The Experts below are selected from a list of 4398 Experts worldwide ranked by ideXlab platform
Kenta Nakai - One of the best experts on this subject based on the ideXlab platform.
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WoLF PSORT: Protein Localization Prediction Software
2016Co-Authors: Paul Horton, Keun-joon Park, Takeshi Obayashi, Kenta NakaiAbstract:Intracellular localization is an important clue to the function of proteins and aberrant localization has been implicated in human disease. Fortunately the one dimensional amino acid sequences of proteins, can yield much accessible information regarding localization. WoLF PSORT draws heavily from the PSORT [2] and PSORTII [3] programs, but unlike those older programs, uses feature selection and
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WoLF PSORT: protein localization predictor.
Nucleic Acids Research, 2007Co-Authors: Paul Horton, Keun-joon Park, Takeshi Obayashi, Naoya Fujita, Hajime Harada, C.j. Adams-collier, Kenta NakaiAbstract:WoLF PSORT is 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. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfPSORT.org
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PSORT b improving protein subcellular localization prediction for gram negative bacteria
Nucleic Acids Research, 2003Co-Authors: Jennifer L. Gardy, Martin Ester, Cory Spencer, Ke Wang, Gabor E Tusnady, Istvan Simon, Sujun Hua, Katalin Defays, Christophe Lambert, Kenta NakaiAbstract:Automated prediction of bacterial protein subcellular localization is an important tool for genome annotation and drug discovery. PSORT has been one of the most widelyused computational methods for such bacterial protein analysis; however, it has not been updated since it was introduced in 1991. In addition, neither PSORT nor anyof the other computational methods available make predictions for all five of the localization sites characteristic of Gram-negative bacteria. Here we present PSORT-B, an updated version of PSORT for Gram-negative bacteria, which is available as a web-based application at http://www.PSORT.org. PSORT-B examines a given protein sequence for amino acid composition, similarityto proteins of known localization, presence of a signal peptide, transmembrane alpha-helices and motifs corresponding to specific localizations. A probabilistic method integrates these analyses, returning a list of five possible localization sites with associated probabilityscores. PSORT-B, designed to favor high precision (specificity) over high recall (sensitivity), attained an overall precision of 97% and recall of 75% in 5-fold cross-validation tests, using a dataset we developed of 1443 proteins of experimentallyknown localization. This dataset, the largest of its kind, is freelyavailable, along with the PSORT-B source code (under GNU General Public License).
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Improvement of PSORT II Protein Sorting Prediction for Mammalian Proteins
Genome Informatics, 2002Co-Authors: Mitsuteru Nakao, Kenta NakaiAbstract:The PSORT system [8] is a unique tool for the prediction of protein subcellular localization in a sense that it can deal with proteins localized at almost all the subcellular compartments. In its several versions, PSORT II [5] was developed for the prediction of eukaryotic proteins using yeast sequences as its training data. The reason why the data from a single species were used was that training data were favored to reflect the subcellular proportion of a proteome. However, since the yeast is a unicellular organism, applying PSORT II to sequences of multicellular organisms can be problematic sometimes. For example, it has been pointed out that secretary proteins tend to be under-predicted. Since the first release of PSORT II, genome projects have been producing rich information of genes for many model organisms including yeasts, nematode, mouse and human. Amongst them, the information of mouse genes is managed in Mouse Genome Database [2] (MGD) and Mouse Genome Informatics (MGI). The information includes the data of the full length cDNAs [7] with the annotation of subcellular localization sites of their products. In this work, we report the improvement of PSORT II from three aspects: the employment of mammalian (murine) data, the optimization of the learning method, and the optimization of the sequence features used.
Jennifer L. Gardy - One of the best experts on this subject based on the ideXlab platform.
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Methods for predicting bacterial protein subcellular localization
Nature Reviews Microbiology, 2006Co-Authors: Jennifer L. Gardy, Fiona S. L. BrinkmanAbstract:The prediction of a bacterial protein's subcellular localization can be of considerable aid to microbiological research. It can be used to infer potential functions for a protein, to either design or support the results of particular experimental approaches and, in the case of surface-exposed proteins, to quickly identify potential drug or vaccine targets in a given pathogen genome, or potential diagnostic/detection targets in pathogen or environmental isolates. Bacterial proteins contain sequence features that either directly influence the targeting of a protein to a particular cellular compartment or else are characteristic of proteins found at a specific localization site. These features are encoded in the protein's amino-acid sequence and can be identified computationally. By analyzing a protein for the presence or absence of one or more of these features and integrating the results, a prediction of which compartment a protein is likely to reside in can be generated. Since the 1991 release of the first comprehensive, web-based bacterial protein localization prediction method, PSORT I, seven other such tools have been released. This review summarizes the techniques implemented by each tool, their benefits, pitfalls and predictive performance. The review also describes alternative methods for localization prediction, including similarity searches against localization databases and the use of predictive tools designed to identify individual sequence features. The performance of these methods is compared with that of the seven broad-spectrum localization prediction tools. PSORTb and Proteome Analyst are the most precise predictive methods currently available, with other methods complementing them when higher sensitivity (a larger number of predictions) is required. The precision of certain localization prediction tools has now surpassed the precision of some high-throughput laboratory methods for localization determination. We can now reliably assign potential localization sites to the majority of proteins encoded in a genome. The computational prediction of the particular cellular compartment that a bacterial protein is destined for is an important aspect of microbiological research. This article discusses the methods currently available to predict bacterial protein localization. The computational prediction of the subcellular localization of bacterial proteins is an important step in genome annotation and in the search for novel vaccine or drug targets. Since the 1991 release of PSORT I ? the first comprehensive algorithm to predict bacterial protein localization ? many other localization prediction tools have been developed. These methods offer significant improvements in predictive performance over PSORT I and the accuracy of some methods now rivals that of certain high-throughput laboratory methods for protein localization identification.
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PSORT b improving protein subcellular localization prediction for gram negative bacteria
Nucleic Acids Research, 2003Co-Authors: Jennifer L. Gardy, Martin Ester, Cory Spencer, Ke Wang, Gabor E Tusnady, Istvan Simon, Sujun Hua, Katalin Defays, Christophe Lambert, Kenta NakaiAbstract:Automated prediction of bacterial protein subcellular localization is an important tool for genome annotation and drug discovery. PSORT has been one of the most widelyused computational methods for such bacterial protein analysis; however, it has not been updated since it was introduced in 1991. In addition, neither PSORT nor anyof the other computational methods available make predictions for all five of the localization sites characteristic of Gram-negative bacteria. Here we present PSORT-B, an updated version of PSORT for Gram-negative bacteria, which is available as a web-based application at http://www.PSORT.org. PSORT-B examines a given protein sequence for amino acid composition, similarityto proteins of known localization, presence of a signal peptide, transmembrane alpha-helices and motifs corresponding to specific localizations. A probabilistic method integrates these analyses, returning a list of five possible localization sites with associated probabilityscores. PSORT-B, designed to favor high precision (specificity) over high recall (sensitivity), attained an overall precision of 97% and recall of 75% in 5-fold cross-validation tests, using a dataset we developed of 1443 proteins of experimentallyknown localization. This dataset, the largest of its kind, is freelyavailable, along with the PSORT-B source code (under GNU General Public License).
Koji Nakano - One of the best experts on this subject based on the ideXlab platform.
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AN EFFICIENT PARALLEL SORTING COMPATIBLE WITH THE STANDARD QSORT
International Journal of Foundations of Computer Science, 2011Co-Authors: Duhu Man, Yasuaki Ito, Koji NakanoAbstract:The main contribution of this paper is to present an efficient parallel sorting "PSORT" compatible with the standard qsort. Our parallel sorting "PSORT" is implemented such that its interface is compatible with "qsort" in C Standard Library. Therefore, any application program that uses standard "qsort" can be accelerated by simply replacing "qsort" call by our "PSORT". Also, "PSORT" uses standard "qsort" as a subroutine for local sequential sorting. So, if the performance of "qsort" is improved by anyone in the open source community, then that of our "PSORT" is also automatically improved. To evaluate the performance of our "PSORT", we have implemented our parallel sorting in a Linux server with four Intel hexad-core processors (i.e. twenty four processor cores). The experimental results show that our "PSORT" is approximately 11 times faster than standard "qsort" using 24 processors.
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PDCAT - An Efficient Parallel Sorting Compatible with the Standard qsort
2009 International Conference on Parallel and Distributed Computing Applications and Technologies, 2009Co-Authors: Duhu Man, Yasuaki Ito, Koji NakanoAbstract:The main contribution of this paper is to present an efficient parallel sorting "PSORT" compatible with the standard qsort. Our parallel sorting "PSORT" is implemented such that its interface is compatible with "qsort" in C Standard Library. Therefore, any application program that uses standard "qsort" can be accelerated by simply replacing "qsort" call by our "PSORT" . Also, "PSORT" uses standard "qsort" as a subroutine for local sequential sorting. So, if the performance of "qsort" is improved by anyone in the community, then that of our "PSORT" is also automatically improved. To evaluate the performance of our "PSORT", we have implemented our parallel sorting in a Linux server with two Intel quad-core processors (i. e. eight processor cores). The experimental results show that our "PSORT" is approximately 6 times faster than standard "qsort" using 8 processors. Since the speed up factor cannot be more than 8 if we use 8 cores, our algorithm is close to optimal. Also, as far as we know, no previously published parallel implementations achieve a speed up factor less than 4 using 8 cores.
Paul Horton - One of the best experts on this subject based on the ideXlab platform.
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WoLF PSORT: Protein Localization Prediction Software
2016Co-Authors: Paul Horton, Keun-joon Park, Takeshi Obayashi, Kenta NakaiAbstract:Intracellular localization is an important clue to the function of proteins and aberrant localization has been implicated in human disease. Fortunately the one dimensional amino acid sequences of proteins, can yield much accessible information regarding localization. WoLF PSORT draws heavily from the PSORT [2] and PSORTII [3] programs, but unlike those older programs, uses feature selection and
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WoLF PSORT: protein localization predictor.
Nucleic Acids Research, 2007Co-Authors: Paul Horton, Keun-joon Park, Takeshi Obayashi, Naoya Fujita, Hajime Harada, C.j. Adams-collier, Kenta NakaiAbstract:WoLF PSORT is 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. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfPSORT.org
Hong-bin Shen - One of the best experts on this subject based on the ideXlab platform.
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Large-scale predictions of gram-negative bacterial protein subcellular locations.
Journal of proteome research, 2006Co-Authors: Kuo-chen Chou, Hong-bin ShenAbstract:Many species of Gram-negative bacteria are pathogenic bacteria that can cause disease in a host organism. This pathogenic capability is usually associated with certain components in Gram-negative cells. Therefore, developing an automated method for fast and reliabe prediction of Gram-negative protein subcellular location will allow us to not only timely annotate gene products, but also screen candidates for drug discovery. However, protein subcellular location prediction is a very difficult problem, particularly when more location sites need to be involved and when unknown query proteins do not have significant homology to proteins of known subcellular locations. PSORT-B, a recently updated version of PSORT, widely used for predicting Gram-negative protein subcellular location, only covers five location sites. Also, the data set used to train PSORT-B contains many proteins with high degrees of sequence identity in a same location group and, hence, may bear a strong homology bias. To overcome these problem...
