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https://www.readbyqxmd.com/read/27812935/computational-methods-for-annotation-transfers-from-sequence
#1
Domenico Cozzetto, David T Jones
Surveys of public sequence resources show that experimentally supported functional information is still completely missing for a considerable fraction of known proteins and is clearly incomplete for an even larger portion. Bioinformatics methods have long made use of very diverse data sources alone or in combination to predict protein function, with the understanding that different data types help elucidate complementary biological roles. This chapter focuses on methods accepting amino acid sequences as input and producing GO term assignments directly as outputs; the relevant biological and computational concepts are presented along with the advantages and limitations of individual approaches...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/27604469/an-expanded-evaluation-of-protein-function-prediction-methods-shows-an-improvement-in-accuracy
#2
Yuxiang Jiang, Tal Ronnen Oron, Wyatt T Clark, Asma R Bankapur, Daniel D'Andrea, Rosalba Lepore, Christopher S Funk, Indika Kahanda, Karin M Verspoor, Asa Ben-Hur, Da Chen Emily Koo, Duncan Penfold-Brown, Dennis Shasha, Noah Youngs, Richard Bonneau, Alexandra Lin, Sayed M E Sahraeian, Pier Luigi Martelli, Giuseppe Profiti, Rita Casadio, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Adrian Altenhoff, Nives Skunca, Christophe Dessimoz, Tunca Dogan, Kai Hakala, Suwisa Kaewphan, Farrokh Mehryary, Tapio Salakoski, Filip Ginter, Hai Fang, Ben Smithers, Matt Oates, Julian Gough, Petri Törönen, Patrik Koskinen, Liisa Holm, Ching-Tai Chen, Wen-Lian Hsu, Kevin Bryson, Domenico Cozzetto, Federico Minneci, David T Jones, Samuel Chapman, Dukka Bkc, Ishita K Khan, Daisuke Kihara, Dan Ofer, Nadav Rappoport, Amos Stern, Elena Cibrian-Uhalte, Paul Denny, Rebecca E Foulger, Reija Hieta, Duncan Legge, Ruth C Lovering, Michele Magrane, Anna N Melidoni, Prudence Mutowo-Meullenet, Klemens Pichler, Aleksandra Shypitsyna, Biao Li, Pooya Zakeri, Sarah ElShal, Léon-Charles Tranchevent, Sayoni Das, Natalie L Dawson, David Lee, Jonathan G Lees, Ian Sillitoe, Prajwal Bhat, Tamás Nepusz, Alfonso E Romero, Rajkumar Sasidharan, Haixuan Yang, Alberto Paccanaro, Jesse Gillis, Adriana E Sedeño-Cortés, Paul Pavlidis, Shou Feng, Juan M Cejuela, Tatyana Goldberg, Tobias Hamp, Lothar Richter, Asaf Salamov, Toni Gabaldon, Marina Marcet-Houben, Fran Supek, Qingtian Gong, Wei Ning, Yuanpeng Zhou, Weidong Tian, Marco Falda, Paolo Fontana, Enrico Lavezzo, Stefano Toppo, Carlo Ferrari, Manuel Giollo, Damiano Piovesan, Silvio C E Tosatto, Angela Del Pozo, José M Fernández, Paolo Maietta, Alfonso Valencia, Michael L Tress, Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino, Hafeez Ur Rehman, Matteo Re, Marco Mesiti, Giorgio Valentini, Joachim W Bargsten, Aalt D J van Dijk, Branislava Gemovic, Sanja Glisic, Vladmir Perovic, Veljko Veljkovic, Nevena Veljkovic, Danillo C Almeida-E-Silva, Ricardo Z N Vencio, Malvika Sharan, Jörg Vogel, Lakesh Kansakar, Shanshan Zhang, Slobodan Vucetic, Zheng Wang, Michael J E Sternberg, Mark N Wass, Rachael P Huntley, Maria J Martin, Claire O'Donovan, Peter N Robinson, Yves Moreau, Anna Tramontano, Patricia C Babbitt, Steven E Brenner, Michal Linial, Christine A Orengo, Burkhard Rost, Casey S Greene, Sean D Mooney, Iddo Friedberg, Predrag Radivojac
BACKGROUND: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. RESULTS: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function...
2016: Genome Biology
https://www.readbyqxmd.com/read/27561554/ffpred-3-feature-based-function-prediction-for-all-gene-ontology-domains
#3
Domenico Cozzetto, Federico Minneci, Hannah Currant, David T Jones
Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-wide blind tests aimed at benchmarking available tools on a genomic scale. Sequence-based predictors, especially those performing homology-based transfers, remain the most popular but increasing understanding of their limitations has stimulated the development of complementary approaches, which mostly exploit machine learning. Here we present FFPred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homology with characterized proteins can provide little aid...
2016: Scientific Reports
https://www.readbyqxmd.com/read/25391399/disopred3-precise-disordered-region-predictions-with-annotated-protein-binding-activity
#4
David T Jones, Domenico Cozzetto
MOTIVATION: A sizeable fraction of eukaryotic proteins contain intrinsically disordered regions (IDRs), which act in unfolded states or by undergoing transitions between structured and unstructured conformations. Over time, sequence-based classifiers of IDRs have become fairly accurate and currently a major challenge is linking IDRs to their biological roles from the molecular to the systems level. RESULTS: We describe DISOPRED3, which extends its predecessor with new modules to predict IDRs and protein-binding sites within them...
March 15, 2015: Bioinformatics
https://www.readbyqxmd.com/read/25348407/genome3d-exploiting-structure-to-help-users-understand-their-sequences
#5
Tony E Lewis, Ian Sillitoe, Antonina Andreeva, Tom L Blundell, Daniel W A Buchan, Cyrus Chothia, Domenico Cozzetto, José M Dana, Ioannis Filippis, Julian Gough, David T Jones, Lawrence A Kelley, Gerard J Kleywegt, Federico Minneci, Jaina Mistry, Alexey G Murzin, Bernardo Ochoa-Montaño, Matt E Oates, Marco Punta, Owen J L Rackham, Jonathan Stahlhacke, Michael J E Sternberg, Sameer Velankar, Christine Orengo
Genome3D (http://www.genome3d.eu) is a collaborative resource that provides predicted domain annotations and structural models for key sequences. Since introducing Genome3D in a previous NAR paper, we have substantially extended and improved the resource. We have annotated representatives from Pfam families to improve coverage of diverse sequences and added a fast sequence search to the website to allow users to find Genome3D-annotated sequences similar to their own. We have improved and extended the Genome3D data, enlarging the source data set from three model organisms to 10, and adding VIVACE, a resource new to Genome3D...
January 2015: Nucleic Acids Research
https://www.readbyqxmd.com/read/23900810/evaluation-of-predictions-in-the-casp10-model-refinement-category
#6
Timothy Nugent, Domenico Cozzetto, David T Jones
Here we report on the assessment results of the third experiment to evaluate the state of the art in protein model refinement, where participants were invited to improve the accuracy of initial protein models for 27 targets. Using an array of complementary evaluation measures, we find that five groups performed better than the naïve (null) method-a marked improvement over CASP9, although only three were significantly better. The leading groups also demonstrated the ability to consistently improve both backbone and side chain positioning, while other groups reliably enhanced other aspects of protein physicality...
February 2014: Proteins
https://www.readbyqxmd.com/read/23717476/ffpred-2-0-improved-homology-independent-prediction-of-gene-ontology-terms-for-eukaryotic-protein-sequences
#7
Federico Minneci, Damiano Piovesan, Domenico Cozzetto, David T Jones
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...
2013: PloS One
https://www.readbyqxmd.com/read/23514099/protein-function-prediction-by-massive-integration-of-evolutionary-analyses-and-multiple-data-sources
#8
Domenico Cozzetto, Daniel W A Buchan, Kevin Bryson, David T Jones
BACKGROUND: Accurate protein function annotation is a severe bottleneck when utilizing the deluge of high-throughput, next generation sequencing data. Keeping database annotations up-to-date has become a major scientific challenge that requires the development of reliable automatic predictors of protein function. The CAFA experiment provided a unique opportunity to undertake comprehensive 'blind testing' of many diverse approaches for automated function prediction. We report on the methodology we used for this challenge and on the lessons we learnt...
2013: BMC Bioinformatics
https://www.readbyqxmd.com/read/23466039/the-contribution-of-intrinsic-disorder-prediction-to-the-elucidation-of-protein-function
#9
REVIEW
Domenico Cozzetto, David T Jones
Current knowledge of protein function stems primarily from the detailed characterization of some protein domain families and from extensive homology-based annotation transfers. Intrinsically disordered proteins exploit complementary molecular recognition mechanisms and their study has potential to make a significant contribution to the field. Building on established computational biology techniques, efforts are now aimed at unveiling their biological roles at the molecular and system level. Recent developments towards identifying functional sites in disordered regions, and analysing how the occurrence of protein disorder in a gene can provide insight into its biochemical and cellular function are discussed...
June 2013: Current Opinion in Structural Biology
https://www.readbyqxmd.com/read/23353650/a-large-scale-evaluation-of-computational-protein-function-prediction
#10
Predrag Radivojac, Wyatt T Clark, Tal Ronnen Oron, Alexandra M Schnoes, Tobias Wittkop, Artem Sokolov, Kiley Graim, Christopher Funk, Karin Verspoor, Asa Ben-Hur, Gaurav Pandey, Jeffrey M Yunes, Ameet S Talwalkar, Susanna Repo, Michael L Souza, Damiano Piovesan, Rita Casadio, Zheng Wang, Jianlin Cheng, Hai Fang, Julian Gough, Patrik Koskinen, Petri Törönen, Jussi Nokso-Koivisto, Liisa Holm, Domenico Cozzetto, Daniel W A Buchan, Kevin Bryson, David T Jones, Bhakti Limaye, Harshal Inamdar, Avik Datta, Sunitha K Manjari, Rajendra Joshi, Meghana Chitale, Daisuke Kihara, Andreas M Lisewski, Serkan Erdin, Eric Venner, Olivier Lichtarge, Robert Rentzsch, Haixuan Yang, Alfonso E Romero, Prajwal Bhat, Alberto Paccanaro, Tobias Hamp, Rebecca Kaßner, Stefan Seemayer, Esmeralda Vicedo, Christian Schaefer, Dominik Achten, Florian Auer, Ariane Boehm, Tatjana Braun, Maximilian Hecht, Mark Heron, Peter Hönigschmid, Thomas A Hopf, Stefanie Kaufmann, Michael Kiening, Denis Krompass, Cedric Landerer, Yannick Mahlich, Manfred Roos, Jari Björne, Tapio Salakoski, Andrew Wong, Hagit Shatkay, Fanny Gatzmann, Ingolf Sommer, Mark N Wass, Michael J E Sternberg, Nives Škunca, Fran Supek, Matko Bošnjak, Panče Panov, Sašo Džeroski, Tomislav Šmuc, Yiannis A I Kourmpetis, Aalt D J van Dijk, Cajo J F ter Braak, Yuanpeng Zhou, Qingtian Gong, Xinran Dong, Weidong Tian, Marco Falda, Paolo Fontana, Enrico Lavezzo, Barbara Di Camillo, Stefano Toppo, Liang Lan, Nemanja Djuric, Yuhong Guo, Slobodan Vucetic, Amos Bairoch, Michal Linial, Patricia C Babbitt, Steven E Brenner, Christine Orengo, Burkhard Rost, Sean D Mooney, Iddo Friedberg
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...
March 2013: Nature Methods
https://www.readbyqxmd.com/read/22101153/psicov-precise-structural-contact-prediction-using-sparse-inverse-covariance-estimation-on-large-multiple-sequence-alignments
#11
David T Jones, Daniel W A Buchan, Domenico Cozzetto, Massimiliano Pontil
MOTIVATION: The accurate prediction of residue-residue contacts, critical for maintaining the native fold of a protein, remains an open problem in the field of structural bioinformatics. Interest in this long-standing problem has increased recently with algorithmic improvements and the rapid growth in the sizes of sequence families. Progress could have major impacts in both structure and function prediction to name but two benefits. Sequence-based contact predictions are usually made by identifying correlated mutations within multiple sequence alignments (MSAs), most commonly through the information-theoretic approach of calculating mutual information between pairs of sites in proteins...
January 15, 2012: Bioinformatics
https://www.readbyqxmd.com/read/17430199/computer-assisted-protein-domain-boundary-prediction-using-the-dompred-server
#12
REVIEW
Kevin Bryson, Domenico Cozzetto, David T Jones
Domain prediction from sequence is a particularly challenging task, and currently, a large variety of different methodologies are employed to tackle the task. Here we try to classify these diverse approaches into a number of broad categories. Completely automatic domain prediction from sequence alone is currently fraught with problems, but this should not be so surprising since human experts currently have significant disagreement on domain assignment even when given the structures. It can be argued that we should only test the domain prediction methods on benchmark data that human experts agree upon and this is the approach we take in this paper...
April 2007: Current Protein & Peptide Science
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