Summary: QuickGO is a web-based tool that allows easy browsing of the Gene Ontology (GO) and all associated electronic and manual GO annotations provided by the GO Consortium annotation groups QuickGO has been a popular GO browser for many years, but after a recent redevelopment it is now able to offer a greater range of facilities including bulk downloads of GO annotation data which can be extensively filtered by a range of different parameters and GO slim set generation.Availability and Implementation: QuickGO has implemented in JavaScript, Ajax and HTML, with all major browsers supported. It can be queried online at http://www.ebi.ac.uk/QuickGO. The software for QuickGO is freely available under the Apache 2 licence and can be downloaded from http://www.ebi.ac.uk/QuickGO/installation.htmlContact: goa@ebi.ac.uk; dbinns@ebi.ac.uk

IntAct is an open source database and software suite for modeling, storing and analyzing molecular interaction data. The data available in the database originates entirely from published literature and is manually annotated by expert biologists to a high level of detail, including experimental methods, conditions and interacting domains. The database features over 126 000 binary interactions extracted from over 2100 scientific publications and makes extensive use of controlled vocabularies. The web site provides tools allowing users to search, visualize and download data from the repository. IntAct supports and encourages local installations as well as direct data submission and curation collaborations. IntAct source code and data are freely available from .

Cytokinins are plant hormones that regulate plant cell division. The D-type cyclin CycD3 was found to be elevated in a mutant of Arabidopsis with a high level of cytokinin and to be rapidly induced by cytokinin application in both cell cultures and whole plants. Constitutive expression of CycD3 in transgenic plants allowed induction and maintenance of cell division in the absence of exogenous cytokinin. Results suggest that cytokinin activates Arabidopsis cell division through induction of CycD3 at the G1-S cell cycle phase transition.

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.