Search

Your search keyword '"Marco Notaro"' showing total 25 results
Start Over Author "Marco Notaro"
25 results on '"Marco Notaro"'

1. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Naihui Zhou, Yuxiang Jiang, Timothy R. Bergquist, Alexandra J. Lee, Balint Z. Kacsoh, Alex W. Crocker, Kimberley A. Lewis, George Georghiou, Huy N. Nguyen, Md Nafiz Hamid, Larry Davis, Tunca Dogan, Volkan Atalay, Ahmet S. Rifaioglu, Alperen Dalkıran, Rengul Cetin Atalay, Chengxin Zhang, Rebecca L. Hurto, Peter L. Freddolino, Yang Zhang, Prajwal Bhat, Fran Supek, José M. Fernández, Branislava Gemovic, Vladimir R. Perovic, Radoslav S. Davidović, Neven Sumonja, Nevena Veljkovic, Ehsaneddin Asgari, Mohammad R.K. Mofrad, Giuseppe Profiti, Castrense Savojardo, Pier Luigi Martelli, Rita Casadio, Florian Boecker, Heiko Schoof, Indika Kahanda, Natalie Thurlby, Alice C. McHardy, Alexandre Renaux, Rabie Saidi, Julian Gough, Alex A. Freitas, Magdalena Antczak, Fabio Fabris, Mark N. Wass, Jie Hou, Jianlin Cheng, Zheng Wang, Alfonso E. Romero, Alberto Paccanaro, Haixuan Yang, Tatyana Goldberg, Chenguang Zhao, Liisa Holm, Petri Törönen, Alan J. Medlar, Elaine Zosa, Itamar Borukhov, Ilya Novikov, Angela Wilkins, Olivier Lichtarge, Po-Han Chi, Wei-Cheng Tseng, Michal Linial, Peter W. Rose, Christophe Dessimoz, Vedrana Vidulin, Saso Dzeroski, Ian Sillitoe, Sayoni Das, Jonathan Gill Lees, David T. Jones, Cen Wan, Domenico Cozzetto, Rui Fa, Mateo Torres, Alex Warwick Vesztrocy, Jose Manuel Rodriguez, Michael L. Tress, Marco Frasca, Marco Notaro, Giuliano Grossi, Alessandro Petrini, Matteo Re, Giorgio Valentini, Marco Mesiti, Daniel B. Roche, Jonas Reeb, David W. Ritchie, Sabeur Aridhi, Seyed Ziaeddin Alborzi, Marie-Dominique Devignes, Da Chen Emily Koo, Richard Bonneau, Vladimir Gligorijević, Meet Barot, Hai Fang, Stefano Toppo, Enrico Lavezzo, Marco Falda, Michele Berselli, Silvio C.E. Tosatto, Marco Carraro, Damiano Piovesan, Hafeez Ur Rehman, Qizhong Mao, Shanshan Zhang, Slobodan Vucetic, Gage S. Black, Dane Jo, Erica Suh, Jonathan B. Dayton, Dallas J. Larsen, Ashton R. Omdahl, Liam J. McGuffin, Danielle A. Brackenridge, Patricia C. Babbitt, Jeffrey M. Yunes, Paolo Fontana, Feng Zhang, Shanfeng Zhu, Ronghui You, Zihan Zhang, Suyang Dai, Shuwei Yao, Weidong Tian, Renzhi Cao, Caleb Chandler, Miguel Amezola, Devon Johnson, Jia-Ming Chang, Wen-Hung Liao, Yi-Wei Liu, Stefano Pascarelli, Yotam Frank, Robert Hoehndorf, Maxat Kulmanov, Imane Boudellioua, Gianfranco Politano, Stefano Di Carlo, Alfredo Benso, Kai Hakala, Filip Ginter, Farrokh Mehryary, Suwisa Kaewphan, Jari Björne, Hans Moen, Martti E.E. Tolvanen, Tapio Salakoski, Daisuke Kihara, Aashish Jain, Tomislav Šmuc, Adrian Altenhoff, Asa Ben-Hur, Burkhard Rost, Steven E. Brenner, Christine A. Orengo, Constance J. Jeffery, Giovanni Bosco, Deborah A. Hogan, Maria J. Martin, Claire O’Donovan, Sean D. Mooney, Casey S. Greene, Predrag Radivojac, and Iddo Friedberg

Subjects
Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Published
2019
Full Text
View/download PDF

2. UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction

Author

Paolo Perlasca, Marco Frasca, Cheick Tidiane Ba, Marco Notaro, Alessandro Petrini, Elena Casiraghi, Giuliano Grossi, Jessica Gliozzo, Giorgio Valentini, and Marco Mesiti

Subjects
Imbalance-aware protein function prediction, Imbalance-aware protein networks integration, Visualization of protein networks, Web service for protein function and network integration, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate —in a function-specific fashion— the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships. Results We address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks —e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks. Conclusions UNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.

Published
2019
Full Text
View/download PDF

3. A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks

Author

Marco Frasca, Giuliano Grossi, Jessica Gliozzo, Marco Mesiti, Marco Notaro, Paolo Perlasca, Alessandro Petrini, and Giorgio Valentini

Subjects
GPU-based Hopfield nets, Large-sized networks, Protein function prediction, Biological networks, Node label prediction, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Several problems in network biology and medicine can be cast into a framework where entities are represented through partially labeled networks, and the aim is inferring the labels (usually binary) of the unlabeled part. Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalanced, that is one class is largely under-represented: for instance in the automated protein function prediction (AFP) for most Gene Ontology terms only few proteins are annotated, or in the disease-gene prioritization problem only few genes are actually known to be involved in the etiology of a given disease. Imbalance-aware approaches to accurately predict node labels in biological networks are thereby required. Furthermore, such methods must be scalable, since input data can be large-sized as, for instance, in the context of multi-species protein networks. Results We propose a novel semi-supervised parallel enhancement of COSNet, an imbalance-aware algorithm build on Hopfield neural model recently suggested to solve the AFP problem. By adopting an efficient representation of the graph and assuming a sparse network topology, we empirically show that it can be efficiently applied to networks with millions of nodes. The key strategy to speed up the computations is to partition nodes into independent sets so as to process each set in parallel by exploiting the power of GPU accelerators. This parallel technique ensures the convergence to asymptotically stable attractors, while preserving the asynchronous dynamics of the original model. Detailed experiments on real data and artificial big instances of the problem highlight scalability and efficiency of the proposed method. Conclusions By parallelizing COSNet we achieved on average a speed-up of 180x in solving the AFP problem in the S. cerevisiae, Mus musculus and Homo sapiens organisms, while lowering memory requirements. In addition, to show the potential applicability of the method to huge biomolecular networks, we predicted node labels in artificially generated sparse networks involving hundreds of thousands to millions of nodes.

Published
2018
Full Text
View/download PDF

4. Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods

Author

Marco Notaro, Max Schubach, Peter N. Robinson, and Giorgio Valentini

Subjects
Human Phenotype Ontology, Hierarchical multi-label classification, Hierarchical ensemble methods, Gene-Abnormal phenotype association, Human Phenotype Ontology term prediction, Phenotype gene prioritization, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background The prediction of human gene–abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene–disease associations has been widely investigated, the related problem of gene–phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. Results We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a “flat” learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Conclusions Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.

Published
2017
Full Text
View/download PDF

5. Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools.

Author

Paolo Perlasca, Marco Frasca, Cheick Tidiane Ba, Jessica Gliozzo, Marco Notaro, Mario Pennacchioni, Giorgio Valentini, and Marco Mesiti

Subjects
Medicine, Science
Abstract

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.

Published
2020
Full Text
View/download PDF

9. Tell Bennet, Christopher Chute, Peter DeWitt, Kenneth Gersing, Andrew Girvin, Melissa Haendel, Jeremy Harper, Janos Hajagos, Stephanie Hong, Emily Pfaff, Jane Reusch, Corneliu Antoniescu, Kimberly Robaski: A Methodological Framework for the Comparative Evaluation of Multiple Imputation Methods: Multiple Imputation of Race, Ethnicity and Body Mass Index in the U.S. National COVID Cohort Collaborative.

Author

Elena Casiraghi, Rachel Wong, Margaret Hall, Ben D. Coleman, Marco Notaro, Michael D. Evans, Jena S. Tronieri, Hannah Blau, Bryan Laraway, Tiffany J. Callahan, Lauren E. Chan, Carolyn T. Bramante, John B. Buse, Richard A. Moffitt, Til Sturmer, Steven G. Johnson, Yu Raymond Shao, Justin T. Reese, Peter N. Robinson, Alberto Paccanaro, Giorgio Valentini, Jared D. Huling, and Kenneth Wilkins

Published
2022
Full Text
View/download PDF

11. A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Author

Elena Casiraghi, Rachel Wong, Margaret Hall, Ben D. Coleman, Marco Notaro, Michael D. Evans, Jena S. Tronieri, Hannah Blau, Bryan Laraway, Tiffany J. Callahan, Lauren E. Chan, Carolyn T. Bramante, John B. Buse, Richard A. Moffitt, Til Sturmer, Steven G. Johnson, Yu Raymond Shao, Justin T. Reese, Peter N. Robinson, Alberto Paccanaro, Giorgio Valentini, Jared D. Huling, and Kenneth Wilkins

Published
2023
Full Text
View/download PDF

17. HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve Gene Ontology term prediction

Author

Giorgio Valentini, Peter N. Robinson, Alessandro Petrini, Jessica Gliozzo, Marco Frasca, Marco Notaro, and Elena Casiraghi

Subjects
Statistics and Probability, Computer science, Ontology (information science), Machine learning, computer.software_genre, Biochemistry, 03 medical and health sciences, 0302 clinical medicine, Ensembles of classifiers, Controlled vocabulary, Protein function prediction, Molecular Biology, 030304 developmental biology, 0303 health sciences, business.industry, Modular design, Directed acyclic graph, Ensemble learning, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, 030220 oncology & carcinogenesis, Scalability, Artificial intelligence, business, computer
Abstract

Motivation Automated protein function prediction is a complex multi-class, multi-label, structured classification problem in which protein functions are organized in a controlled vocabulary, according to the Gene Ontology (GO). ‘Hierarchy-unaware’ classifiers, also known as ‘flat’ methods, predict GO terms without exploiting the inherent structure of the ontology, potentially violating the True-Path-Rule (TPR) that governs the GO, while ‘hierarchy-aware’ approaches, even if they obey the TPR, do not always show clear improvements with respect to flat methods, or do not scale well when applied to the full GO. Results To overcome these limitations, we propose Hierarchical Ensemble Methods for Directed Acyclic Graphs (HEMDAG), a family of highly modular hierarchical ensembles of classifiers, able to build upon any flat method and to provide ‘TPR-safe’ predictions, by leveraging a combination of isotonic regression and TPR learning strategies. Extensive experiments on synthetic and real data across several organisms firstly show that HEMDAG can be used as a general tool to improve the predictions of flat classifiers, and secondly that HEMDAG is competitive versus state-of-the-art hierarchy-aware learning methods proposed in the last CAFA international challenges. Availability and implementation Fully tested R code freely available at https://anaconda.org/bioconda/r-hemdag. Tutorial and documentation at https://hemdag.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2021

18. Author response for 'FZD6 triggers Wnt–signalling driven by WNT10B IVS1 expression and highlights new targets in T cell acute lymphoblastic leukemia'

Author

Marco Notaro, Alessandra Trojani, Gianluigi Reda, Adriana Cassaro, Roberto Cairoli, Ilaria Esposito, Jessica Gliozzo, Barbara Di Camillo, Giovanni Grillo, Alessandro Beghini, and Giorgio Valentini

Subjects
medicine.anatomical_structure, Lymphoblastic Leukemia, T cell, Cancer research, medicine, Wnt signalling, Biology
Published
2021
Full Text
View/download PDF

19. Multi-resolution visualization and analysis of biomolecular networks through hierarchical community detection and web-based graphical tools

Author

Cheick Tidiane Ba, Marco Mesiti, Paolo Perlasca, Marco Notaro, Jessica Gliozzo, Mario Pennacchioni, Marco Frasca, and Giorgio Valentini

Subjects
Proteomics, Computer science, computer.software_genre, Biochemistry, Infographics, Rendering (computer graphics), Macromolecular Structure Analysis, Medicine and Health Sciences, Protein function prediction, Computer Networks, Data Management, Multidisciplinary, Applied Mathematics, Simulation and Modeling, Gene Ontologies, Genomics, Graph, Oncology, Physical Sciences, Medicine, The Internet, Protein Interaction Networks, Data mining, Graphs, Algorithms, Metabolic Networks and Pathways, Network Analysis, Research Article, Computer and Information Sciences, Protein Structure, Science, Research and Analysis Methods, Genetics, Web application, Humans, Molecular Biology, Internet, business.industry, SARS-CoV-2, Data Visualization, COVID-19, Proteins, Biology and Life Sciences, Computational Biology, Cancers and Neoplasms, Genome Analysis, Visualization, business, Protein Structure Networks, computer, Mathematics
Abstract

The visual exploration and analysis of biomolecular networks is of paramount importance for identifying hidden and complex interaction patterns among proteins. Although many tools have been proposed for this task, they are mainly focused on the query and visualization of a single protein with its neighborhood. The global exploration of the entire network and the interpretation of its underlying structure still remains difficult, mainly due to the excessively large size of the biomolecular networks. In this paper we propose a novel multi-resolution representation and exploration approach that exploits hierarchical community detection algorithms for the identification of communities occurring in biomolecular networks. The proposed graphical rendering combines two types of nodes (protein and communities) and three types of edges (protein-protein, community-community, protein-community), and displays communities at different resolutions, allowing the user to interactively zoom in and out from different levels of the hierarchy. Links among communities are shown in terms of relationships and functional correlations among the biomolecules they contain. This form of navigation can be also combined by the user with a vertex centric visualization for identifying the communities holding a target biomolecule. Since communities gather limited-size groups of correlated proteins, the visualization and exploration of complex and large networks becomes feasible on off-the-shelf computer machines. The proposed graphical exploration strategies have been implemented and integrated in UNIPred-Web, a web application that we recently introduced for combining the UNIPred algorithm, able to address both integration and protein function prediction in an imbalance-aware fashion, with an easy to use vertex-centric exploration of the integrated network. The tool has been deeply amended from different standpoints, including the prediction core algorithm. Several tests on networks of different size and connectivity have been conducted to show off the vast potential of our methodology; moreover, enrichment analyses have been performed to assess the biological meaningfulness of detected communities. Finally, a CoV-human network has been embedded in the system, and a corresponding case study presented, including the visualization and the prediction of human host proteins that potentially interact with SARS-CoV2 proteins.

Published
2020

20. FZD6 triggers Wnt–signalling driven by WNT10BIVS1 expression and highlights new targets in T-cell acute lymphoblastic leukemia

Author

Adriana, C, Giovanni, G, Marco, N, Jessica, G, Ilaria, E, Gianluigi, R, Alessandra, T, Giorgio, V, Barbara Di, C, Cairoli, R, Alessandro, B, Adriana Cassaro, Giovanni Grillo, Marco Notaro, Jessica Gliozzo, Ilaria Esposito, Gianluigi Reda, Alessandra Trojani, Giorgio Valentini, Barbara Di Camillo, Cairoli Roberto, Alessandro Beghini, Adriana, C, Giovanni, G, Marco, N, Jessica, G, Ilaria, E, Gianluigi, R, Alessandra, T, Giorgio, V, Barbara Di, C, Cairoli, R, Alessandro, B, Adriana Cassaro, Giovanni Grillo, Marco Notaro, Jessica Gliozzo, Ilaria Esposito, Gianluigi Reda, Alessandra Trojani, Giorgio Valentini, Barbara Di Camillo, Cairoli Roberto, and Alessandro Beghini

Abstract

Wnt/Fzd signaling has been implicated in hematopoietic stem cell maintenance and in acute leukemia establishment. In our previous work, we described a recurrent rearrangement involving the WNT10B locus (WNT10BR), characterized by the expression of WNT10BIVS1 transcript variant, in acute myeloid leukemia. To determine the occurrence of WNT10BR in T-cell acute lymphoblastic leukemia (T-ALL), we retrospectively analyzed an Italian cohort of patients (n = 20) and detected a high incidence (13/20) of WNT10BIVS1 expression. To address genes involved in WNT10B molecular response, we have designed a Wnt-targeted RNA sequencing panel. Identifying Wnt agonists and antagonists, it results that the expression of FZD6, LRP5, and PROM1 genes stands out in WNT10BIVS1 positive patients compared to negative ones. Using MOLT4 and MUTZ-2 as leukemic cell models, which are characterized by the expression of WNT10BIVS1, we have observed that WNT10B drives major Wnt activation to the FZD6 receptor complex through receipt of ligand. Additionally, short hairpin RNAs (shRNAs)-mediated gene silencing and small molecule-mediated inhibition of WNTs secretion have been observed to interfere with the WNT10B/FZD6 interaction. We have therefore identified that WNT10BIVS1 knockdown, or pharmacological interference by the LGK974 porcupine (PORCN) inhibitor, reduces WNT10B/FZD6 protein complex formation and significantly impairs intracellular effectors and leukemic expansion. These results describe the molecular circuit induced by WNT10B and suggest WNT10B/FZD6 as a new target in the T-ALL treatment strategy.

Published
2021

21. DNA binding modes influence Rap1 activity in the regulation of telomere length and MRX functions at DNA ends

Author

Jacopo Vertemara, Renata Tisi, Diego Bonetti, Marco Notaro, Giuseppe Zampella, Paolo Pizzul, Carlo Rinaldi, Maria Pia Longhese, Bonetti, D, Rinaldi, C, Vertemara, J, Notaro, M, Pizzul, P, Tisi, R, Zampella, G, and Longhese, M

Subjects
endocrine system, Saccharomyces cerevisiae Proteins, Transcription, Genetic, DNA damage, Telomere-Binding Proteins, Mutant, Saccharomyces cerevisiae, Genome Integrity, Repair and Replication, Biology, medicine.disease_cause, Models, Biological, Shelterin Complex, Turn (biochemistry), chemistry.chemical_compound, Genetics, medicine, DNA Breaks, Double-Stranded, DSB, telomere, genome stability, Rap1, MRX, DNA, Fungal, Alleles, Mutation, fungi, Telomere Homeostasis, Telomere, Cell biology, enzymes and coenzymes (carbohydrates), chemistry, Multiprotein Complexes, Rap1, Function (biology), DNA, DNA Damage, Protein Binding, Transcription Factors
Abstract

The cellular response to DNA double-strand breaks (DSBs) is initiated by the Mre11–Rad50–Xrs2 (MRX) complex that has structural and catalytic functions. MRX association at DSBs is counteracted by Rif2, which is known to interact with Rap1 that binds telomeric DNA through two tandem Myb-like domains. Whether and how Rap1 acts at DSBs is unknown. Here we show that Rif2 inhibits MRX association to DSBs in a manner dependent on Rap1, which binds to DSBs and promotes Rif2 association to them. Rap1 in turn can negatively regulate MRX function at DNA ends also independently of Rif2. In fact, a characterization of Rap1 mutant variants shows that Rap1 binding to DNA through both Myb-like domains results in formation of Rap1-DNA complexes that control MRX functions at both DSBs and telomeres primarily through Rif2. By contrast, Rap1 binding to DNA through a single Myb-like domain results in formation of high stoichiometry complexes that act at DNA ends mostly in a Rif2-independent manner. Altogether these findings indicate that the DNA binding modes of Rap1 influence its functional properties, thus highlighting the structural plasticity of this protein.

Published
2020

22. UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction

Author

Alessandro Petrini, Giuliano Grossi, Elena Casiraghi, Marco Frasca, Marco Notaro, Paolo Perlasca, Marco Mesiti, Cheick Tidiane Ba, Jessica Gliozzo, and Giorgio Valentini

Subjects
Computer science, lcsh:Computer applications to medicine. Medical informatics, Biochemistry, 03 medical and health sciences, User-Computer Interface, 0302 clinical medicine, Protein Annotation, Structural Biology, Human–computer interaction, Protein function prediction, Protein Interaction Maps, Molecular Biology, lcsh:QH301-705.5, 030304 developmental biology, 0303 health sciences, Protein function, Internet, Applied Mathematics, Web service for protein function and network integration, Computational Biology, Proteins, Computer Science Applications, Visualization, lcsh:Biology (General), 030220 oncology & carcinogenesis, Imbalance-aware protein networks integration, Visualization of protein networks, lcsh:R858-859.7, DNA microarray, Protein network, Imbalance-aware protein function prediction, Software, Algorithms
Abstract

Background One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate —in a function-specific fashion— the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships. Results We address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks —e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks. Conclusions UNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.

Published
2019

23. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Renzhi Cao, Alice C. McHardy, Cen Wan, Jonathan G. Lees, Vedrana Vidulin, Alex Warwick Vesztrocy, Huy N Nguyen, Devon Johnson, Ian Sillitoe, Alessandro Petrini, Richard Bonneau, Hans Moen, Peter L. Freddolino, Rui Fa, Alfredo Benso, Jianlin Cheng, Indika Kahanda, Qizhong Mao, Zihan Zhang, Chenguang Zhao, Rebecca L. Hurto, Predrag Radivojac, Stefano Di Carlo, Sayoni Das, Suwisa Kaewphan, Sabeur Aridhi, Alan Medlar, Casey S. Greene, Constance J. Jeffery, Christophe Dessimoz, Jose Manuel Rodriguez, Gianfranco Politano, Michele Berselli, Jia-Ming Chang, Deborah A. Hogan, Julian Gough, Tunca Doğan, David T. Jones, Claire O'Donovan, Volkan Atalay, Paolo Fontana, Feng Zhang, Shuwei Yao, Robert Hoehndorf, Olivier Lichtarge, Alex W. Crocker, Ahmet Sureyya Rifaioglu, Rabie Saidi, Farrokh Mehryary, Neven Sumonja, Yang Zhang, Florian Boecker, Jie Hou, Christine A. Orengo, Matteo Re, Natalie Thurlby, Chengxin Zhang, Stefano Pascarelli, Alberto Paccanaro, Hafeez Ur Rehman, Yuxiang Jiang, Mohammad R. K. Mofrad, Naihui Zhou, Asa Ben-Hur, Steven E. Brenner, Martti Tolvanen, Filip Ginter, Mark N. Wass, Patricia C. Babbitt, David W. Ritchie, George Georghiou, Stefano Toppo, Caleb Chandler, Larry Davis, Da Chen Emily Koo, Itamar Borukhov, Petri Törönen, Rengul Cetin-Atalay, Fabio Fabris, Haixuan Yang, Kai Hakala, Silvio C. E. Tosatto, Domenico Cozzetto, Slobodan Vucetic, Balint Z. Kacsoh, Luke W Sagers, Alex A. Freitas, Tapio Salakoski, Fran Supek, Alfonso E. Romero, Angela D. Wilkins, Elaine Zosa, Shanshan Zhang, Yotam Frank, Jonathan B. Dayton, Jeffrey M. Yunes, Pier Luigi Martelli, Dallas J. Larsen, Giuliano Grossi, Alexandra J. Lee, Marco Mesiti, Yi-Wei Liu, Jonas Reeb, Damiano Piovesan, Sean D. Mooney, Magdalena Antczak, Erica Suh, Marco Falda, Marie-Dominique Devignes, Castrense Savojardo, Zheng Wang, Danielle A Brackenridge, Peter W. Rose, Enrico Lavezzo, Dane Jo, Ronghui You, Tomislav Šmuc, Liam J. McGuffin, Michael L. Tress, Ilya Novikov, Adrian M. Altenhoff, Burkhard Rost, Miguel Amezola, Mateo Torres, Prajwal Bhat, Wen-Hung Liao, Meet Barot, Marco Notaro, Suyang Dai, Giorgio Valentini, Jari Björne, Nevena Veljkovic, Wei-Cheng Tseng, Po-Han Chi, Alperen Dalkiran, Maxat Kulmanov, Nafiz Hamid, Aashish Jain, Branislava Gemovic, Alexandre Renaux, Ashton Omdahl, Daniel B. Roche, Vladimir Perovic, Iddo Friedberg, Daisuke Kihara, Giovanni Bosco, Gage S. Black, Saso Dzeroski, Liisa Holm, Marco Frasca, Michal Linial, Ehsaneddin Asgari, Tatyana Goldberg, Maria Jesus Martin, Vladimir Gligorijević, Marco Carraro, Shanfeng Zhu, Radoslav Davidovic, Timothy Bergquist, Hai Fang, José M. Fernández, Giuseppe Profiti, Weidong Tian, Imane Boudellioua, Kimberley A. Lewis, Seyed Ziaeddin Alborzi, and Rita Casadio

Subjects
0303 health sciences, Protein function, biology, Computer science, 030302 biochemistry & molecular biology, Pseudomonas, Computational biology, Biological process, biology.organism_classification, Genome, 3. Good health, 03 medical and health sciences, Molecular function, Cellular component, Mutation screening, Critical assessment, Protein function prediction, Gene, Function (biology), 030304 developmental biology
Abstract

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Published
2019
Full Text
View/download PDF

24. The Cafa Challenge Reports Improved Protein Function Prediction And New Functional Annotations For Hundreds Of Genes Through Experimental Screens

Author

Heiko Schoof, Ahmet Sureyya Rifaioglu, Ian Sillitoe, Shanfeng Zhu, Marco Carraro, Naihui Zhou, Asa Ben-Hur, Rui Fa, Alice C. McHardy, David W. Ritchie, George Georghiou, Filip Ginter, Haixuan Yang, Alex A. Freitas, Constance J. Jeffery, Tapio Salakoski, Radoslav Davidovic, Huy N Nguyen, Devon Johnson, Yotam Frank, Alexandra J. Lee, Sean D. Mooney, Marco Falda, Marie-Dominique Devignes, Gianfranco Politano, David T. Jones, Silvio C. E. Tosatto, Renzhi Cao, Zihan Zhang, Sabeur Aridhi, Stefano Pascarelli, Vedrana Vidulin, Qizhong Mao, Balint Z. Kacsoh, Patricia C. Babbitt, Giovanni Bosco, Farrokh Mehryary, Florian Boecker, Alfonso E. Romero, Angela D. Wilkins, Saso Dzeroski, Richard Bonneau, Hans Moen, Chengxin Zhang, Prajwal Bhat, Giuliano Grossi, Martti Tolvanen, Matteo Re, Meet Barot, Mohammad R. K. Mofrad, Predrag Radivojac, Stefano Di Carlo, Tatyana Goldberg, Branislava Gemovic, Suyang Dai, Pier Luigi Martelli, Giorgio Valentini, Maxat Kulmanov, Maria Jesus Martin, Claire O'Donovan, Dallas J. Larsen, Alexandre Renaux, Alan Medlar, Jeffrey M. Yunes, Erica Suh, Volkan Atalay, Vladimir Gligorijević, Fran Supek, Elaine Zosa, Wei-Cheng Tseng, Nafiz Hamid, Marco Mesiti, Tunca Doğan, Petri Törönen, Hafeez Ur Rehman, Jose Manuel Rodriguez, Alessandro Petrini, Sayoni Das, Burkhard Rost, Miguel Amezola, Mateo Torres, Jianlin Cheng, Daisuke Kihara, Liisa Holm, Marco Frasca, Steven E. Brenner, Stefano Toppo, Adrian M. Altenhoff, Chenguang Zhao, Daniel B. Roche, Alperen Dalkiran, Alex W. Crocker, Marco Notaro, Iddo Friedberg, Michal Linial, Julian Gough, Damiano Piovesan, Slobodan Vucetic, Natalie Thurlby, Olivier Lichtarge, Jari Björne, Jonas Reeb, Rabie Saidi, Yuxiang Jiang, Christophe Dessimoz, Jie Hou, Ronghui You, Tomislav Šmuc, Paolo Fontana, Michele Berselli, Jia-Ming Chang, Deborah A. Hogan, Larry Davis, Ehsaneddin Asgari, Shuwei Yao, Zheng Wang, Fabio Fabris, Michael L. Tress, Caleb Chandler, Christine A. Orengo, Rengul Cetin Atalay, Castrense Savojardo, Danielle A Brackenridge, Peter W. Rose, Yang Zhang, Dane Jo, Gage S. Black, Shanshan Zhang, Aashish Jain, Liam J. McGuffin, Timothy Bergquist, Peter L. Freddolino, Robert Hoehndorf, Rita Casadio, Da Chen Emily Koo, Mark N. Wass, Hai Fang, Casey S. Greene, Suwisa Kaewphan, Magdalena Antczak, Wen-Hung Liao, Enrico Lavezzo, Neven Sumonja, Ashton Omdahl, José M. Fernández, Ilya Novikov, Jonathan B. Dayton, Feng Zhang, Vladimir Perovic, Cen Wan, Jonathan G. Lees, Kai Hakala, Weidong Tian, Alex Warwick Vesztrocy, Domenico Cozzetto, Nevena Veljkovic, Yi-Wei Liu, Imane Boudellioua, Po-Han Chi, Kimberley A. Lewis, Seyed Ziaeddin Alborzi, Giuseppe Profiti, Alberto Paccanaro, Itamar Borukhov, Alfredo Benso, Indika Kahanda, Rebecca L. Hurto, Bilgisayar Mühendisliği, National Science Foundation (United States), Gordon and Betty Moore Foundation, United States of Department of Health & Human Services, Cystic Fibrosis Foundation, Consejo Nacional de Ciencia y Tecnología (México), Deutsche Forschungsgemeinschaft (Alemania), European Research Council, Ministerio de Ciencia e Innovación (España), Unión Europea, University of Turku (Finlandia), Finlands Akademi (Finlandia), National Natural Science Foundation of China, Nanjing Agricultural University. The Academy of Science. National Key Research & Development Program of China, Ministero dell Istruzione, dell Universita e della Ricerca (Italia), Shanghai Municipal Science and Technology Major Project, Biotechnology and Biological Sciences Research Council (Reino Unido), Extreme Science and Engineering Discovery Environment, Ministry of Education, Science and Technological Development (Serbia), Ministry of Science and Technology, Ministry for Education (Baviera) (Alemania), Yad Hanadiv, University of Milan (Italia), Swiss National Science Foundation, Unión Europea. European Cooperation in Science and Technology (COST), Plataforma ISCIII de Bioinformática (España), Scientific and Technological Research Council of Turkey, Ministry of Education (China), University of Padua (Italia), Mühendislik ve Doğa Bilimleri Fakültesi -- Bilgisayar Mühendisliği Bölümü, Rifaioğlu, Ahmet Süreyya, Zhou N., Jiang Y., Bergquist T.R., Lee A.J., Kacsoh B.Z., Crocker A.W., Lewis K.A., Georghiou G., Nguyen H.N., Hamid M.N., Davis L., Dogan T., Atalay V., Rifaioglu A.S., Dalklran A., Cetin Atalay R., Zhang C., Hurto R.L., Freddolino P.L., Zhang Y., Bhat P., Supek F., Fernandez J.M., Gemovic B., Perovic V.R., Davidovic R.S., Sumonja N., Veljkovic N., Asgari E., Mofrad M.R.K., Profiti G., Savojardo C., Martelli P.L., Casadio R., Boecker F., Schoof H., Kahanda I., Thurlby N., McHardy A.C., Renaux A., Saidi R., Gough J., Freitas A.A., Antczak M., Fabris F., Wass M.N., Hou J., Cheng J., Wang Z., Romero A.E., Paccanaro A., Yang H., Goldberg T., Zhao C., Holm L., Toronen P., Medlar A.J., Zosa E., Borukhov I., Novikov I., Wilkins A., Lichtarge O., Chi P.-H., Tseng W.-C., Linial M., Rose P.W., Dessimoz C., Vidulin V., Dzeroski S., Sillitoe I., Das S., Lees J.G., Jones D.T., Wan C., Cozzetto D., Fa R., Torres M., Warwick Vesztrocy A., Rodriguez J.M., Tress M.L., Frasca M., Notaro M., Grossi G., Petrini A., Re M., Valentini G., Mesiti M., Roche D.B., Reeb J., Ritchie D.W., Aridhi S., Alborzi S.Z., Devignes M.-D., Koo D.C.E., Bonneau R., Gligorijevic V., Barot M., Fang H., Toppo S., Lavezzo E., Falda M., Berselli M., Tosatto S.C.E., Carraro M., Piovesan D., Ur Rehman H., Mao Q., Zhang S., Vucetic S., Black G.S., Jo D., Suh E., Dayton J.B., Larsen D.J., Omdahl A.R., McGuffin L.J., Brackenridge D.A., Babbitt P.C., Yunes J.M., Fontana P., Zhang F., Zhu S., You R., Zhang Z., Dai S., Yao S., Tian W., Cao R., Chandler C., Amezola M., Johnson D., Chang J.-M., Liao W.-H., Liu Y.-W., Pascarelli S., Frank Y., Hoehndorf R., Kulmanov M., Boudellioua I., Politano G., Di Carlo S., Benso A., Hakala K., Ginter F., Mehryary F., Kaewphan S., Bjorne J., Moen H., Tolvanen M.E.E., Salakoski T., Kihara D., Jain A., Smuc T., Altenhoff A., Ben-Hur A., Rost B., Brenner S.E., Orengo C.A., Jeffery C.J., Bosco G., Hogan D.A., Martin M.J., O'Donovan C., Mooney S.D., Greene C.S., Radivojac P., Friedberg I., Faculty of Economic and Social Sciences and Solvay Business School, Faculty of Sciences and Bioengineering Sciences, Faculty of Engineering, Computational genomics, Institute of Biotechnology, Bioinformatics, Genetics, Helsinki Institute of Life Science HiLIFE, Discovery Research Group/Prof. Hannu Toivonen, Iowa State University (ISU), European Bioinformatics Institute, École Polytechnique de Montréal (EPM), Vinča Institute of Nuclear Sciences, University of Belgrade [Belgrade], University of Bologna, Max Planck Institute for Plant Breeding Research (MPIPZ), European Virus Bioinformatics Center [Jena], Université libre de Bruxelles (ULB), Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes (LIMOS), SIGMA Clermont (SIGMA Clermont)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Department of Computer Science, University of Bristol [Bristol], Department of Computer Science [Columbia], University of Missouri [Columbia] (Mizzou), University of Missouri System-University of Missouri System, Yale School of Public Health (YSPH), Departamento de Geometría y Topología, Universidad de Granada (UGR), Tumor Biology Center, Centre for Nephrology [London, UK], University College of London [London] (UCL), Baylor College of Medicine (BCM), Baylor University, Department of Knowledge Technologies, Structural and Molecular Biology Department, University College London, Queen Mary University of London (QMUL), Spanish National Cancer Research Center (CNIO), Dipartimento di Informatica, Università degli Studi di Milano [Milano] (UNIMI), Dipartimento di Scienze dell'Informazione [Milano], United States Naval Academy, Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Department of Molecular Medicine, Universita degli Studi di Padova, Centro de Regulación Genómica (CRG), Universitat Pompeu Fabra [Barcelona] (UPF), Physics Department, National Tsing Hua University [Hsinchu] (NTHU), Dipartimento di Automatica e Informatica [Torino] (DAUIN), Politecnico di Torino = Polytechnic of Turin (Polito), University of Turku, Bioinformatics Laboratory, University of Turku-Turku Center for Computer Science, Toyota Technological Institute at Chicago [Chicago] (TTIC), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne (UNIL), Department of Computer Science [Colorado State University], Colorado State University [Fort Collins] (CSU), Centre for Plant Integrative Biology [Nothingham] (CPIB), University of Nottingham, UK (UON), BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany., University of Bologna/Università di Bologna, Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Université d'Auvergne - Clermont-Ferrand I (UdA)-SIGMA Clermont (SIGMA Clermont)-Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Centre National de la Recherche Scientifique (CNRS), Universidad de Granada = University of Granada (UGR), Università degli Studi di Milano = University of Milan (UNIMI), Università degli Studi di Padova = University of Padua (Unipd), and Université de Lausanne = University of Lausanne (UNIL)

Subjects
Library, Male, Identification, Candida-albicans, Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Procedures, Genome, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0302 clinical medicine, Candida albicans, Molecular genetics, lcsh:QH301-705.5, ComputingMilieux_MISCELLANEOUS, Biological ontology, Settore BIO/11 - BIOLOGIA MOLECOLARE, 0303 health sciences, 318 Medical biotechnology, Biotechnology & applied microbiology, Ontology, Expectation, Genetics & heredity, Plant leaf, ddc, 3. Good health, Drosophila melanogaster, Human experiment, Fungal genome, Pseudomonas aeruginosa, Female, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Genome, Fungal, BIOINFORMATICS, Long-Term memory, Locomotion, Human, Adult, Memory, Long-Term, lcsh:QH426-470, Bioinformatics, Long term memory, Generation, Bacterial genome, Computational biology, Biology, Article, 03 medical and health sciences, Annotation, Big data, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Pseudomonas, Genetics, Animals, Humans, Gene, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Animal, Research, Experimental data, Molecular Sequence Annotation, Cell Biology, Nonhuman, Human genetics, lcsh:Genetics, lcsh:Biology (General), Biofilms, Proteins | Genes | Protein functions, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 030217 neurology & neurosurgery, Function (biology), Genome, Bacterial
Abstract

Tosatto, Silvio/0000-0003-4525-7793; Zhang, Feng/0000-0003-3447-897X; Gonzalez, Jose Maria Fernandez/0000-0002-4806-5140; Devignes, Marie-Dominique/0000-0002-0399-8713; Wass, Mark/0000-0001-5428-6479; Falda, Marco/0000-0003-2642-519X; Thurlby, Natalie/0000-0002-1007-0286; Zosa, Elaine/0000-0003-2482-0663; Dessimoz, Christophe/0000-0002-2170-853X; Yunes, Jeffrey/0000-0003-1869-3231; Hamid, Md Nafiz/0000-0001-8681-6526; Hoehndorf, Robert/0000-0001-8149-5890; Dogan, Tunca/0000-0002-1298-9763; NOTARO, MARCO/0000-0003-4309-2200; Cozzetto, Domenico/0000-0001-6752-5432; Lewis, Kimberley/0000-0003-3010-8453; Roche, Daniel/0000-0002-9204-1840; Martin, Maria-Jesus/0000-0001-5454-2815; Tress, Michael/0000-0001-9046-6370; Tolvanen, Martti/0000-0003-3434-7646; Cheng, Jianlin/0000-0003-0305-2853; Rose, Peter/0000-0001-9981-9750; Renaux, Alexandre/0000-0002-4339-2791; Kacsoh, Balint/0000-0001-9171-0611; O'Donovan, Claire/0000-0001-8051-7429; Kulmanov, Maxat/0000-0003-1710-1820; Friedberg, Iddo/0000-0002-1789-8000; Zhou, Naihui/0000-0001-6268-6149, WOS: 000498615000001, PubMed ID: 31744546, Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens., National Science FoundationNational Science Foundation (NSF) [DBI1564756, DBI-1458359, DBI-1458390, DMS1614777, CMMI1825941, NSF 1458390]; Gordon and Betty Moore FoundationGordon and Betty Moore Foundation [GBMF 4552]; National Institutes of Health NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P20 GM113132]; Cystic Fibrosis Foundation [CFRDP STANTO19R0]; BBSRCBiotechnology and Biological Sciences Research Council (BBSRC) [BB/K004131/1, BB/F00964X/1, BB/M025047/1, BB/M015009/1]; Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT)Consejo Nacional de Ciencia y Tecnologia (CONACyT) [14-INV-088, PINV15-315]; NSFNational Science Foundation (NSF) [1660648, DBI 1759934, IIS1763246, DBI-1458477, 0965768, DMR-1420073, DBI-1458443]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM093123, DP1MH110234, UL1 TR002319, U24 TR002306]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155 "RESIST"German Research Foundation (DFG) [39087428]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM123055, R01GM60595, R15GM120650, GM083107, GM116960, AI134678, NIH R35-GM128637, R00-GM097033]; ERCEuropean Research Council (ERC) [StG 757700]; Spanish Ministry of Science, Innovation and Universities [BFU2017-89833-P]; Severo Ochoa award; Centre of Excellence project "BioProspecting of Adriatic Sea"; Croatian Government; European Regional Development FundEuropean Union (EU) [KK.01.1.1.01.0002]; ATT Tieto kayttoon grant; Academy of FinlandAcademy of Finland; University of Turku; CSC-IT Center for Science Ltd.; University of Miami; National Cancer Institute of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [U01CA198942]; Helsinki Institute for Life Sciences; Academy of FinlandAcademy of Finland [292589]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31671367, 31471245, 91631301, 61872094, 61572139]; National Key Research and Development Program of China [2016YFC1000505, 2017YFC0908402]; Italian Ministry of Education, University and Research (MIUR) PRIN 2017 projectMinistry of Education, Universities and Research (MIUR) [2017483NH8]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 2018SHZDZX01]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019431/1, BB/L020505/1, BB/L002817/1]; Elsevier; Extreme Science and Engineering Discovery Environment (XSEDE) award [MCB160101, MCB160124]; Ministry of Education, Science and Technological Development of the Republic of Serbia [173001]; Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2]; Montana State University; Bavarian Ministry for Education; Simons Foundation; NIH NINDSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [1R21NS103831-01]; University of Illinois at Chicago (UIC) Cancer Center award; UIC College of Liberal Arts and Sciences Faculty Award; UIC International Development Award; Yad Hanadiv [9660/2019]; National Institute of General Medical Science of the National Institute of Health [GM066099, GM079656]; Research Supporting Plan (PSR) of University of Milan [PSR2018-DIP-010-MFRAS]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [150654]; EMBL-European Bioinformatics Institute core funds; CAFA BBSRC [BB/N004876/1]; European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grantEuropean Union (EU) [778247]; COST ActionEuropean Cooperation in Science and Technology (COST) [BM1405]; NIH/NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R01 GM071749]; National Human Genome Research Institute of the National of Health [U41 HG007234]; INB Grant (ISCIII-SGEFI/ERDF) [PT17/0009/0001]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [EEEAG-116E930]; KanSil [2016K121540]; Universita degli Studi di Milano; 111 ProjectMinistry of Education, China - 111 Project [B18015]; key project of Shanghai Science Technology [16JC1420402]; ZJLab; project Ribes Network POR-FESR 3S4H [TOPP-ALFREVE18-01]; PRID/SID of University of Padova [TOPP-SID19-01]; NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R15GM120650]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/3454-01-01, URF/1/3790-01-01]; "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education; National Center for High-performance ComputingIstanbul Technical University, The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124.; The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443.

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results