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High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.

The goal of the Gene Ontology (GO) project is to provide a uniform way to describe the functions of gene products from organisms across all kingdoms of life and thereby enable analysis of genomic data. Protein annotations are either based on experiments or predicted from protein sequences. Since most sequences have not been experimentally characterized, most available annotations need to be based on predictions. To make as accurate inferences as possible, the GO Consortium's Reference Genome Project is using an explicit evolutionary framework to infer annotations of proteins from a broad set of genomes from experimental annotations in a semi-automated manner. Most components in the pipeline, such as selection of sequences, building multiple sequence alignments and phylogenetic trees, retrieving experimental annotations and depositing inferred annotations, are fully automated. However, the most crucial step in our pipeline relies on software-assisted curation by an expert biologist. This curation tool, Phylogenetic Annotation and INference Tool (PAINT) helps curators to infer annotations among members of a protein family. PAINT allows curators to make precise assertions as to when functions were gained and lost during evolution and record the evidence (e.g. experimentally supported GO annotations and phylogenetic information including orthology) for those assertions. In this article, we describe how we use PAINT to infer protein function in a phylogenetic context with emphasis on its strengths, limitations and guidelines. We also discuss specific examples showing how PAINT annotations compare with those generated by other highly used homology-based methods.

Background: The social amoebae (Dictyostelia) are a diverse group of Amoebozoa that achieve multicellularity by aggregation and undergo morphogenesis into fruiting bodies with terminally differentiated spores and stalk cells. There are four groups of dictyostelids, with the most derived being a group that contains the model species Dictyostelium discoideum., Results: We have produced a draft genome sequence of another group dictyostelid, Dictyostelium purpureum, and compare it to the D. discoideum genome. The assembly (8.41 × coverage) comprises 799 scaffolds totaling 33.0 Mb, comparable to the D. discoideum genome size. Sequence comparisons suggest that these two dictyostelids shared a common ancestor approximately 400 million years ago. In spite of this divergence, most orthologs reside in small clusters of conserved synteny. Comparative analyses revealed a core set of orthologous genes that illuminate dictyostelid physiology, as well as differences in gene family content. Interesting patterns of gene conservation and divergence are also evident, suggesting function differences; some protein families, such as the histidine kinases, have undergone little functional change, whereas others, such as the polyketide synthases, have undergone extensive diversification. The abundant amino acid homopolymers encoded in both genomes are generally not found in homologous positions within proteins, so they are unlikely to derive from ancestral DNA triplet repeats. Genes involved in the social stage evolved more rapidly than others, consistent with either relaxed selection or accelerated evolution due to social conflict., Conclusions: The findings from this new genome sequence and comparative analysis shed light on the biology and evolution of the Dictyostelia.

In this protocol, we present a standard method for extracting DNA from cells of the social amoeba Dictyostelium discoideum. While this procedure is similar to other phenol:chloroform-based purification methods, it is modified to account for the high level of carbohydrate and nucleases found in Dictyostelium cells. Genomic DNA can be isolated from wild-type and genetically modified cells using the described protocol, allowing molecular genetic analyses to be performed. Following cell lysis, nucleic acid extraction, and precipitation, the isolated DNA is suitable for digestion by restriction enzymes, amplification by PCR and Southern blotting. This procedure takes approximately 3 h to complete.

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

Knowledge of biological organisms at the molecular level that has been gathered is now organized into databases, often within ontological frameworks. To enable computational comparisons of annotations across different genomes and organisms, controlled vocabularies have been essential, as is the case in the functional annotation classifications used for bacteria, such as MultiFun and the more widely used Gene Ontology. The function of individual gene products as well as the processes in which collections of them participate constitute a wealth of classes that describe the biological role of gene products in a large number of organisms in the three kingdoms of life. In this contribution, we highlight from a qualitative perspective some limitations of these frameworks and discuss challenges that need to be addressed to bridge the gap between annotation as currently captured by ontologies and databases and our understanding of the basic principles in the organization and functioning of organisms; we illustrate these challenges with some examples in bacteria. We hope that raising awareness of these issues will encourage users of Gene Ontology and similar ontologies to be careful about data interpretation and lead to improved data representation. [ABSTRACT FROM AUTHOR]