The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Eric Lecuyer - One of the best experts on this subject based on the ideXlab platform.
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cefra seq systematic mapping of rna Subcellular distribution properties through cell fractionation coupled to deep sequencing
Methods, 2017Co-Authors: Fabio Alexis Lefebvre, Neal A L Cody, Louis Philip Benoit Bouvrette, Julie Bergalet, Xiaofeng Wang, Eric LecuyerAbstract:The Subcellular trafficking of RNA molecules is a conserved feature of eukaryotic cells and plays key functions in diverse processes implicating polarised cellular activities. Large-scale imaging and Subcellular transcriptomic studies suggest that regulated RNA localization is a highly prevalent process that appears to be disrupted in several neuromuscular disorders. These features underline the importance and usefulness of implementing procedures to assess global transcriptome Subcellular distribution properties. Here, we present a method combining biochemical fractionation of cells and high-throughput RNA sequencing (CeFra-seq) that enables rapid and efficient systematic mapping of RNA cytotopic distributions in cells. The described procedure involves biochemical fractionation to derive extracts of nuclear, cytosolic, endomembrane, cytoplasmic insoluble and extracellular material from cell culture lines. The RNA content of each fraction can then be profiled by deep-sequencing, revealing global Subcellular signatures. We provide a detailed protocol for the CeFra-seq procedure along with relevant validation steps and data analysis guidelines to graphically represent RNA spatial distribution features. As a complement to imaging approaches, CeFra-seq represents a powerful and scalable tool to investigate global alterations in RNA trafficking.
Pietro De Camilli - One of the best experts on this subject based on the ideXlab platform.
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light activated protein interaction with high spatial Subcellular confinement
Proceedings of the National Academy of Sciences of the United States of America, 2018Co-Authors: Lorena Benedetti, Andrew E S Barentine, Mirko Messa, Heather Wheeler, Joerg Bewersdorf, Pietro De CamilliAbstract:Methods to acutely manipulate protein interactions at the Subcellular level are powerful tools in cell biology. Several blue-light-dependent optical dimerization tools have been developed. In these systems one protein component of the dimer (the bait) is directed to a specific Subcellular location, while the other component (the prey) is fused to the protein of interest. Upon illumination, binding of the prey to the bait results in its Subcellular redistribution. Here, we compared and quantified the extent of light-dependent dimer occurrence in small, Subcellular volumes controlled by three such tools: Cry2/CIB1, iLID, and Magnets. We show that both the location of the photoreceptor protein(s) in the dimer pair and its (their) switch-off kinetics determine the Subcellular volume where dimer formation occurs and the amount of protein recruited in the illuminated volume. Efficient spatial confinement of dimer to the area of illumination is achieved when the photosensitive component of the dimerization pair is tethered to the membrane of intracellular compartments and when on and off kinetics are extremely fast, as achieved with iLID or Magnets. Magnets and the iLID variants with the fastest switch-off kinetics induce and maintain protein dimerization in the smallest volume, although this comes at the expense of the total amount of dimer. These findings highlight the distinct features of different optical dimerization systems and will be useful guides in the choice of tools for specific applications.
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light activated protein interaction with high spatial Subcellular confinement
Proceedings of the National Academy of Sciences of the United States of America, 2018Co-Authors: Lorena Benedetti, Andrew E S Barentine, Mirko Messa, Heather Wheeler, Joerg Bewersdorf, Pietro De CamilliAbstract:Methods to acutely manipulate protein interactions at the Subcellular level are powerful tools in cell biology. Several blue-light-dependent optical dimerization tools have been developed. In these systems one protein component of the dimer (the bait) is directed to a specific Subcellular location, while the other component (the prey) is fused to the protein of interest. Upon illumination, binding of the prey to the bait results in its Subcellular redistribution. Here, we compared and quantified the extent of light-dependent dimer occurrence in small, Subcellular volumes controlled by three such tools: Cry2/CIB1, iLID, and Magnets. We show that both the location of the photoreceptor protein(s) in the dimer pair and its (their) switch-off kinetics determine the Subcellular volume where dimer formation occurs and the amount of protein recruited in the illuminated volume. Efficient spatial confinement of dimer to the area of illumination is achieved when the photosensitive component of the dimerization pair is tethered to the membrane of intracellular compartments and when on and off kinetics are extremely fast, as achieved with iLID or Magnets. Magnets and the iLID variants with the fastest switch-off kinetics induce and maintain protein dimerization in the smallest volume, although this comes at the expense of the total amount of dimer. These findings highlight the distinct features of different optical dimerization systems and will be useful guides in the choice of tools for specific applications.
Jenn-kang Hwang - One of the best experts on this subject based on the ideXlab platform.
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cello2go a web server for protein Subcellular localization prediction with functional gene ontology annotation
PLOS ONE, 2014Co-Authors: Chihwen Cheng, Kueichung Chang, Shaowei Huang, Jenn-kang HwangAbstract: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.
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Prediction of protein Subcellular localization.
Proteins, 2006Co-Authors: Yu-ching Chen, Jenn-kang HwangAbstract:Because the protein's function is usually related to its Subcellular localization, the ability to predict Subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict Subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting Subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in Subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and Subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of Subcellular localization.
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predicting Subcellular localization of proteins for gram negative bacteria by support vector machines based on n peptide compositions
Protein Science, 2004Co-Authors: Chihjen Lin, Jenn-kang HwangAbstract:Gram-negative bacteria have five major Subcellular localization sites: the cytoplasm, the periplasm, the inner membrane, the outer membrane, and the extracellular space. The Subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict Subcellular localization becomes increasingly important. We present an approach to predict Subcellular localization for Gram-negative bacteria. This method uses the support vector machines trained by multiple feature vectors based on n-peptide compositions. For a standard data set comprising 1443 proteins, the overall prediction accuracy reaches 89%, which, to the best of our knowledge, is the highest prediction rate ever reported. Our prediction is 14% higher than that of the recently developed multimodular PSORT-B. Because of its simplicity, this approach can be easily extended to other organisms and should be a useful tool for the high-throughput and large-scale analysis of proteomic and genomic data.
Lining Zhang - One of the best experts on this subject based on the ideXlab platform.
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RNALocate: a resource for RNA Subcellular localizations.
Nucleic acids research, 2016Co-Authors: Ting Zhang, Puwen Tan, Liqiang Wang, Nana Jin, Lin Zhang, Huan Yang, Lining ZhangAbstract:Increasing evidence has revealed that RNA Subcellular localization is a very important feature for deeply understanding RNA's biological functions after being transported into intra- or extra-cellular regions. RNALocate is a web-accessible database that aims to provide a high-quality RNA Subcellular localization resource and facilitate future researches on RNA function or structure. The current version of RNALocate documents more than 37 700 manually curated RNA Subcellular localization entries with experimental evidence, involving more than 21 800 RNAs with 42 Subcellular localizations in 65 species, mainly including Homo sapiens, Mus musculus and Saccharomyces cerevisiae etc. Besides, RNA homology, sequence and interaction data have also been integrated into RNALocate. Users can access these data through online search, browse, blast and visualization tools. In conclusion, RNALocate will be of help in elucidating the entirety of RNA Subcellular localization, and developing new prediction methods. The database is available at http://www.rna-society.org/rnalocate/.
Xiaoqi Zheng - One of the best experts on this subject based on the ideXlab platform.
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Prediction of bacterial protein Subcellular localization by incorporating various features into Chou's PseAAC and a backward feature selection approach.
Biochimie, 2014Co-Authors: Weidong Xiao, Xiaoqi Zheng, Lan Huang, Shiwen Zhou, Hua YangAbstract:Information on the Subcellular localization of bacterial proteins is essential for protein function prediction, genome annotation and drug design. Here we proposed a novel approach to predict the Subcellular localization of bacterial proteins by fusing features from position-specific score matrix (PSSM), Gene Ontology (GO) and PROFEAT. A backward feature selection approach by linear kennel of SVM was then used to rank the integrated feature vectors and extract optimal features. Finally, SVM was applied for predicting protein Subcellular locations based on these optimal features. To validate the performance of our method, we employed jackknife cross-validation tests on three low similarity datasets, i.e., M638, Gneg1456 and Gpos523. The overall accuracies of 94.98%, 93.21%, and 94.57% were achieved for these three datasets, which are higher (from 1.8% to 10.9%) than those by state-of-the-art tools. Comparison results suggest that our method could serve as a very useful vehicle for expediting the prediction of bacterial protein Subcellular localization.
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predicting Subcellular location of apoptosis proteins with pseudo amino acid composition approach from amino acid substitution matrix and auto covariance transformation
Amino Acids, 2012Co-Authors: Xiaoqing Yu, Xiaoqi Zheng, Jun WangAbstract:Apoptosis proteins are very important for understanding the mechanism of programmed cell death. Obtaining information on Subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on amino acid substitution matrix and auto covariance transformation, we introduce a new sequence-based model, which not only quantitatively describes the differences between amino acids, but also partially incorporates the sequence-order information. This method is applied to predict the apoptosis proteins’ Subcellular location of two widely used datasets by the support vector machine classifier. The results obtained by jackknife test are quite promising, indicating that the proposed method might serve as a potential and efficient prediction model for apoptosis protein Subcellular location prediction.
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an ensemble classifier for eukaryotic protein Subcellular location prediction using gene ontology categories and amino acid hydrophobicity
PLOS ONE, 2012Co-Authors: Yuan Zhang, Xiaoqi Zheng, Lingyun Zou, Yue ZhouAbstract:With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their Subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the Subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict Subcellular locations of eukaryotic proteins.
