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

Abstract

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

Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies

“…Functional annotations relating to musculoskeletal disease, especially OA, are poor and result in spurious descriptors. These important issues have been realized8 and methods to improve annotations are being developed 9…”

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

“…These algorithms are recognized as powerful alternative method for the functional prediction of both proteins [66][67][68][69][70] and other biomolecules [71]. However, over one third of the protein sequences in the UniProt [26] are still labeled as "putative", "uncharacterized", "unknown function" or "hypothetical", and the difficulty in discovering the functional class of the remaining proteins are reported to come from the false discovery rate of the in-silico methods [55,56,72]. Moreover, the identification accuracies of those approaches still need to be further improved [55,56,73].…”

“…However, over one third of the protein sequences in the UniProt [26] are still labeled as "putative", "uncharacterized", "unknown function" or "hypothetical", and the difficulty in discovering the functional class of the remaining proteins are reported to come from the false discovery rate of the in-silico methods [55,56,72]. Moreover, the identification accuracies of those approaches still need to be further improved [55,56,73]. Thus, it is urgently necessary to assess the identification accuracies and false discovery rates among those different in-silico approaches.…”

“…These include sequence similarity [27,28] Among these in-silico methods [52], the basic local alignment search tool (BLAST) [53] revealing protein functions based on excess sequence similarity [54] demonstrated great capacity and attracted substantial interests from the researchers of this field [55,56]. Apart from BLAST, the methods based on the machine learning algorithm (a specific type of artificial intelligence) were frequently used in recent years to predict protein function [57][58][59][60][61][62], and various types of software together with several web-based tools integrating these methods were developed to predict the protein function from sequences irrespective of sequence or structural similarity [36,63].…”

ASAP: A Machine-Learning Framework for Local Protein Properties

“…ML approaches are the state of the art in most non-classic prediction challenges. These methods are applied in community annotation challenges such as Critical Assessment of protein Function Annotation (CAFA) (5,6), and Critical Assessment for Information Extraction in Biology (BioCreAtIvE) (7). ML approaches actually benefit from the growth of available sequences, while 'brittle' rulebased methods often fail to cope with the growing variability and quantity of possible annotations and sequences.…”