An expanded evaluation of protein function prediction methods shows an improvement in accuracy.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy.
复制标题
DOI：
10.1186/s13059-016-1037-6
复制
发表时间：
2016-09-07
期刊：
影响因子：
12.3
通讯作者：
Radivojac P
中科院分区：
文献类型：
--
作者：
Jiang Y;Oron TR;Clark WT;Bankapur AR;D'Andrea D;Lepore R;Funk CS;Kahanda I;Verspoor KM;Ben-Hur A;Koo da CE;Penfold-Brown D;Shasha D;Youngs N;Bonneau R;Lin A;Sahraeian SM;Martelli PL;Profiti G;Casadio R;Cao R;Zhong Z;Cheng J;Altenhoff A;Skunca N;Dessimoz C;Dogan T;Hakala K;Kaewphan S;Mehryary F;Salakoski T;Ginter F;Fang H;Smithers B;Oates M;Gough J;Törönen P;Koskinen P;Holm L;Chen CT;Hsu WL;Bryson K;Cozzetto D;Minneci F;Jones DT;Chapman S;Bkc D;Khan IK;Kihara D;Ofer D;Rappoport N;Stern A;Cibrian-Uhalte E;Denny P;Foulger RE;Hieta R;Legge D;Lovering RC;Magrane M;Melidoni AN;Mutowo-Meullenet P;Pichler K;Shypitsyna A;Li B;Zakeri P;ElShal S;Tranchevent LC;Das S;Dawson NL;Lee D;Lees JG;Sillitoe I;Bhat P;Nepusz T;Romero AE;Sasidharan R;Yang H;Paccanaro A;Gillis J;Sedeño-Cortés AE;Pavlidis P;Feng S;Cejuela JM;Goldberg T;Hamp T;Richter L;Salamov A;Gabaldon T;Marcet-Houben M;Supek F;Gong Q;Ning W;Zhou Y;Tian W;Falda M;Fontana P;Lavezzo E;Toppo S;Ferrari C;Giollo M;Piovesan D;Tosatto SC;Del Pozo A;Fernández JM;Maietta P;Valencia A;Tress ML;Benso A;Di Carlo S;Politano G;Savino A;Rehman HU;Re M;Mesiti M;Valentini G;Bargsten JW;van Dijk AD;Gemovic B;Glisic S;Perovic V;Veljkovic V;Veljkovic N;Almeida-E-Silva DC;Vencio RZ;Sharan M;Vogel J;Kansakar L;Zhang S;Vucetic S;Wang Z;Sternberg MJ;Wass MN;Huntley RP;Martin MJ;O'Donovan C;Robinson PN;Moreau Y;Tramontano A;Babbitt PC;Brenner SE;Linial M;Orengo CA;Rost B;Greene CS;Mooney SD;Friedberg I;Radivojac P
我们理解生命的分子基础的主要瓶颈是蛋白质的功能分配。虽然分子实验提供了最可靠的蛋白质注释，但其相对较低的通量和有限的范围导致计算功能预测的作用越来越大。然而，评估蛋白质功能预测方法和跟踪该领域的进展仍然具有挑战性。我们进行了功能注释（CAFA）的第二次关键评估，这是一项评估自动分配蛋白质功能的计算方法的定时挑战。我们评估了来自56个研究小组的126种方法，以评估它们使用基因本体论预测生物学功能的能力，以及使用人类表型本体论预测来自18个物种的3681种蛋白质的基因-疾病关联。与CAFA 1相比，CAFA 2在数据集大小、种类和评估指标方面进行了扩展分析。为了回顾该领域的进展，分析比较了CAFA 1和CAFA 2的最佳方法。CAFA 2中表现最好的方法优于CAFA 1中的方法。这种提高的准确性可以归因于越来越多的实验注释和改进的功能预测方法的结合。评估还显示，最佳性能算法的定义是本体特定的，不同的性能指标可用于探测准确预测的性质，以及生物过程和人类表型本体中预测的相对多样性。虽然CAFA 1和CAFA 2之间有方法上的改进，但对结果的解释和个别方法的有用性仍然取决于具体情况。本文的在线版本（doi：10.1186/s13059-016-1037-6）包含补充材料，可供授权用户使用。
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. 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. 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. The online version of this article (doi:10.1186/s13059-016-1037-6) contains supplementary material, which is available to authorized users.
登录
查看更多内容
DOI：
10.1093/bioinformatics/btt228
发表时间：
2013-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Clark WT;Radivojac P
通讯作者：
Radivojac P
DOI：
10.1093/bioinformatics/btu472
发表时间：
2014-09-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Jiang Y;Clark WT;Friedberg I;Radivojac P
通讯作者：
Radivojac P
影响因子：
4.8
作者：
Cal, S;Argüelles, JM;López-Otin, C
通讯作者：
López-Otin, C
影响因子：
2.9
作者：
Clark, Wyatt T.;Radivojac, Predrag
通讯作者：
Radivojac, Predrag
影响因子：
6.7
作者：
Costello, J. C.;Stolovitzky, G.
通讯作者：
Stolovitzky, G.