Limits...
An expanded evaluation of protein function prediction methods shows an improvement in accuracy

View Article: PubMed Central - PubMed

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.

Electronic supplementary material: The online version of this article (doi:10.1186/s13059-016-1037-6) contains supplementary material, which is available to authorized users.

No MeSH data available.


Overall evaluation using the maximum F measure, Fmax. Evaluation was carried out on no-knowledge benchmark sequences in the full mode. The coverage of each method is shown within its performance bar. A perfect predictor would be characterized with Fmax=1. Confidence intervals (95 %) were determined using bootstrapping with 10,000 iterations on the set of benchmark sequences. For cases in which a principal investigator participated in multiple teams, the results of only the best-scoring method are presented. Details for all methods are provided in Additional file 1
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC5015320&req=5

Fig4: Overall evaluation using the maximum F measure, Fmax. Evaluation was carried out on no-knowledge benchmark sequences in the full mode. The coverage of each method is shown within its performance bar. A perfect predictor would be characterized with Fmax=1. Confidence intervals (95 %) were determined using bootstrapping with 10,000 iterations on the set of benchmark sequences. For cases in which a principal investigator participated in multiple teams, the results of only the best-scoring method are presented. Details for all methods are provided in Additional file 1

Mentions: Protein-centric evaluation measures how accurately methods can assign functional terms to a protein. The protein-centric performance evaluation of the top-ten methods is shown in Figs. 4, 5, and 6. The 95 % confidence intervals were estimated using bootstrapping on the benchmark set with B=10,000 iterations [15]. The results provide a broad insight into the state of the art.Fig. 4


An expanded evaluation of protein function prediction methods shows an improvement in accuracy
Overall evaluation using the maximum F measure, Fmax. Evaluation was carried out on no-knowledge benchmark sequences in the full mode. The coverage of each method is shown within its performance bar. A perfect predictor would be characterized with Fmax=1. Confidence intervals (95 %) were determined using bootstrapping with 10,000 iterations on the set of benchmark sequences. For cases in which a principal investigator participated in multiple teams, the results of only the best-scoring method are presented. Details for all methods are provided in Additional file 1
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5015320&req=5

Fig4: Overall evaluation using the maximum F measure, Fmax. Evaluation was carried out on no-knowledge benchmark sequences in the full mode. The coverage of each method is shown within its performance bar. A perfect predictor would be characterized with Fmax=1. Confidence intervals (95 %) were determined using bootstrapping with 10,000 iterations on the set of benchmark sequences. For cases in which a principal investigator participated in multiple teams, the results of only the best-scoring method are presented. Details for all methods are provided in Additional file 1
Mentions: Protein-centric evaluation measures how accurately methods can assign functional terms to a protein. The protein-centric performance evaluation of the top-ten methods is shown in Figs. 4, 5, and 6. The 95 % confidence intervals were estimated using bootstrapping on the benchmark set with B=10,000 iterations [15]. The results provide a broad insight into the state of the art.Fig. 4

View Article: PubMed Central - PubMed

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.

Electronic supplementary material: The online version of this article (doi:10.1186/s13059-016-1037-6) contains supplementary material, which is available to authorized users.

No MeSH data available.