A report on the results of the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

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.

Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.

TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR1 (TCP) transcription factors control developmental processes in plants. The 24 TCP transcription factors encoded in the Arabidopsis (Arabidopsis thaliana) genome are divided into two classes, class I and class II TCPs, which are proposed to act antagonistically. We performed a detailed phenotypic analysis of the class I tcp20 mutant, showing an increase in leaf pavement cell sizes in 10-d-old seedlings. Subsequently, a glucocorticoid receptor induction assay was performed, aiming to identify potential target genes of the TCP20 protein during leaf development. The LIPOXYGENASE2 (LOX2) and class I TCP9 genes were identified as TCP20 targets, and binding of TCP20 to their regulatory sequences could be confirmed by chromatin immunoprecipitation analyses. LOX2 encodes for a jasmonate biosynthesis gene, which is also targeted by class II TCP proteins that are under the control of the microRNA JAGGED AND WAVY (JAW), although in an antagonistic manner. Mutation of TCP9, the second identified TCP20 target, resulted in increased pavement cell sizes during early leaf developmental stages. Analysis of senescence in the single tcp9 and tcp20 mutants and the tcp9tcp20 double mutants showed an earlier onset of this process in comparison with wild-type control plants in the double mutant only. Both the cell size and senescence phenotypes are opposite to the known class II TCP mutant phenotype in JAW plants. Altogether, these results point to an antagonistic function of class I and class II TCP proteins in the control of leaf development via the jasmonate signaling pathway.