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3. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T, Bankapur, Asma R, D'Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S, Kahanda, Indika, Verspoor, Karin M, Ben-Hur, Asa, Koo, Emily, Penfold-Brown, Duncan, Shasha, Dennis, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed ME, Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nives, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T, Chapman, Samuel, C., Dukka B K., Khan, Ishita K, Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amos, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E, Hieta, Reija, Legge, Duncan, Lovering, Ruth C, Magrane, Michele, Melidoni, Anna N, Mutowo-Meullenet, Prudence, Pichler, Klemens, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, ElShal, Sarah, Tranchevent, Léon-Charles, Das, Sayoni, Dawson, Natalie L, Lee, David, Lees, Jonathan G, Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamás, Romero, Alfonso E, Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E, Pavlidis, Paul, Feng, Shou, Cejuela, Juan M, Goldberg, Tatyana, Hamp, Tobias, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio, del Pozo, Angela, Fernández, José M, Maietta, Paolo, Valencia, Alfonso, Tress, Michael L, Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco, Savino, Alessandro, Rehman, Hafeez Ur, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W, van Dijk, Aalt DJ, Gemovic, Branislava, Glisic, Sanja, Perovic, Vladmir, Veljkovic, Veljko, Veljkovic, Nevena, Almeida-e-Silva, Danillo C, Vencio, Ricardo ZN, Sharan, Malvika, Vogel, Jörg, Kansakar, Lakesh, Zhang, Shanshan, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael JE, Wass, Mark N, Huntley, Rachael P, Martin, Maria J, O'Donovan, Claire, Robinson, Peter N, Moreau, Yves, Tramontano, Anna, Babbitt, Patricia C, Brenner, Steven E, Linial, Michal, Orengo, Christine A, Rost, Burkhard, Greene, Casey S, Mooney, Sean D, Friedberg, Iddo, and Radivojac, Predrag

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations in technology for generating data and the inherently stochastic nature of biomolecular events have led to the discrepancy between the volume of data and the amount of knowledge gleaned from it. A major bottleneck in our ability to understand the molecular underpinnings of life is the assignment of function to biological macromolecules, especially 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, accurately assessing methods for protein function prediction and tracking progress in the field remain challenging. Methodology: We have conducted the second Critical Assessment of Functional Annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. One hundred twenty-six methods from 56 research groups were evaluated for their ability to predict biological functions using the Gene Ontology and gene-disease associations using the Human Phenotype Ontology on a set of 3,681 proteins from 18 species. CAFA2 featured significantly expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2. Conclusions: The top performing methods in CAFA2 outperformed the best methods from CAFA1, demonstrating that computational function prediction is improving. This increased accuracy can be attributed to the combined effect of the growing number of experimental annotations and improved methods for function prediction., Comment: Submitted to Genome Biology

Published
2016
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5. A large-scale evaluation of computational protein function prediction

Author

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, Braak, Cajo JF ter, 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

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Algorithms, Animals, Computational Biology, Databases, Protein, Exoribonucleases, Forecasting, Humans, Molecular Biology, Molecular Sequence Annotation, Proteins, Species Specificity, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
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

6. DDPLA: A Dynamic Differential Privacy Algorithm for Social Network Based on Local Community

Author

Yuanpeng Long Yuanpeng Long, Xianyi Zhou Yuanpeng Long, Yang Li Xianyi Zhou, Xuena Zhang Yang Li, and Bin Xing Xuena Zhang

Subjects
Computer Networks and Communications, Software
Abstract

Social networks contain a large number of privacy information. Personal privacy will be jeopardized if network data without privacy protection is released directly. In view of the current privacy protection technology to protect the social network, there are some problems such as low maintenance of network structure or low accuracy of network data. In order to solve these problems, this paper proposes a dynamic differential privacy algorithm for social network based on local community (DDPLA). The algorithm can divide the social network into different communities, dynamically generate privacy budgets for different communities, and then generate uncertainty graphs. Experiments show that compared with other algorithms, the social network processed by DDPLA algorithm can better balance data utility and privacy protection. Furthermore, the algorithm can better protect important nodes. &nbsp

Published
2023
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9. Epigenetic features are significantly associated with alternative splicing

Author

Zhou Yuanpeng, Lu Yulan, and Tian Weidong

Subjects
Alternative splicing, Epigenetics, DNA methylation, Nucleosome occupancy, Histone modifications, Transcription factors, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background While alternative splicing (AS) contributes greatly to protein diversities, the relationship between various types of AS and epigenetic factors remains largely unknown. Results In this study, we discover that a number of epigenetic features, including DNA methylation, nucleosome occupancy, specific histone modifications and protein features, are strongly associated with AS. To further enhance our understanding of the association between these features and AS, we cluster our investigated features based on their association patterns with each AS type into four groups, with H3K36me3, EGR1, GABP, SRF, SIN3A and RNA Pol II grouped together and showing strongest association with AS. In addition, we find that the AS types can be classified into two general classes, namely the exon skipping related process (ESRP), and the alternative splice site selection process (ASSP), based on their association levels with the epigenetic features. Conclusion Our analysis thus suggests that epigenetic features are likely to play important roles in regulating AS.

Published
2012
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12. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Jiang, Yuxiang, primary, Oron, Tal Ronnen, additional, Clark, Wyatt T., additional, Bankapur, Asma R., additional, D’Andrea, Daniel, additional, Lepore, Rosalba, additional, Funk, Christopher S., additional, Kahanda, Indika, additional, Verspoor, Karin M., additional, Ben-Hur, Asa, additional, Koo, Da Chen Emily, additional, Penfold-Brown, Duncan, additional, Shasha, Dennis, additional, Youngs, Noah, additional, Bonneau, Richard, additional, Lin, Alexandra, additional, Sahraeian, Sayed M. E., additional, Martelli, Pier Luigi, additional, Profiti, Giuseppe, additional, Casadio, Rita, additional, Cao, Renzhi, additional, Zhong, Zhaolong, additional, Cheng, Jianlin, additional, Altenhoff, Adrian, additional, Skunca, Nives, additional, Dessimoz, Christophe, additional, Dogan, Tunca, additional, Hakala, Kai, additional, Kaewphan, Suwisa, additional, Mehryary, Farrokh, additional, Salakoski, Tapio, additional, Ginter, Filip, additional, Fang, Hai, additional, Smithers, Ben, additional, Oates, Matt, additional, Gough, Julian, additional, Törönen, Petri, additional, Koskinen, Patrik, additional, Holm, Liisa, additional, Chen, Ching-Tai, additional, Hsu, Wen-Lian, additional, Bryson, Kevin, additional, Cozzetto, Domenico, additional, Minneci, Federico, additional, Jones, David T., additional, Chapman, Samuel, additional, BKC, Dukka, additional, Khan, Ishita K., additional, Kihara, Daisuke, additional, Ofer, Dan, additional, Rappoport, Nadav, additional, Stern, Amos, additional, Cibrian-Uhalte, Elena, additional, Denny, Paul, additional, Foulger, Rebecca E., additional, Hieta, Reija, additional, Legge, Duncan, additional, Lovering, Ruth C., additional, Magrane, Michele, additional, Melidoni, Anna N., additional, Mutowo-Meullenet, Prudence, additional, Pichler, Klemens, additional, Shypitsyna, Aleksandra, additional, Li, Biao, additional, Zakeri, Pooya, additional, ElShal, Sarah, additional, Tranchevent, Léon-Charles, additional, Das, Sayoni, additional, Dawson, Natalie L., additional, Lee, David, additional, Lees, Jonathan G., additional, Sillitoe, Ian, additional, Bhat, Prajwal, additional, Nepusz, Tamás, additional, Romero, Alfonso E., additional, Sasidharan, Rajkumar, additional, Yang, Haixuan, additional, Paccanaro, Alberto, additional, Gillis, Jesse, additional, Sedeño-Cortés, Adriana E., additional, Pavlidis, Paul, additional, Feng, Shou, additional, Cejuela, Juan M., additional, Goldberg, Tatyana, additional, Hamp, Tobias, additional, Richter, Lothar, additional, Salamov, Asaf, additional, Gabaldon, Toni, additional, Marcet-Houben, Marina, additional, Supek, Fran, additional, Gong, Qingtian, additional, Ning, Wei, additional, Zhou, Yuanpeng, additional, Tian, Weidong, additional, Falda, Marco, additional, Fontana, Paolo, additional, Lavezzo, Enrico, additional, Toppo, Stefano, additional, Ferrari, Carlo, additional, Giollo, Manuel, additional, Piovesan, Damiano, additional, Tosatto, Silvio C.E., additional, del Pozo, Angela, additional, Fernández, José M., additional, Maietta, Paolo, additional, Valencia, Alfonso, additional, Tress, Michael L., additional, Benso, Alfredo, additional, Di Carlo, Stefano, additional, Politano, Gianfranco, additional, Savino, Alessandro, additional, Rehman, Hafeez Ur, additional, Re, Matteo, additional, Mesiti, Marco, additional, Valentini, Giorgio, additional, Bargsten, Joachim W., additional, van Dijk, Aalt D. J., additional, Gemovic, Branislava, additional, Glisic, Sanja, additional, Perovic, Vladmir, additional, Veljkovic, Veljko, additional, Veljkovic, Nevena, additional, Almeida-e-Silva, Danillo C., additional, Vencio, Ricardo Z. N., additional, Sharan, Malvika, additional, Vogel, Jörg, additional, Kansakar, Lakesh, additional, Zhang, Shanshan, additional, Vucetic, Slobodan, additional, Wang, Zheng, additional, Sternberg, Michael J. E., additional, Wass, Mark N., additional, Huntley, Rachael P., additional, Martin, Maria J., additional, O’Donovan, Claire, additional, Robinson, Peter N., additional, Moreau, Yves, additional, Tramontano, Anna, additional, Babbitt, Patricia C., additional, Brenner, Steven E., additional, Linial, Michal, additional, Orengo, Christine A., additional, Rost, Burkhard, additional, Greene, Casey S., additional, Mooney, Sean D., additional, Friedberg, Iddo, additional, and Radivojac, Predrag, additional

Published
2016
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16. An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

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, Jiang, Yuxiang, Oron, Tal Ronnen, Clark, Wyatt T., Bankapur, Asma R., D’Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Kahanda, Indika, Verspoor, Karin M., Ben-Hur, Asa, Koo, Da Chen Emily, Penfold-Brown, Duncan, Shasha, Denni, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Cao, Renzhi, Zhong, Zhaolong, Cheng, Jianlin, Altenhoff, Adrian, Skunca, Nive, Dessimoz, Christophe, Dogan, Tunca, Hakala, Kai, Kaewphan, Suwisa, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Chen, Ching-Tai, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T., Chapman, Samuel, Bkc, Dukka, Khan, Ishita K., Kihara, Daisuke, Ofer, Dan, Rappoport, Nadav, Stern, Amo, Cibrian-Uhalte, Elena, Denny, Paul, Foulger, Rebecca E., Hieta, Reija, Legge, Duncan, Lovering, Ruth C., Magrane, Michele, Melidoni, Anna N., Mutowo-Meullenet, Prudence, Pichler, Klemen, Shypitsyna, Aleksandra, Li, Biao, Zakeri, Pooya, Elshal, Sarah, Tranchevent, Léon-Charle, Das, Sayoni, Dawson, Natalie L., Lee, David, Lees, Jonathan G., Sillitoe, Ian, Bhat, Prajwal, Nepusz, Tamá, Romero, Alfonso E., Sasidharan, Rajkumar, Yang, Haixuan, Paccanaro, Alberto, Gillis, Jesse, Sedeño-Cortés, Adriana E., Pavlidis, Paul, Feng, Shou, Cejuela, Juan M., Goldberg, Tatyana, Hamp, Tobia, Richter, Lothar, Salamov, Asaf, Gabaldon, Toni, Marcet-Houben, Marina, Supek, Fran, Gong, Qingtian, Ning, Wei, Zhou, Yuanpeng, Tian, Weidong, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio C.E., del Pozo, Angela, Fernández, José M., Maietta, Paolo, Valencia, Alfonso, Tress, Michael L., Benso, Alfredo, Di Carlo, Stefano, Politano, Gianfranco, Savino, Alessandro, Rehman, Hafeez Ur, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W., van Dijk, Aalt D. J., Gemovic, Branislava, Glisic, Sanja, Perovic, Vladmir, Veljkovic, Veljko, Veljkovic, Nevena, Almeida-e-Silva, Danillo C., Vencio, Ricardo Z. N., Sharan, Malvika, Vogel, Jörg, Kansakar, Lakesh, Zhang, Shanshan, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael J. E., Wass, Mark N., Huntley, Rachael P., Martin, Maria J., O’Donovan, Claire, Robinson, Peter N., Moreau, Yve, Tramontano, Anna, Babbitt, Patricia C., Brenner, Steven E., Linial, Michal, Orengo, Christine A., Rost, Burkhard, Greene, Casey S., Mooney, Sean D., Friedberg, Iddo, Radivojac, Predrag, Friedberg, Iddo [0000-0002-1789-8000], Apollo - University of Cambridge Repository, (ukupan broj autora: 147), Biotechnology and Biological Sciences Research Council (BBSRC), National Science Foundation (Estados Unidos), United States of Department of Health & Human Services, National Natural Science Foundation of China, Natural Sciences and Engineering Research Council (Canadá), São Paulo Research Foundation, Ministerio de Economía y Competitividad (España), Biotechnology and Biological Sciences Research Council (Reino Unido), Katholieke Universiteit Leuven (Bélgica), Newton International Fellowship Scheme of the Royal Society grant, British Heart Foundation, Ministry of Education, Science and Technological Development (Serbia), Office of Biological and Environmental Research (Estados Unidos), Australian Research Council, University of Padua (Italia), Swiss National Science Foundation, Institute of Biotechnology, Computational genomics, and Bioinformatics

Subjects
0301 basic medicine, Computer science, Disease gene prioritization, Protein function prediction, Ecology, Evolution, Behavior and Systematics, Genetics, Cell Biology, 05 Environmental Sciences, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit, computer.software_genre, Quantitative Biology - Quantitative Methods, Wiskundige en Statistische Methoden - Biometris, Field (computer science), Laboratorium voor Plantenveredeling, Function (engineering), Databases, Protein, 1183 Plant biology, microbiology, virology, Quantitative Methods (q-bio.QM), media_common, Genetics & Heredity, Settore BIO/11 - BIOLOGIA MOLECOLARE, Ecology, SISTA, 1184 Genetics, developmental biology, physiology, Life Sciences & Biomedicine, Algorithms, Bioinformatics, Evolution, media_common.quotation_subject, BIOINFORMÁTICA, Machine learning, Bottleneck, Set (abstract data type), BIOS Applied Bioinformatics, 03 medical and health sciences, Annotation, Structure-Activity Relationship, Behavior and Systematics, Human Phenotype Ontology, Humans, ddc:610, DISINTEGRIN, Mathematical and Statistical Methods - Biometris, BIOINFORMATICS, 08 Information And Computing Sciences, Science & Technology, business.industry, Research, ADAM, Proteins, Computational Biology, Molecular Sequence Annotation, 06 Biological Sciences, Data set, ONTOLOGY, Plant Breeding, 030104 developmental biology, Gene Ontology, Biotechnology & Applied Microbiology, FOS: Biological sciences, Artificial intelligence, business, computer, Software
Abstract

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. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. CONCLUSIONS: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent., We acknowledge the contributions of Maximilian Hecht, Alexander Grün, Julia Krumhoff, My Nguyen Ly, Jonathan Boidol, Rene Schoeffel, Yann Spöri, Jessika Binder, Christoph Hamm and Karolina Worf. This work was partially supported by the following grants: National Science Foundation grants DBI-1458477 (PR), DBI-1458443 (SDM), DBI-1458390 (CSG), DBI-1458359 (IF), IIS-1319551 (DK), DBI-1262189 (DK), and DBI-1149224 (JC); National Institutes of Health grants R01GM093123 (JC), R01GM097528 (DK), R01GM076990 (PP), R01GM071749 (SEB), R01LM009722 (SDM), and UL1TR000423 (SDM); the National Natural Science Foundation of China grants 3147124 (WT) and 91231116 (WT); the National Basic Research Program of China grant 2012CB316505 (WT); NSERC grant RGPIN 371348-11 (PP); FP7 infrastructure project TransPLANT Award 283496 (ADJvD); Microsoft Research/FAPESP grant 2009/53161-6 and FAPESP fellowship 2010/50491-1 (DCAeS); Biotechnology and Biological Sciences Research Council grants BB/L020505/1 (DTJ), BB/F020481/1 (MJES), BB/K004131/1 (AP), BB/F00964X/1 (AP), and BB/L018241/1 (CD); the Spanish Ministry of Economics and Competitiveness grant BIO2012-40205 (MT); KU Leuven CoE PFV/10/016 SymBioSys (YM); the Newton International Fellowship Scheme of the Royal Society grant NF080750 (TN). CSG was supported in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative grant GBMF4552. Computational resources were provided by CSC – IT Center for Science Ltd., Espoo, Finland (TS). This work was supported by the Academy of Finland (TS). RCL and ANM were supported by British Heart Foundation grant RG/13/5/30112. PD, RCL, and REF were supported by Parkinson’s UK grant G-1307, the Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, and the Ministry of Education, Science and Technological Development of the Republic of Serbia grant 173001. This work was a Technology Development effort for ENIGMA – Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, which is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research grant DE-AC02-05CH11231. ENIGMA only covers the application of this work to microbial proteins. NSF DBI-0965616 and Australian Research Council grant DP150101550 (KMV). NSF DBI-0965768 (ABH). NIH T15 LM00945102 (training grant for CSF). FP7 FET grant MAESTRA ICT-2013-612944 and FP7 REGPOT grant InnoMol (FS). NIH R01 GM60595 (PCB). University of Padova grants CPDA138081/13 (ST) and GRIC13AAI9 (EL). Swiss National Science Foundation grant 150654 and UK BBSRC grant BB/M015009/1 (COD). PRB2 IPT13/0001 - ISCIII-SGEFI / FEDER (JMF)., This is the final version of the article. It first appeared from BioMed Central at http://dx.doi.org/10.1186/s13059-016-1037-6.

Published
2016

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