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

Author

Zhou, Naihui, Jiang, Yuxiang, Bergquist, Timothy R, Lee, Alexandra J, Kacsoh, Balint Z, Crocker, Alex W, Lewis, Kimberley A, Georghiou, George, Nguyen, Huy N, Hamid, Md Nafiz, Davis, Larry, Dogan, Tunca, Atalay, Volkan, Rifaioglu, Ahmet S, Dalkıran, Alperen, Cetin Atalay, Rengul, Zhang, Chengxin, Hurto, Rebecca L, Freddolino, Peter L, Zhang, Yang, Bhat, Prajwal, Supek, Fran, Fernández, José M, Gemovic, Branislava, Perovic, Vladimir R, Davidović, Radoslav S, Sumonja, Neven, Veljkovic, Nevena, Asgari, Ehsaneddin, Mofrad, Mohammad RK, Profiti, Giuseppe, Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Boecker, Florian, Schoof, Heiko, Kahanda, Indika, Thurlby, Natalie, McHardy, Alice C, Renaux, Alexandre, Saidi, Rabie, Gough, Julian, Freitas, Alex A, Antczak, Magdalena, Fabris, Fabio, Wass, Mark N, Hou, Jie, Cheng, Jianlin, Wang, Zheng, Romero, Alfonso E, Paccanaro, Alberto, Yang, Haixuan, Goldberg, Tatyana, Zhao, Chenguang, Holm, Liisa, Törönen, Petri, Medlar, Alan J, Zosa, Elaine, Borukhov, Itamar, Novikov, Ilya, Wilkins, Angela, Lichtarge, Olivier, Chi, Po-Han, Tseng, Wei-Cheng, Linial, Michal, Rose, Peter W, Dessimoz, Christophe, Vidulin, Vedrana, Dzeroski, Saso, Sillitoe, Ian, Das, Sayoni, Lees, Jonathan Gill, Jones, David T, Wan, Cen, Cozzetto, Domenico, Fa, Rui, Torres, Mateo, Warwick Vesztrocy, Alex, Rodriguez, Jose Manuel, Tress, Michael L, Frasca, Marco, Notaro, Marco, Grossi, Giuliano, Petrini, Alessandro, Re, Matteo, Valentini, Giorgio, Mesiti, Marco, Roche, Daniel B, Reeb, Jonas, Ritchie, David W, Aridhi, Sabeur, Alborzi, Seyed Ziaeddin, Devignes, Marie-Dominique, Koo, Da Chen Emily, Bonneau, Richard, Gligorijević, Vladimir, Barot, Meet, Fang, Hai, Toppo, Stefano, and Lavezzo, Enrico

Subjects
Human Genome, Networking and Information Technology R&D (NITRD), Genetics, Generic health relevance, Animals, Biofilms, Candida albicans, Drosophila melanogaster, Genome, Bacterial, Genome, Fungal, Humans, Locomotion, Memory, Long-Term, Molecular Sequence Annotation, Pseudomonas aeruginosa, Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, 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.ConclusionWe 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

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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7. Whole Genome Sequence Analysis of Brucella abortus Isolates from Various Regions of South Africa

Author

Ledwaba, Maphuti Betty, primary, Glover, Barbara Akorfa, additional, Matle, Itumeleng, additional, Profiti, Giuseppe, additional, Martelli, Pier Luigi, additional, Casadio, Rita, additional, Zilli, Katiuscia, additional, Janowicz, Anna, additional, Marotta, Francesca, additional, Garofolo, Giuliano, additional, and van Heerden, Henriette, additional

Published
2021
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9. MOESM1 of The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Naihui Zhou, Yuxiang Jiang, Bergquist, Timothy, Lee, Alexandra, Balint Kacsoh, Crocker, Alex, Lewis, Kimberley, Georghiou, George, Nguyen, Huy, Md Nafiz Hamid, Davis, Larry, Tunca Dogan, Atalay, Volkan, Rifaioglu, Ahmet, Dalkıran, Alperen, Rengul Cetin Atalay, Chengxin Zhang, Hurto, Rebecca, Freddolino, Peter, Zhang, Yang, Prajwal Bhat, Supek, Fran, Fernández, José, Gemovic, Branislava, Perovic, Vladimir, Davidović, Radoslav, Sumonja, Neven, Veljkovic, Nevena, Ehsaneddin Asgari, Mofrad, Mohammad, Profiti, Giuseppe, Castrense Savojardo, Martelli, Pier Luigi, Casadio, Rita, Boecker, Florian, Schoof, Heiko, Indika Kahanda, Thurlby, Natalie, McHardy, Alice, Renaux, Alexandre, Saidi, Rabie, Gough, Julian, Freitas, Alex, Antczak, Magdalena, Fabris, Fabio, Wass, Mark, Hou, Jie, Jianlin Cheng, Wang, Zheng, Romero, Alfonso, Paccanaro, Alberto, Haixuan Yang, Goldberg, Tatyana, Chenguang Zhao, Holm, Liisa, Törönen, Petri, Medlar, Alan, Zosa, Elaine, Borukhov, Itamar, Novikov, Ilya, Wilkins, Angela, Lichtarge, Olivier, Po-Han Chi, Tseng, Wei-Cheng, Linial, Michal, Rose, Peter, Dessimoz, Christophe, Vidulin, Vedrana, Saso Dzeroski, Sillitoe, Ian, Sayoni Das, Lees, Jonathan Gill, Jones, David, Wan, Cen, Cozzetto, Domenico, Fa, Rui, Torres, Mateo, Vesztrocy, Alex Warwick, Rodriguez, Jose Manuel, Tress, Michael, Frasca, Marco, Notaro, Marco, Grossi, Giuliano, Petrini, Alessandro, Re, Matteo, Valentini, Giorgio, Mesiti, Marco, Roche, Daniel, Reeb, Jonas, Ritchie, David, Sabeur Aridhi, Alborzi, Seyed Ziaeddin, Marie-Dominique Devignes, Koo, Da Chen Emily, Bonneau, Richard, Gligorijević, Vladimir, Meet Barot, Fang, Hai, Toppo, Stefano, Lavezzo, Enrico, Falda, Marco, Berselli, Michele, Tosatto, Silvio, Carraro, Marco, Piovesan, Damiano, Hafeez Ur Rehman, Qizhong Mao, Shanshan Zhang, Vucetic, Slobodan, Black, Gage, Jo, Dane, Suh, Erica, Dayton, Jonathan, Larsen, Dallas, Omdahl, Ashton, McGuffin, Liam, Brackenridge, Danielle, Babbitt, Patricia, Yunes, Jeffrey, Fontana, Paolo, Zhang, Feng, Shanfeng Zhu, Ronghui You, Zihan Zhang, Suyang Dai, Shuwei Yao, Weidong Tian, Renzhi Cao, Chandler, Caleb, Amezola, Miguel, Johnson, Devon, Chang, Jia-Ming, Wen-Hung Liao, Liu, Yi-Wei, Pascarelli, Stefano, Yotam Frank, Hoehndorf, Robert, Kulmanov, Maxat, Boudellioua, Imane, Politano, Gianfranco, Carlo, Stefano Di, Benso, Alfredo, Hakala, Kai, Ginter, Filip, Mehryary, Farrokh, Suwisa Kaewphan, Björne, Jari, Moen, Hans, Tolvanen, Martti, Salakoski, Tapio, Kihara, Daisuke, Aashish Jain, Šmuc, Tomislav, Altenhoff, Adrian, Ben-Hur, Asa, Rost, Burkhard, Brenner, Steven, Orengo, Christine, Jeffery, Constance, Bosco, Giovanni, Hogan, Deborah, Martin, Maria, O’Donovan, Claire, Mooney, Sean, Greene, Casey, Radivojac, Predrag, and Friedberg, Iddo

Subjects
Pharmacology, FOS: Biological sciences, Data_FILES, Genetics, Biochemistry, Molecular Biology, 69999 Biological Sciences not elsewhere classified, Developmental Biology
Abstract

Additional file 1 Additional figures and tables referenced in the article.

Published
2019
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10. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

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

Published
2019
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14. Additional file 5: Figure S3. of Ancient pathogen-driven adaptation triggers increased susceptibility to non-celiac wheat sensitivity in present-day European populations

Author

Sazzini, Marco, Fanti, Sara De, Cherubini, Anna, Quagliariello, Andrea, Profiti, Giuseppe, Martelli, Pier, Casadio, Rita, Ricci, Chiara, Campieri, Massimo, Lanzini, Alberto, Volta, Umberto, Caio, Giacomo, Franceschi, Claudio, Spisni, Enzo, and Luiselli, Donata

Abstract

Admixture-like plot displaying membership probabilities for NCWS and continental population clusters computed by DAPC. Probabilities belonging to the EAS, EUR, and AFR clusters are, respectively, displayed in green, red, and blue. (PDF 74Â kb)

Published
2016
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15. Additional file 1: Figure S1. of Ancient pathogen-driven adaptation triggers increased susceptibility to non-celiac wheat sensitivity in present-day European populations

Author

Sazzini, Marco, Fanti, Sara De, Cherubini, Anna, Quagliariello, Andrea, Profiti, Giuseppe, Martelli, Pier, Casadio, Rita, Ricci, Chiara, Campieri, Massimo, Lanzini, Alberto, Volta, Umberto, Caio, Giacomo, Franceschi, Claudio, Spisni, Enzo, and Luiselli, Donata

Abstract

Flowchart describing schematic representation of the implemented research approach detailing steps for NCWS sample selection and applied population genetics analytical workflow. (PDF 24Â kb)

Published
2016
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16. Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Author

Yuxiang Jiang, Oron, Tal Ronnen, Clark, Wyatt T., Bankapur, Asma R., D’Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Indika Kahanda, Verspoor, Karin M., Ben-Hur, Asa, Koo, Da Chen Emily, Penfold-Brown, Duncan, Shasha, Dennis, Youngs, Noah, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, Casadio, Rita, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Altenhoff, Adrian, Skunca, Nives, Dessimoz, Christophe, Tunca Dogan, Hakala, Kai, Suwisa Kaewphan, Mehryary, Farrokh, Salakoski, Tapio, Ginter, Filip, Fang, Hai, Smithers, Ben, Oates, Matt, Gough, Julian, Törönen, Petri, Koskinen, Patrik, Holm, Liisa, Ching-Tai Chen, Hsu, Wen-Lian, Bryson, Kevin, Cozzetto, Domenico, Minneci, Federico, Jones, David T., Chapman, Samuel, Dukka BKC, Ishita K. Khan, 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, Pooya Zakeri, ElShal, Sarah, Léon-Charles Tranchevent, Sayoni Das, Dawson, Natalie L., Lee, David, Lees, Jonathan G., Sillitoe, Ian, Prajwal Bhat, Nepusz, Tamás, Romero, Alfonso E., Sasidharan, Rajkumar, Haixuan Yang, 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, Qingtian Gong, Ning, Wei, Yuanpeng Zhou, Weidong Tian, Falda, Marco, Fontana, Paolo, Lavezzo, Enrico, Toppo, Stefano, Ferrari, Carlo, Giollo, Manuel, Piovesan, Damiano, Tosatto, Silvio C.E., Pozo, Angela Del, Fernández, José M., Maietta, Paolo, Valencia, Alfonso, Tress, Michael L., Benso, Alfredo, Carlo, Stefano Di, Politano, Gianfranco, Savino, Alessandro, Hafeez Ur Rehman, Re, Matteo, Mesiti, Marco, Valentini, Giorgio, Bargsten, Joachim W., Dijk, Aalt D. J. Van, Gemovic, Branislava, Glisic, Sanja, Vladmir Perovic, Veljkovic, Veljko, Veljkovic, Nevena, Danillo C. Almeida-E-Silva, Vencio, Ricardo Z. N., Malvika Sharan, Vogel, Jörg, Lakesh Kansakar, Shanshan Zhang, Vucetic, Slobodan, Wang, Zheng, Sternberg, Michael J. E., 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

Abstract

A document containing a subset of CAFA2 analyses that are equivalent to those provided about the CAFA1 experiment in the CAFA1 supplement. (PDF 11100 kb)

Published
2016
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19. Graph algorithms for bioinformatics

Author

Profiti, Giuseppe <1980>

Subjects
INF/01 Informatica, Computer Science::Databases
Abstract

Biological data are inherently interconnected: protein sequences are connected to their annotations, the annotations are structured into ontologies, and so on. While protein-protein interactions are already represented by graphs, in this work I am presenting how a graph structure can be used to enrich the annotation of protein sequences thanks to algorithms that analyze the graph topology. We also describe a novel solution to restrict the data generation needed for building such a graph, thanks to constraints on the data and dynamic programming. The proposed algorithm ideally improves the generation time by a factor of 5. The graph representation is then exploited to build a comprehensive database, thanks to the rising technology of graph databases. While graph databases are widely used for other kind of data, from Twitter tweets to recommendation systems, their application to bioinformatics is new. A graph database is proposed, with a structure that can be easily expanded and queried.

Published
2015

21. Tools and data services registry: a community effort to document bioinformatics resources

Author

Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thomas, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Hans, Ioannidis, Vassilios, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chris, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C.E., de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, Brunak, Søren, Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thomas, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Hans, Ioannidis, Vassilios, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chris, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C.E., de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, and Brunak, Søren

Abstract

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.

Published
2016

22. Tools and data services registry:a community effort to document bioinformatics resources

Author

Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thomas, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Hans-Ioan, Ioannidis, Vassilios, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chris, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C. E., de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, Brunak, Søren, Ison, Jon, Rapacki, Kristoffer, Ménager, Hervé, Kalaš, Matúš, Rydza, Emil, Chmura, Piotr, Anthon, Christian, Beard, Niall, Berka, Karel, Bolser, Dan, Booth, Tim, Bretaudeau, Anthony, Brezovsky, Jan, Casadio, Rita, Cesareni, Gianni, Coppens, Frederik, Cornell, Michael, Cuccuru, Gianmauro, Davidsen, Kristian, Vedova, Gianluca Della, Dogan, Tunca, Doppelt-Azeroual, Olivia, Emery, Laura, Gasteiger, Elisabeth, Gatter, Thomas, Goldberg, Tatyana, Grosjean, Marie, Grüning, Björn, Helmer-Citterich, Manuela, Ienasescu, Hans-Ioan, Ioannidis, Vassilios, Jespersen, Martin Closter, Jimenez, Rafael, Juty, Nick, Juvan, Peter, Koch, Maximilian, Laibe, Camille, Li, Jing-Woei, Licata, Luana, Mareuil, Fabien, Mičetić, Ivan, Friborg, Rune Møllegaard, Moretti, Sebastien, Morris, Chris, Möller, Steffen, Nenadic, Aleksandra, Peterson, Hedi, Profiti, Giuseppe, Rice, Peter, Romano, Paolo, Roncaglia, Paola, Saidi, Rabie, Schafferhans, Andrea, Schwämmle, Veit, Smith, Callum, Sperotto, Maria Maddalena, Stockinger, Heinz, Vařeková, Radka Svobodová, Tosatto, Silvio C. E., de la Torre, Victor, Uva, Paolo, Via, Allegra, Yachdav, Guy, Zambelli, Federico, Vriend, Gert, Rost, Burkhard, Parkinson, Helen, Løngreen, Peter, and Brunak, Søren

Abstract

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.

Published
2016

23. 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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24. Ancient pathogen-driven adaptation triggers increased susceptibility to non-celiac wheat sensitivity in present-day European populations

Author

Sazzini, Marco, primary, De Fanti, Sara, additional, Cherubini, Anna, additional, Quagliariello, Andrea, additional, Profiti, Giuseppe, additional, Martelli, Pier Luigi, additional, Casadio, Rita, additional, Ricci, Chiara, additional, Campieri, Massimo, additional, Lanzini, Alberto, additional, Volta, Umberto, additional, Caio, Giacomo, additional, Franceschi, Claudio, additional, Spisni, Enzo, additional, and Luiselli, Donata, additional

Published
2016
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25. Graph algorithms for bioinformatics

Author

Casadio, Rita, Profiti, Giuseppe <1980>, Casadio, Rita, and Profiti, Giuseppe <1980>

Abstract

Biological data are inherently interconnected: protein sequences are connected to their annotations, the annotations are structured into ontologies, and so on. While protein-protein interactions are already represented by graphs, in this work I am presenting how a graph structure can be used to enrich the annotation of protein sequences thanks to algorithms that analyze the graph topology. We also describe a novel solution to restrict the data generation needed for building such a graph, thanks to constraints on the data and dynamic programming. The proposed algorithm ideally improves the generation time by a factor of 5. The graph representation is then exploited to build a comprehensive database, thanks to the rising technology of graph databases. While graph databases are widely used for other kind of data, from Twitter tweets to recommendation systems, their application to bioinformatics is new. A graph database is proposed, with a structure that can be easily expanded and queried.

Published
2015

26. Tools and data services registry: a community effort to document bioinformatics resources

Author

Ison, Jon, primary, Rapacki, Kristoffer, additional, Ménager, Hervé, additional, Kalaš, Matúš, additional, Rydza, Emil, additional, Chmura, Piotr, additional, Anthon, Christian, additional, Beard, Niall, additional, Berka, Karel, additional, Bolser, Dan, additional, Booth, Tim, additional, Bretaudeau, Anthony, additional, Brezovsky, Jan, additional, Casadio, Rita, additional, Cesareni, Gianni, additional, Coppens, Frederik, additional, Cornell, Michael, additional, Cuccuru, Gianmauro, additional, Davidsen, Kristian, additional, Vedova, Gianluca Della, additional, Dogan, Tunca, additional, Doppelt-Azeroual, Olivia, additional, Emery, Laura, additional, Gasteiger, Elisabeth, additional, Gatter, Thomas, additional, Goldberg, Tatyana, additional, Grosjean, Marie, additional, Grüning, Björn, additional, Helmer-Citterich, Manuela, additional, Ienasescu, Hans, additional, Ioannidis, Vassilios, additional, Jespersen, Martin Closter, additional, Jimenez, Rafael, additional, Juty, Nick, additional, Juvan, Peter, additional, Koch, Maximilian, additional, Laibe, Camille, additional, Li, Jing-Woei, additional, Licata, Luana, additional, Mareuil, Fabien, additional, Mičetić, Ivan, additional, Friborg, Rune Møllegaard, additional, Moretti, Sebastien, additional, Morris, Chris, additional, Möller, Steffen, additional, Nenadic, Aleksandra, additional, Peterson, Hedi, additional, Profiti, Giuseppe, additional, Rice, Peter, additional, Romano, Paolo, additional, Roncaglia, Paola, additional, Saidi, Rabie, additional, Schafferhans, Andrea, additional, Schwämmle, Veit, additional, Smith, Callum, additional, Sperotto, Maria Maddalena, additional, Stockinger, Heinz, additional, Vařeková, Radka Svobodová, additional, Tosatto, Silvio C.E., additional, de la Torre, Victor, additional, Uva, Paolo, additional, Via, Allegra, additional, Yachdav, Guy, additional, Zambelli, Federico, additional, Vriend, Gert, additional, Rost, Burkhard, additional, Parkinson, Helen, additional, Løngreen, Peter, additional, and Brunak, Søren, additional

Published
2015
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29. An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

Author

Yuxiang Jiang, Tal Ronnen Oron, Clark, Wyatt T., Bankapur, Asma R., D'Andrea, Daniel, Lepore, Rosalba, Funk, Christopher S., Kahanda, Indika, Verspoor, Karin M., Asa Ben-Hur, Da Chen Emily Koo, Penfold-Brown, Duncan, Shasha, Dennis, Noah Youngs, Bonneau, Richard, Lin, Alexandra, Sahraeian, Sayed M. E., Martelli, Pier Luigi, Profiti, Giuseppe, and Casadio, Rita

Published
2016
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30. Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome

Author

Giuseppe Profiti, Diana Giannuzzi, Ludovica Montanucci, Emidio Capriotti, Ivan Rossi, Luca Aresu, Piero Fariselli, Capriotti, Emidio, Montanucci, Ludovica, Profiti, Giuseppe, Rossi, Ivan, Giannuzzi, Diana, Aresu, Luca, and Fariselli, Piero

Subjects
genomic variants, variant interpretation, dog genome, machine learning, Genotype, Genomics, Single-nucleotide polymorphism, Genome-wide association study, Computational biology, Biology, Genome, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, Dogs, Genetics, SNP, Animals, 030304 developmental biology, 0303 health sciences, Internet, Genetic Variation, Matthews correlation coefficient, Binary classification, Web Server Issue, Human genome, 030217 neurology & neurosurgery, Algorithms, Software, Genome-Wide Association Study
Abstract

As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve.

Published
2019

31. BUSCA: an integrative web server to predict subcellular localization of proteins

Author

Pier Luigi Martelli, Giuseppe Profiti, Piero Fariselli, Castrense Savojardo, Rita Casadio, Savojardo, Castrense, Martelli, Pier Luigi, Fariselli, Piero, Profiti, Giuseppe, and Casadio, Rita

Subjects
0106 biological sciences, 0301 basic medicine, Web server, Chloroplasts, Protein sequence analysis, Gene Expression, Protein subcellular localization prediction, protein sequence analysis, machine learning, Computational biology, Biology, Protein Sorting Signals, computer.software_genre, 01 natural sciences, Bacterial protein, Mitochondrial Proteins, 03 medical and health sciences, Annotation, Genetics, Cell Nucleus, Internet, Bacteria, Cell Membrane, Eukaryota, Membrane Proteins, Molecular Sequence Annotation, Subcellular localization, Protein subcellular localization prediction, Mitochondria, Transmembrane domain, Benchmarking, 030104 developmental biology, Eukaryotic Cells, Gene Ontology, Membrane protein, Prokaryotic Cells, Web Server Issue, computer, Software, 010606 plant biology & botany
Abstract

Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization.

Published
2018

32. The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation

Author

Giuseppe Profiti, Pier Luigi Martelli, Rita Casadio, Profiti, Giuseppe, Martelli, Pier Luigi, and Casadio, Rita

Subjects
0301 basic medicine, protein structure rpediction, Bar (music), Biology, Bioinformatics, 03 medical and health sciences, Annotation, Similarity (network science), Sequence Analysis, Protein, Genetics, Cluster Analysis, protein interaction, Cluster analysis, Hidden Markov model, Sequence, Internet, Information retrieval, Proteins, Molecular Sequence Annotation, sequence similarity, 030104 developmental biology, Web Server Issue, Graph (abstract data type), UniProt, Software, protein function prediction, clustering
Abstract

BAR 3.0 updates our server BAR (Bologna Annotation Resource) for predicting protein structural and functional features from sequence. We increase data volume, query capabilities and information conveyed to the user. The core of BAR 3.0 is a graph-based clustering procedure of UniProtKB sequences, following strict pairwise similarity criteria (sequence identity ≥40% with alignment coverage ≥90%). Each cluster contains the available annotation downloaded from UniProtKB, GO, PFAM and PDB. After statistical validation, GO terms and PFAM domains are cluster-specific and annotate new sequences entering the cluster after satisfying similarity constraints. BAR 3.0 includes 28 869 663 sequences in 1 361 773 clusters, of which 22.2% (22 241 661 sequences) and 47.4% (24 555 055 sequences) have at least one validated GO term and one PFAM domain, respectively. 1.4% of the clusters (36% of all sequences) include PDB structures and the cluster is associated to a hidden Markov model that allows building template-target alignment suitable for structural modeling. Some other 3 399 026 sequences are singletons. BAR 3.0 offers an improved search interface, allowing queries by UniProtKB-accession, Fasta sequence, GO-term, PFAM-domain, organism, PDB and ligand/s. When evaluated on the CAFA2 targets, BAR 3.0 largely outperforms our previous version and scores among state-of-the-art methods. BAR 3.0 is publicly available and accessible at http://bar.biocomp.unibo.it/bar3.

Published
2017

33. eDGAR: a database of Disease-Gene Associations with annotated Relationships among genes

Author

Giulia Babbi, Rita Casadio, Samuele Bovo, Giuseppe Profiti, Castrense Savojardo, Pier Luigi Martelli, Babbi, Giulia, Martelli, Pier Luigi, Profiti, Giuseppe, Bovo, Samuele, Savojardo, Castrense, and Casadio, Rita

Subjects
0301 basic medicine, lcsh:QH426-470, lcsh:Biotechnology, Genomics, Biology, Protein functional annotation, computer.software_genre, 03 medical and health sciences, Annotation, 0302 clinical medicine, Protein-protein interaction, lcsh:TP248.13-248.65, Databases, Genetic, Genetics, Humans, Protein Interaction Maps, KEGG, Gene, Gene/disease relationship, Database, Research, Genetic Diseases, Inborn, Molecular Sequence Annotation, Phenotypic trait, lcsh:Genetics, 030104 developmental biology, DNA microarray, Functional enrichment, computer, Functional genomics, 030217 neurology & neurosurgery, Metabolic Networks and Pathways, Biotechnology
Abstract

Background Genetic investigations, boosted by modern sequencing techniques, allow dissecting the genetic component of different phenotypic traits. These efforts result in the compilation of lists of genes related to diseases and show that an increasing number of diseases is associated with multiple genes. Investigating functional relations among genes associated with the same disease contributes to highlighting molecular mechanisms of the pathogenesis. Results We present eDGAR, a database collecting and organizing the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar. For each disease-associated gene, eDGAR collects information on its annotation. Specifically, for lists of genes, eDGAR provides information on: i) interactions retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG and REACTOME pathways; v) enriched functional annotations computed with NET-GE; vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes and/or co-localisation in neighboring loci. The present release of eDGAR includes 2672 diseases, related to 3658 different genes, for a total number of 5729 gene-disease associations. 71% of the genes are linked to 621 multigenic diseases and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical and regulatory interactions. eDGAR includes a network based enrichment method for detecting statistically significant functional terms associated to groups of genes. Conclusions eDGAR offers a resource to analyze disease-gene associations. In multigenic diseases genes can share physical interactions and/or co-occurrence in the same functional processes. eDGAR is freely available at: edgar.biocomp.unibo.it Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3911-3) contains supplementary material, which is available to authorized users.

Published
2017

34. Ancient pathogen-driven adaptation triggers increased susceptibility to non-celiac wheat sensitivity in present-day European populations

Author

Rita Casadio, Pier Luigi Martelli, Chiara Ricci, Enzo Spisni, Claudio Franceschi, Anna Cherubini, Umberto Volta, Sara De Fanti, Giuseppe Profiti, Giacomo Caio, Marco Sazzini, Massimo Campieri, Andrea Quagliariello, Alberto Lanzini, Donata Luiselli, Sazzini, Marco, De Fanti, Sara, Cherubini, Anna, Quagliariello, Andrea, Profiti, Giuseppe, Martelli, Pier Luigi, Casadio, Rita, Ricci, Chiara, Campieri, Massimo, Lanzini, Alberto, Volta, Umberto, Caio, Giacomo, Franceschi, Claudio, Spisni, Enzo, Luiselli, Donata, ARAG - AREA FINANZA E PARTECIPATE, CENTRO INTERDIPARTIMENTALE ALMA MATER RESEARCH INSTITUTE ON GLOBAL CHALLENGES AND CLIMATE CHANGE (ALMA CLIMATE), DIPARTIMENTO DI BENI CULTURALI, DIPARTIMENTO DI FARMACIA E BIOTECNOLOGIE, DIPARTIMENTO DI SCIENZE BIOLOGICHE, GEOLOGICHE E AMBIENTALI, DIPARTIMENTO DI SCIENZE MEDICHE E CHIRURGICHE, Facolta' di MEDICINA e CHIRURGIA, AREA MIN. 05 - Scienze biologiche, Da definire, and AREA MIN. 06 - Scienze mediche

Subjects
0301 basic medicine, Non-celiac wheat sensitivity, Endocrinology, Diabetes and Metabolism, Natural selection, Biology, Balancing selection, NO, Human dietary shifts, 03 medical and health sciences, 0302 clinical medicine, Genetics, Allele, Human dietary shift, Human evolutionary genetics, Research, Haplotype, Non-celiac wheat sensitivity, Human dietary shifts, Human adaptation, Natural selection, Evolutionary medicine, Evolutionary medicine, Human genetics, Human adaptation, 030104 developmental biology, 030211 gastroenterology & hepatology, Adaptation
Abstract

none 15 no Background: Non-celiac wheat sensitivity is an emerging wheat-related syndrome showing peak prevalence in Western populations. Recent studies hypothesize that new gliadin alleles introduced in the human diet by replacement of ancient wheat with modern varieties can prompt immune responses mediated by the CXCR3-chemokine axis potentially underlying such pathogenic inflammation. This cultural shift may also explain disease epidemiology, having turned European-specific adaptive alleles previously targeted by natural selection into disadvantageous ones. Methods: To explore this evolutionary scenario, we performed ultra-deep sequencing of genes pivotal in the CXCR3-inflammatory pathway on individuals diagnosed for non-celiac wheat sensitivity and we applied anthropological evolutionary genetics methods to sequence data from worldwide populations to investigate the genetic legacy of natural selection on these loci. Results: Our results indicate that balancing selection has maintained two divergent CXCL10/CXCL11 haplotypes in Europeans, one responsible for boosting inflammatory reactions and another for encoding moderate chemokine expression. Conclusions: This led to considerably higher occurrence of the former haplotype in Western people than in Africans and East Asians, suggesting that they might be more prone to side effects related to the consumption of modern wheat varieties. Accordingly, this study contributed to shed new light on some of the mechanisms potentially involved in the disease etiology and on the evolutionary bases of its present-day epidemiological patterns. Moreover, overrepresentation of disease homozygotes for the dis-adaptive haplotype plausibly accounts for their even more enhanced CXCR3-axis expression and for their further increase in disease risk, representing a promising finding to be validated by larger follow-up studies. open Sazzini, Marco; De Fanti, Sara; Cherubini, Anna; Quagliariello, Andrea; Profiti, Giuseppe; Martelli, Pier Luigi; Casadio, Rita; Ricci, Chiara; Campieri, Massimo; Lanzini, Alberto; Volta, Umberto; Caio, Giacomo; Franceschi, Claudio; Spisni, Enzo; Luiselli, Donata Sazzini, Marco; De Fanti, Sara; Cherubini, Anna; Quagliariello, Andrea; Profiti, Giuseppe; Martelli, Pier Luigi; Casadio, Rita; Ricci, Chiara; Campieri, Massimo; Lanzini, Alberto; Volta, Umberto; Caio, Giacomo; Franceschi, Claudio; Spisni, Enzo; Luiselli, Donata

Published
2015

35. AlignBucket: a tool to speed up 'all-against-all' protein sequence alignments optimizing length constraints

Author

Giuseppe Profiti, Rita Casadio, Piero Fariselli, Profiti, Giuseppe, Fariselli, Piero, and Casadio, Rita

Subjects
Statistics and Probability, Computer science, Nearest neighbor search, Sequence alignment, Bioinformatics, Biochemistry, Protein sequencing, Humans, Databases, Protein, sequence alignment, optimization, constraints, similarity search, sequence alignment, optimization, constraints, similarity search, Molecular Biology, Sequence, Computational Biology, Proteins, Partition (database), Computer Science Applications, Computational Mathematics, Range (mathematics), Computational Theory and Mathematics, Algorithm, Algorithms, Software
Abstract

Motivation: The next-generation sequencing era requires reliable, fast and efficient approaches for the accurate annotation of the ever-increasing number of biological sequences and their variations. Transfer of annotation upon similarity search is a standard approach. The procedure of all-against-all protein comparison is a preliminary step of different available methods that annotate sequences based on information already present in databases. Given the actual volume of sequences, methods are necessary to pre-process data to reduce the time of sequence comparison. Results: We present an algorithm that optimizes the partition of a large volume of sequences (the whole database) into sets where sequence length values (in residues) are constrained depending on a bounded minimal and expected alignment coverage. The idea is to optimally group protein sequences according to their length, and then computing the all-against-all sequence alignments among sequences that fall in a selected length range. We describe a mathematically optimal solution and we show that our method leads to a 5-fold speed-up in real world cases. Availability and implementation: The software is available for downloading at http://www.biocomp.unibo.it/∼giuseppe/partitioning.html. Contact: giuseppe.profiti2@unibo.it Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2015

36. Tools and data services registry: a community effort to document bioinformatics resources.

Author

Ison J, Rapacki K, Ménager H, Kalaš M, Rydza E, Chmura P, Anthon C, Beard N, Berka K, Bolser D, Booth T, Bretaudeau A, Brezovsky J, Casadio R, Cesareni G, Coppens F, Cornell M, Cuccuru G, Davidsen K, Vedova GD, Dogan T, Doppelt-Azeroual O, Emery L, Gasteiger E, Gatter T, Goldberg T, Grosjean M, Grüning B, Helmer-Citterich M, Ienasescu H, Ioannidis V, Jespersen MC, Jimenez R, Juty N, Juvan P, Koch M, Laibe C, Li JW, Licata L, Mareuil F, Mičetić I, Friborg RM, Moretti S, Morris C, Möller S, Nenadic A, Peterson H, Profiti G, Rice P, Romano P, Roncaglia P, Saidi R, Schafferhans A, Schwämmle V, Smith C, Sperotto MM, Stockinger H, Vařeková RS, Tosatto SC, de la Torre V, Uva P, Via A, Yachdav G, Zambelli F, Vriend G, Rost B, Parkinson H, Løngreen P, and Brunak S

Subjects
Data Curation, Software, Computational Biology, Registries
Abstract

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Published
2016
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