple predictors via protein-protein interaction network with edge clustering coefficients.

One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods

CURE-Chloroplast: A chloroplast C-to-U RNA editing predictor for seed plants

Abstract Background RNA editing is a type of post-transcriptional modification of RNA and belongs to the class of mechanisms that contribute to the complexity of transcriptomes. C-to-U RNA editing is commonly observed in plant mitochondria and chloroplasts. The in vivo mechanism of recognizing C-to-U RNA editing sites is still unknown. In recent years, many efforts have been made to computationally predict C-to-U RNA editing sites in the mitochondria of seed plants, but there is still no algorithm available for C-to-U RNA editing site prediction in the chloroplasts of seed plants. Results In this paper, we extend our algorithm CURE, which can accurately predict the C-to-U RNA editing sites in mitochondria, to predict C-to-U RNA editing sites in the chloroplasts of seed plants. The algorithm achieves over 80% sensitivity and over 99% specificity. We implement the algorithm as an online service called CURE-Chloroplast http://bioinfo.au.tsinghua.edu.cn/pure. Conclusion CURE-Chloroplast is an online service for predicting the C-to-U RNA editing sites in the chloroplasts of seed plants. The online service allows the processing of entire chloroplast genome sequences. Since CURE-Chloroplast performs very well, it could be a helpful tool in the study of C-to-U RNA editing in the chloroplasts of seed plants.</p

The summary of dataset.

<p>(A) The number of locative proteins in different subcellular locations. There are 6951 proteins with experimentally annotated subcellular locations in the dataset. Because one protein may have more than one subcellular location, the number of locative proteins is 9493. (B) The number of proteins with different number of subcellular locations.</p

Performances of iterative prediction.

a<p>AIM is Aiming, as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086879#pone.0086879.e015" target="_blank">eqn (15)</a>;</p>b<p>CVR is Coverage, as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086879#pone.0086879.e016" target="_blank">eqn (16)</a>;</p>c<p>ACC is Accuracy, as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086879#pone.0086879.e017" target="_blank">eqn (17)</a>;</p>d<p>ATR is Absolute-True-Rate, as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086879#pone.0086879.e018" target="_blank">eqn (18)</a>;</p>e<p>AFR is Absolute-False-Rate, as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0086879#pone.0086879.e019" target="_blank">eqn (19)</a>.</p