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.
Original language | English (US) |
---|---|
Article number | 184 |
Journal | Genome biology |
Volume | 17 |
Issue number | 1 |
DOIs | |
State | Published - Sep 7 2016 |
Externally published | Yes |
Keywords
- Disease gene prioritization
- Protein function prediction
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
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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, Vol. 17, No. 1, 184, 07.09.2016.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - An expanded evaluation of protein function prediction methods shows an improvement in accuracy
AU - Jiang, Yuxiang
AU - Oron, Tal Ronnen
AU - Clark, Wyatt T.
AU - Bankapur, Asma R.
AU - D'Andrea, Daniel
AU - Lepore, Rosalba
AU - Funk, Christopher S.
AU - Kahanda, Indika
AU - Verspoor, Karin M.
AU - Ben-Hur, Asa
AU - Koo, Da Chen Emily
AU - Penfold-Brown, Duncan
AU - Shasha, Dennis
AU - Youngs, Noah
AU - Bonneau, Richard
AU - Lin, Alexandra
AU - Sahraeian, Sayed M.E.
AU - Martelli, Pier Luigi
AU - Profiti, Giuseppe
AU - Casadio, Rita
AU - Cao, Renzhi
AU - Zhong, Zhaolong
AU - Cheng, Jianlin
AU - Altenhoff, Adrian
AU - Skunca, Nives
AU - Dessimoz, Christophe
AU - Dogan, Tunca
AU - Hakala, Kai
AU - Kaewphan, Suwisa
AU - Mehryary, Farrokh
AU - Salakoski, Tapio
AU - Ginter, Filip
AU - Fang, Hai
AU - Smithers, Ben
AU - Oates, Matt
AU - Gough, Julian
AU - Törönen, Petri
AU - Koskinen, Patrik
AU - Holm, Liisa
AU - Chen, Ching Tai
AU - Hsu, Wen Lian
AU - Bryson, Kevin
AU - Cozzetto, Domenico
AU - Minneci, Federico
AU - Jones, David T.
AU - Chapman, Samuel
AU - Bkc, Dukka
AU - Khan, Ishita K.
AU - Kihara, Daisuke
AU - Ofer, Dan
AU - Rappoport, Nadav
AU - Stern, Amos
AU - Cibrian-Uhalte, Elena
AU - Denny, Paul
AU - Foulger, Rebecca E.
AU - Hieta, Reija
AU - Legge, Duncan
AU - Lovering, Ruth C.
AU - Magrane, Michele
AU - Melidoni, Anna N.
AU - Mutowo-Meullenet, Prudence
AU - Pichler, Klemens
AU - Shypitsyna, Aleksandra
AU - Li, Biao
AU - Zakeri, Pooya
AU - ElShal, Sarah
AU - Tranchevent, Léon Charles
AU - Das, Sayoni
AU - Dawson, Natalie L.
AU - Lee, David
AU - Lees, Jonathan G.
AU - Sillitoe, Ian
AU - Bhat, Prajwal
AU - Nepusz, Tamás
AU - Romero, Alfonso E.
AU - Sasidharan, Rajkumar
AU - Yang, Haixuan
AU - Paccanaro, Alberto
AU - Gillis, Jesse
AU - Sedeño-Cortés, Adriana E.
AU - Pavlidis, Paul
AU - Feng, Shou
AU - Cejuela, Juan M.
AU - Goldberg, Tatyana
AU - Hamp, Tobias
AU - Richter, Lothar
AU - Salamov, Asaf
AU - Gabaldon, Toni
AU - Marcet-Houben, Marina
AU - Supek, Fran
AU - Gong, Qingtian
AU - Ning, Wei
AU - Zhou, Yuanpeng
AU - Tian, Weidong
AU - Falda, Marco
AU - Fontana, Paolo
AU - Lavezzo, Enrico
AU - Toppo, Stefano
AU - Ferrari, Carlo
AU - Giollo, Manuel
AU - Piovesan, Damiano
AU - Tosatto, Silvio C.E.
AU - del Pozo, Angela
AU - Fernández, José M.
AU - Maietta, Paolo
AU - Valencia, Alfonso
AU - Tress, Michael L.
AU - Benso, Alfredo
AU - Di Carlo, Stefano
AU - Politano, Gianfranco
AU - Savino, Alessandro
AU - Rehman, Hafeez Ur
AU - Re, Matteo
AU - Mesiti, Marco
AU - Valentini, Giorgio
AU - Bargsten, Joachim W.
AU - van Dijk, Aalt D.J.
AU - Gemovic, Branislava
AU - Glisic, Sanja
AU - Perovic, Vladmir
AU - Veljkovic, Veljko
AU - Veljkovic, Nevena
AU - Almeida-e-Silva, Danillo C.
AU - Vencio, Ricardo Z.N.
AU - Sharan, Malvika
AU - Vogel, Jörg
AU - Kansakar, Lakesh
AU - Zhang, Shanshan
AU - Vucetic, Slobodan
AU - Wang, Zheng
AU - Sternberg, Michael J.E.
AU - Wass, Mark N.
AU - Huntley, Rachael P.
AU - Martin, Maria J.
AU - O'Donovan, Claire
AU - Robinson, Peter N.
AU - Moreau, Yves
AU - Tramontano, Anna
AU - Babbitt, Patricia C.
AU - Brenner, Steven E.
AU - Linial, Michal
AU - Orengo, Christine A.
AU - Rost, Burkhard
AU - Greene, Casey S.
AU - Mooney, Sean D.
AU - Friedberg, Iddo
AU - Radivojac, Predrag
N1 - Funding Information: We acknowledge the contributions of Maximilian Hecht, Alexander Grün, Julia Krumhoff, My Nguyen Ly, Jonathan Boidol, Rene Schoeffel, Yann Spöri, Jessika Binder, Christoph Hamm and Karolina Worf. This work was partially supported by the following grants: National Science Foundation grants DBI-1458477 (PR), DBI-1458443 (SDM), DBI-1458390 (CSG), DBI-1458359 (IF), IIS-1319551 (DK), DBI-1262189 (DK), and DBI-1149224 (JC); National Institutes of Health grants R01GM093123 (JC), R01GM097528 (DK), R01GM076990 (PP), R01GM071749 (SEB), R01LM009722 (SDM), and UL1TR000423 (SDM); the National Natural Science Foundation of China grants 3147124 (WT) and 91231116 (WT); the National Basic Research Program of China grant 2012CB316505 (WT); NSERC grant RGPIN 371348-11 (PP); FP7 infrastructure project TransPLANT Award 283496 (ADJvD); Microsoft Research/FAPESP grant 2009/53161-6 and FAPESP fellowship 2010/50491-1 (DCAeS); Biotechnology and Biological Sciences Research Council grants BB/L020505/1 (DTJ), BB/F020481/1 (MJES), BB/K004131/1 (AP), BB/F00964X/1 (AP), and BB/L018241/1 (CD); the Spanish Ministry of Economics and Competitiveness grant BIO2012-40205 (MT); KU Leuven CoE PFV/10/016 SymBioSys (YM); the Newton International Fellowship Scheme of the Royal Society grant NF080750 (TN). CSG was supported in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative grant GBMF4552. Computational resources were provided by CSC – IT Center for Science Ltd., Espoo, Finland (TS). This work was supported by the Academy of Finland (TS). RCL and ANM were supported by British Heart Foundation grant RG/13/5/30112. PD, RCL, and REF were supported by Parkinson’s UK grant G-1307, the Alexander von Humboldt Foundation through the German Federal Ministry for Education and Research, Ernst Ludwig Ehrlich Studienwerk, and the Ministry of Education, Science and Technological Development of the Republic of Serbia grant 173001. This work was a Technology Development effort for ENIGMA – Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov), a Scientific Focus Area Program at Lawrence Berkeley National Laboratory, which is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research grant DE-AC02-05CH11231. ENIGMA only covers the application of this work to microbial proteins. NSF DBI-0965616 and Australian Research Council grant DP150101550 (KMV). NSF DBI-0965768 (ABH). NIH T15 LM00945102 (training grant for CSF). FP7 FET grant MAESTRA ICT-2013-612944 and FP7 REGPOT grant InnoMol (FS). NIH R01 GM60595 (PCB). University of Padova grants CPDA138081/13 (ST) and GRIC13AAI9 (EL). Swiss National Science Foundation grant 150654 and UK BBSRC grant BB/M015009/1 (COD). PRB2 IPT13/0001 - ISCIII-SGEFI / FEDER (JMF). Publisher Copyright: © 2016 The Author(s).
PY - 2016/9/7
Y1 - 2016/9/7
N2 - 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.
AB - 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.
KW - Disease gene prioritization
KW - Protein function prediction
UR - http://www.scopus.com/inward/record.url?scp=84986207718&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986207718&partnerID=8YFLogxK
U2 - 10.1186/s13059-016-1037-6
DO - 10.1186/s13059-016-1037-6
M3 - Article
C2 - 27604469
AN - SCOPUS:84986207718
VL - 17
JO - Genome Biology
JF - Genome Biology
SN - 1465-6914
IS - 1
M1 - 184
ER -