Attenzione: i dati modificati non sono ancora stati salvati. Per confermare inserimenti o cancellazioni di voci è necessario confermare con il tasto SALVA/INSERISCI in fondo alla pagina
CRIS Current Research Information System
BACKGROUND:
A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.
RESULTS:
We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.
CONCLUSIONS:
The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy / Jiang, Yuxiang; Oron, Tal Ronnen; Clark, Wyatt T.; Bankapur, Asma R.; D’Andrea, Daniel; Lepore, Rosalba; Funk, Christopher S.; Kahanda, Indika; Verspoor, Karin M.; Ben-Hur, Asa; Koo, Da Chen Emily; Penfold-Brown, Duncan; Shasha, Dennis; Youngs, Noah; Bonneau, Richard; Lin, Alexandra; Sahraeian, Sayed M. E.; Martelli, Pier Luigi; Profiti, Giuseppe; Casadio, Rita; Cao, Renzhi; Zhong, Zhaolong; Cheng, Jianlin; Altenhoff, Adrian; Skunca, 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; Bkc, Dukka; 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 C.E.; del Pozo, Angela; Fernández, José M.; Maietta, Paolo; Valencia, Alfonso; Tress, Michael L.; Benso, Alfredo; Di Carlo, Stefano; Politano, Gianfranco; Savino, Alessandro; Rehman, Hafeez Ur; Re, Matteo; Mesiti, Marco; Valentini, Giorgio; Bargsten, Joachim W.; van Dijk, Aalt D. J.; Gemovic, Branislava; Glisic, Sanja; Perovic, Vladmir; Veljkovic, Veljko; Veljkovic, Nevena; Almeida-e-Silva, Danillo C.; Vencio, Ricardo Z. N.; Sharan, Malvika; Vogel, Jörg; Kansakar, Lakesh; Zhang, Shanshan; Vucetic, Slobodan; Wang, Zheng; Sternberg, Michael J. E.; Wass, Mark N.; Huntley, Rachael P.; Martin, Maria J.; O’Donovan, Claire; Robinson, Peter N.; Moreau, Yves; Tramontano, Anna; Babbitt, Patricia C.; Brenner, Steven E.; Linial, Michal; Orengo, Christine A.; Rost, Burkhard; Greene, Casey S.; Mooney, Sean D.; Friedberg, Iddo; Radivojac, Predrag. - In: GENOME BIOLOGY. - ISSN 1474-760X. - ELETTRONICO. - 17:1(2016), pp. 184.1-184.19. [10.1186/s13059-016-1037-6]
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
Jiang, Yuxiang;Oron, Tal Ronnen;Clark, Wyatt T.;Bankapur, Asma R.;D’Andrea, Daniel;Lepore, Rosalba;Funk, Christopher S.;Kahanda, Indika;Verspoor, Karin M.;Ben Hur, Asa;Koo, Da Chen Emily;Penfold Brown, Duncan;Shasha, Dennis;Youngs, Noah;Bonneau, Richard;Lin, Alexandra;Sahraeian, Sayed M. E.;MARTELLI, PIER LUIGI;PROFITI, GIUSEPPE;CASADIO, RITA;Cao, Renzhi;Zhong, Zhaolong;Cheng, Jianlin;Altenhoff, Adrian;Skunca, 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;Bkc, Dukka;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 C. E.;del Pozo, Angela;Fernández, José M.;Maietta, Paolo;Valencia, Alfonso;Tress, Michael L.;Benso, Alfredo;Di Carlo, Stefano;Politano, Gianfranco;Savino, Alessandro;Rehman, Hafeez Ur;Re, Matteo;Mesiti, Marco;Valentini, Giorgio;Bargsten, Joachim W.;van Dijk, Aalt D. J.;Gemovic, Branislava;Glisic, Sanja;Perovic, Vladmir;Veljkovic, Veljko;Veljkovic, Nevena;Almeida e. Silva, Danillo C.;Vencio, Ricardo Z. N.;Sharan, Malvika;Vogel, Jörg;Kansakar, Lakesh;Zhang, Shanshan;Vucetic, Slobodan;Wang, Zheng;Sternberg, Michael J. E.;Wass, Mark N.;Huntley, Rachael P.;Martin, Maria J.;O’Donovan, Claire;Robinson, Peter N.;Moreau, Yves;Tramontano, Anna;Babbitt, Patricia C.;Brenner, Steven E.;Linial, Michal;Orengo, Christine A.;Rost, Burkhard;Greene, Casey S.;Mooney, Sean D.;Friedberg, Iddo;Radivojac, Predrag
2016
Abstract
BACKGROUND:
A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging.
RESULTS:
We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2.
CONCLUSIONS:
The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/565171
Citazioni
142
248
229
social impact
Conferma cancellazione
Sei sicuro che questo prodotto debba essere cancellato?
simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.