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4 results on '"Roos, Manfred"'

1. PredictProtein--an open resource for online prediction of protein structural and functional features

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Yachdav, Guy, Kloppman, Kajan, Hecht, Goldberg, Hamp, Honigschmid, Schafferhans, Roos, Manfred, Bernhofer, Richter, Ashkenazy, Punta, Schlessinger, Bromberg, Schneider, Reinhard, Vriend, Sander, Ben-Tal, Rost, Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Yachdav, Guy, Kloppman, Kajan, Hecht, Goldberg, Hamp, Honigschmid, Schafferhans, Roos, Manfred, Bernhofer, Richter, Ashkenazy, Punta, Schlessinger, Bromberg, Schneider, Reinhard, Vriend, Sander, Ben-Tal, and Rost

Abstract

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.

Published
2014

2. PredictProtein--an open resource for online prediction of protein structural and functional features

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Yachdav, Guy, Kloppman, Kajan, Hecht, Goldberg, Hamp, Honigschmid, Schafferhans, Roos, Manfred, Bernhofer, Richter, Ashkenazy, Punta, Schlessinger, Bromberg, Schneider, Reinhard, Vriend, Sander, Ben-Tal, Rost, Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Yachdav, Guy, Kloppman, Kajan, Hecht, Goldberg, Hamp, Honigschmid, Schafferhans, Roos, Manfred, Bernhofer, Richter, Ashkenazy, Punta, Schlessinger, Bromberg, Schneider, Reinhard, Vriend, Sander, Ben-Tal, and Rost

Abstract

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.

Published
2014

3. A large-scale evaluation of computational protein function prediction.

Author

Radivojac, Predrag, Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, ter Braak, Cajo JF, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, Rost, Burkhard, Radivojac, Predrag, Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, ter Braak, Cajo JF, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, and Rost, Burkhard

Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Published
2013

4. A large-scale evaluation of computational protein function prediction.

Author

Radivojac, Predrag, Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, ter Braak, Cajo JF, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, Rost, Burkhard, Radivojac, Predrag, Radivojac, Predrag, Clark, Wyatt T, Oron, Tal Ronnen, Schnoes, Alexandra M, Wittkop, Tobias, Sokolov, Artem, Graim, Kiley, Funk, Christopher, Verspoor, Karin, Ben-Hur, Asa, Pandey, Gaurav, Yunes, Jeffrey M, Talwalkar, Ameet S, Repo, Susanna, Souza, Michael L, Piovesan, Damiano, Casadio, Rita, Wang, Zheng, Cheng, Jianlin, Fang, Hai, Gough, Julian, Koskinen, Patrik, Törönen, Petri, Nokso-Koivisto, Jussi, Holm, Liisa, Cozzetto, Domenico, Buchan, Daniel WA, Bryson, Kevin, Jones, David T, Limaye, Bhakti, Inamdar, Harshal, Datta, Avik, Manjari, Sunitha K, Joshi, Rajendra, Chitale, Meghana, Kihara, Daisuke, Lisewski, Andreas M, Erdin, Serkan, Venner, Eric, Lichtarge, Olivier, Rentzsch, Robert, Yang, Haixuan, Romero, Alfonso E, Bhat, Prajwal, Paccanaro, Alberto, Hamp, Tobias, Kaßner, Rebecca, Seemayer, Stefan, Vicedo, Esmeralda, Schaefer, Christian, Achten, Dominik, Auer, Florian, Boehm, Ariane, Braun, Tatjana, Hecht, Maximilian, Heron, Mark, Hönigschmid, Peter, Hopf, Thomas A, Kaufmann, Stefanie, Kiening, Michael, Krompass, Denis, Landerer, Cedric, Mahlich, Yannick, Roos, Manfred, Björne, Jari, Salakoski, Tapio, Wong, Andrew, Shatkay, Hagit, Gatzmann, Fanny, Sommer, Ingolf, Wass, Mark N, Sternberg, Michael JE, Škunca, Nives, Supek, Fran, Bošnjak, Matko, Panov, Panče, Džeroski, Sašo, Šmuc, Tomislav, Kourmpetis, Yiannis AI, van Dijk, Aalt DJ, ter Braak, Cajo JF, Zhou, Yuanpeng, Gong, Qingtian, Dong, Xinran, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Di Camillo, Barbara, Toppo, Stefano, Lan, Liang, Djuric, Nemanja, Guo, Yuhong, Vucetic, Slobodan, Bairoch, Amos, Linial, Michal, Babbitt, Patricia C, Brenner, Steven E, Orengo, Christine, and Rost, Burkhard

Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Published
2013

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