In addition to GO annotations derived from the manual curation of traditional experimental approaches published in the literature, SGD now contains GO annotations derived from data from high-throughput experiments as well as computational predictions provided by GOA UniProt, creating a central repository for all S. cerevisiae GO annotations. Although all of these annotations are supported by references and evidence codes, the basis for any differences among the GO annotations for any given gene may not be immediately clear. The curation process used for assigning GO annotations from these data varies according to the experimental approach. Therefore, in order to indicate how the data were curated, and to facilitate identification and comparison of these annotations, each GO annotation is now categorized in one of three annotation methods: manually curated, high-throughput or computational ().
The manually curated method indicates that the evidence in a publication has been individually reviewed to generate an annotation. Types of evidence can include experimental results in published literature that focuses on single genes or small sets of genes, author statements in a publication and sequence similarities that have been analyzed by the authors [for examples, see (12
) shown in B)].
Figure 1. Modifications to SGD interfaces to display the different GO annotation methods and data sources. (A) Manually curated and high-throughput GO annotations are individually listed on the Locus Summary, and the computational GO annotations are available by (more ...)
The high-throughput method indicates that, although the evidence for a subset of results from a high-throughput or genome-wide experimental approach may have been reviewed, results for each gene product in the dataset have not been individually reviewed. Generally, this annotation method includes data from experimental approaches in which all significant results were produced using the same condition or analysis [for examples, see (7
In contrast, annotations generated by the computational method are not supported by direct experimental evidence and are not individually reviewed. These annotations include predictions generated by sequence similarity algorithms or by the integrated computational analyses of different sets of high-throughput experimental data that have not been individually reviewed [(for examples, see (11
All literature-based GO annotations from SGD and GOA UniProt are classified either as manually curated or high-throughput. Computational predictions provided by GOA UniProt are classified as computational ().