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Journal Articles
2015
81.
Hirsh L; Piovesan D; Giollo M; Ferrari C; Tosatto S C E
The Victor C++ library for protein representation and advanced manipulation Journal Article
In: Bioinformatics, vol. 31, no. 7, pp. 1138-1140, 2015, (Cited by: 5; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84929143784,
title = {The Victor C++ library for protein representation and advanced manipulation},
author = {Layla Hirsh and Damiano Piovesan and Manuel Giollo and Carlo Ferrari and Silvio C. E. Tosatto},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84929143784&origin=inward},
doi = {10.1093/bioinformatics/btu773},
year = {2015},
date = {2015-01-01},
journal = {Bioinformatics},
volume = {31},
number = {7},
pages = {1138-1140},
publisher = {Oxford University Pressjnl.info@oup.co.uk},
abstract = {© The Author 2014.Motivation: Protein sequence and structure representation and manipulation require dedicated software libraries to support methods of increasing complexity. Here, we describe the VIrtual Constrution TOol for pRoteins (Victor) C++ library, an open source platform dedicated to enabling inexperienced users to develop advanced tools and gathering contributions from the community. The provided application examples cover statistical energy potentials, profile-profile sequence alignments and ab initio loop modeling. Victor was used over the last 15 years in several publications and optimized for efficiency. It is provided as a GitHub repository with source files and unit tests, plus extensive online documentation, including a Wiki with help files and tutorials, examples and Doxygen documentation.},
note = {Cited by: 5; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© The Author 2014.Motivation: Protein sequence and structure representation and manipulation require dedicated software libraries to support methods of increasing complexity. Here, we describe the VIrtual Constrution TOol for pRoteins (Victor) C++ library, an open source platform dedicated to enabling inexperienced users to develop advanced tools and gathering contributions from the community. The provided application examples cover statistical energy potentials, profile-profile sequence alignments and ab initio loop modeling. Victor was used over the last 15 years in several publications and optimized for efficiency. It is provided as a GitHub repository with source files and unit tests, plus extensive online documentation, including a Wiki with help files and tutorials, examples and Doxygen documentation.
2014
82.
Domenico T D; Potenza E; Walsh I; Parra G; Giollo M; Minervini G; Piovesan D; Ihsan A; Ferrari C; Kajava A V; Tosatto S C E
RepeatsDB: A database of tandem repeat protein structures Journal Article
In: Nucleic Acids Research, vol. 42, no. D1, 2014, (Cited by: 59; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84891766568,
title = {RepeatsDB: A database of tandem repeat protein structures},
author = {Tomás Di Domenico and Emilio Potenza and Ian Walsh and Gonzalo Parra and Manuel Giollo and Giovanni Minervini and Damiano Piovesan and Awais Ihsan and Carlo Ferrari and Andrey V. Kajava and Silvio C. E. Tosatto},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84891766568&origin=inward},
doi = {10.1093/nar/gkt1175},
year = {2014},
date = {2014-01-01},
journal = {Nucleic Acids Research},
volume = {42},
number = {D1},
abstract = {RepeatsDB (http://repeatsdb.bio.unipd.it/) is a database of annotated tandem repeat protein structures. Tandem repeats pose a difficult problem for the analysis of protein structures, as the underlying sequence can be highly degenerate. Several repeat types haven been studied over the years, but their annotation was done in a case-by-case basis, thus making large-scale analysis difficult. We developed RepeatsDB to fill this gap. Using state-of-the-art repeat detection methods and manual curation, we systematically annotated the Protein Data Bank, predicting 10 745 repeat structures. In all, 2797 structures were classified according to a recently proposed classification schema, which was expanded to accommodate new findings. In addition, detailed annotations were performed in a subset of 321 proteins. These annotations feature information on start and end positions for the repeat regions and units. RepeatsDB is an ongoing effort to systematically classify and annotate structural protein repeats in a consistent way. It provides users with the possibility to access and download high-quality datasets either interactively or programmatically through web services. © 2013 The Author(s). Published by Oxford University Press.},
note = {Cited by: 59; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
RepeatsDB (http://repeatsdb.bio.unipd.it/) is a database of annotated tandem repeat protein structures. Tandem repeats pose a difficult problem for the analysis of protein structures, as the underlying sequence can be highly degenerate. Several repeat types haven been studied over the years, but their annotation was done in a case-by-case basis, thus making large-scale analysis difficult. We developed RepeatsDB to fill this gap. Using state-of-the-art repeat detection methods and manual curation, we systematically annotated the Protein Data Bank, predicting 10 745 repeat structures. In all, 2797 structures were classified according to a recently proposed classification schema, which was expanded to accommodate new findings. In addition, detailed annotations were performed in a subset of 321 proteins. These annotations feature information on start and end positions for the repeat regions and units. RepeatsDB is an ongoing effort to systematically classify and annotate structural protein repeats in a consistent way. It provides users with the possibility to access and download high-quality datasets either interactively or programmatically through web services. © 2013 The Author(s). Published by Oxford University Press.
83.
Martelli P L; Fontanesi L; Piovesan D; Fariselli P; Casadio R
Mapping and annotating obesity-related genes in pig and human genomes Journal Article
In: Protein and Peptide Letters, vol. 21, no. 8, pp. 840-846, 2014, (Cited by: 3).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84903728406,
title = {Mapping and annotating obesity-related genes in pig and human genomes},
author = {Pier Luigi Martelli and Luca Fontanesi and Damiano Piovesan and Piero Fariselli and Rita Casadio},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84903728406&origin=inward},
doi = {10.2174/09298665113209990053},
year = {2014},
date = {2014-01-01},
journal = {Protein and Peptide Letters},
volume = {21},
number = {8},
pages = {840-846},
publisher = {Bentham Science PublishersP.O. Box 294Bussum1400 AG},
abstract = {Background. Obesity is a major health problem in both developed and emerging countries. Obesity is a complex disease whose etiology involves genetic factors in strong interplay with environmental determinants and lifestyle. The discovery of genetic factors and biological pathways underlying human obesity is hampered by the difficulty in controlling the genetic background of human cohorts. Animal models are then necessary to further dissect the genetics of obesity. Pig has emerged as one of the most attractive models, because of the similarity with humans in the mechanisms regulating the fat deposition. Results. We collected the genes related to obesity in humans and to fat deposition traits in pig. We localized them on both human and pig genomes, building a map useful to interpret comparative studies on obesity. We characterized the collected genes structurally and functionally with BAR+ and mapped them on KEGG pathways and on STRING protein interaction network. Conclusions. The collected set consists of 361 obesity related genes in human and pig genomes. All genes were mapped on the human genome, and 54 could not be localized on the pig genome (release 2012). Only for 3 human genes there is no counterpart in pig, confirming that this animal is a good model for human obesity studies. Obesity related genes are mostly involved in regulation and signaling processes/pathways and relevant connection emerges between obesity-related genes and diseases such as cancer and infectious diseases. © 2014 Bentham Science Publishers.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background. Obesity is a major health problem in both developed and emerging countries. Obesity is a complex disease whose etiology involves genetic factors in strong interplay with environmental determinants and lifestyle. The discovery of genetic factors and biological pathways underlying human obesity is hampered by the difficulty in controlling the genetic background of human cohorts. Animal models are then necessary to further dissect the genetics of obesity. Pig has emerged as one of the most attractive models, because of the similarity with humans in the mechanisms regulating the fat deposition. Results. We collected the genes related to obesity in humans and to fat deposition traits in pig. We localized them on both human and pig genomes, building a map useful to interpret comparative studies on obesity. We characterized the collected genes structurally and functionally with BAR+ and mapped them on KEGG pathways and on STRING protein interaction network. Conclusions. The collected set consists of 361 obesity related genes in human and pig genomes. All genes were mapped on the human genome, and 54 could not be localized on the pig genome (release 2012). Only for 3 human genes there is no counterpart in pig, confirming that this animal is a good model for human obesity studies. Obesity related genes are mostly involved in regulation and signaling processes/pathways and relevant connection emerges between obesity-related genes and diseases such as cancer and infectious diseases. © 2014 Bentham Science Publishers.
2013
84.
Minneci F; Piovesan D; Cozzetto D; Jones D T
FFPred 2.0: Improved Homology-Independent Prediction of Gene Ontology Terms for Eukaryotic Protein Sequences Journal Article
In: PLoS ONE, vol. 8, no. 5, 2013, (Cited by: 43; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84878095702,
title = {FFPred 2.0: Improved Homology-Independent Prediction of Gene Ontology Terms for Eukaryotic Protein Sequences},
author = {Federico Minneci and Damiano Piovesan and Domenico Cozzetto and David T. Jones},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84878095702&origin=inward},
doi = {10.1371/journal.pone.0063754},
year = {2013},
date = {2013-01-01},
journal = {PLoS ONE},
volume = {8},
number = {5},
abstract = {To understand fully cell behaviour, biologists are making progress towards cataloguing the functional elements in the human genome and characterising their roles across a variety of tissues and conditions. Yet, functional information - either experimentally validated or computationally inferred by similarity - remains completely missing for approximately 30% of human proteins. FFPred was initially developed to bridge this gap by targeting sequences with distant or no homologues of known function and by exploiting clear patterns of intrinsic disorder associated with particular molecular activities and biological processes. Here, we present an updated and improved version, which builds on larger datasets of protein sequences and annotations, and uses updated component feature predictors as well as revised training procedures. FFPred 2.0 includes support vector regression models for the prediction of 442 Gene Ontology (GO) terms, which largely expand the coverage of the ontology and of the biological process category in particular. The GO term list mainly revolves around macromolecular interactions and their role in regulatory, signalling, developmental and metabolic processes. Benchmarking experiments on newly annotated proteins show that FFPred 2.0 provides more accurate functional assignments than its predecessor and the ProtFun server do; also, its assignments can complement information obtained using BLAST-based transfer of annotations, improving especially prediction in the biological process category. Furthermore, FFPred 2.0 can be used to annotate proteins belonging to several eukaryotic organisms with a limited decrease in prediction quality. We illustrate all these points through the use of both precision-recall plots and of the COGIC scores, which we recently proposed as an alternative numerical evaluation measure of function prediction accuracy. © 2013 Minneci et al.},
note = {Cited by: 43; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To understand fully cell behaviour, biologists are making progress towards cataloguing the functional elements in the human genome and characterising their roles across a variety of tissues and conditions. Yet, functional information – either experimentally validated or computationally inferred by similarity – remains completely missing for approximately 30% of human proteins. FFPred was initially developed to bridge this gap by targeting sequences with distant or no homologues of known function and by exploiting clear patterns of intrinsic disorder associated with particular molecular activities and biological processes. Here, we present an updated and improved version, which builds on larger datasets of protein sequences and annotations, and uses updated component feature predictors as well as revised training procedures. FFPred 2.0 includes support vector regression models for the prediction of 442 Gene Ontology (GO) terms, which largely expand the coverage of the ontology and of the biological process category in particular. The GO term list mainly revolves around macromolecular interactions and their role in regulatory, signalling, developmental and metabolic processes. Benchmarking experiments on newly annotated proteins show that FFPred 2.0 provides more accurate functional assignments than its predecessor and the ProtFun server do; also, its assignments can complement information obtained using BLAST-based transfer of annotations, improving especially prediction in the biological process category. Furthermore, FFPred 2.0 can be used to annotate proteins belonging to several eukaryotic organisms with a limited decrease in prediction quality. We illustrate all these points through the use of both precision-recall plots and of the COGIC scores, which we recently proposed as an alternative numerical evaluation measure of function prediction accuracy. © 2013 Minneci et al.
85.
Radivojac P; Clark W T; Oron T R; Schnoes A M; Wittkop T; Sokolov A; Graim K; Funk C; Verspoor K; Ben-Hur A; Pandey G; Yunes J M; Talwalkar A S; Repo S; Souza M L; Piovesan D; Casadio R; Wang Z; Cheng J; Fang H; Gough J; Koskinen P; Törönen P; Nokso-Koivisto J; Holm L; Cozzetto D; Buchan D W A; Bryson K; Jones D T; Limaye B; Inamdar H; Datta A; Manjari S K; Joshi R; Chitale M; Kihara D; Lisewski A M; Erdin S; Venner E; Lichtarge O; Rentzsch R; Yang H; Romero A E; Bhat P; Paccanaro A; Hamp T; Kaßner R; Seemayer S; Vicedo E; Schaefer C; Achten D; Auer F; Boehm A; Braun T; Hecht M; Heron M; Hönigschmid P; Hopf T A; Kaufmann S; Kiening M; Krompass D; Landerer C; Mahlich Y; Roos M; Björne J; Salakoski T; Wong A; Shatkay H; Gatzmann F; Sommer I; Wass M N; Sternberg M J E; Škunca N; Supek F; Bošnjak M; Panov P; Džeroski S; Šmuc T; Kourmpetis Y A I; Dijk A D J V; Braak C J F T; Zhou Y; Gong Q; Dong X; Tian W; Falda M; Fontana P; Lavezzo E; Camillo B D; Toppo S; Lan L; Djuric N; Guo Y; Vucetic S; Bairoch A; Linial M; Babbitt P C; Brenner S E; Orengo C; Rost B; …
A large-scale evaluation of computational protein function prediction Journal Article
In: Nature Methods, vol. 10, no. 3, pp. 221-227, 2013, (Cited by: 808; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:84874663959,
title = {A large-scale evaluation of computational protein function prediction},
author = {Predrag Radivojac and Wyatt T. Clark and Tal Ronnen Oron and Alexandra M. Schnoes and Tobias Wittkop and Artem Sokolov and Kiley Graim and Christopher Funk and Karin Verspoor and Asa Ben-Hur and Gaurav Pandey and Jeffrey M. Yunes and Ameet S. Talwalkar and Susanna Repo and Michael L. Souza and Damiano Piovesan and Rita Casadio and Zheng Wang and Jianlin Cheng and Hai Fang and Julian Gough and Patrik Koskinen and Petri Törönen and Jussi Nokso-Koivisto and Liisa Holm and Domenico Cozzetto and Daniel W. A. Buchan and Kevin Bryson and David T. Jones and Bhakti Limaye and Harshal Inamdar and Avik Datta and Sunitha K. Manjari and Rajendra Joshi and Meghana Chitale and Daisuke Kihara and Andreas M. Lisewski and Serkan Erdin and Eric Venner and Olivier Lichtarge and Robert Rentzsch and Haixuan Yang and Alfonso E. Romero and Prajwal Bhat and Alberto Paccanaro and Tobias Hamp and Rebecca Kaßner and Stefan Seemayer and Esmeralda Vicedo and Christian Schaefer and Dominik Achten and Florian Auer and Ariane Boehm and Tatjana Braun and Maximilian Hecht and Mark Heron and Peter Hönigschmid and Thomas A. Hopf and Stefanie Kaufmann and Michael Kiening and Denis Krompass and Cedric Landerer and Yannick Mahlich and Manfred Roos and Jari Björne and Tapio Salakoski and Andrew Wong and Hagit Shatkay and Fanny Gatzmann and Ingolf Sommer and Mark N. Wass and Michael J. E. Sternberg and Nives Škunca and Fran Supek and Matko Bošnjak and Panče Panov and Sašo Džeroski and Tomislav Šmuc and Yiannis A. I. Kourmpetis and Aalt D. J. Van Dijk and Cajo J. F. Ter Braak and Yuanpeng Zhou and Qingtian Gong and Xinran Dong and Weidong Tian and Marco Falda and Paolo Fontana and Enrico Lavezzo and Barbara Di Camillo and Stefano Toppo and Liang Lan and Nemanja Djuric and Yuhong Guo and Slobodan Vucetic and Amos Bairoch and Michal Linial and Patricia C. Babbitt and Steven E. Brenner and Christine Orengo and Burkhard Rost and ...},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84874663959&origin=inward},
doi = {10.1038/nmeth.2340},
year = {2013},
date = {2013-01-01},
journal = {Nature Methods},
volume = {10},
number = {3},
pages = {221-227},
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. © 2013 Nature America, Inc. All rights reserved.},
note = {Cited by: 808; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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. © 2013 Nature America, Inc. All rights reserved.