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(Taken from: http://bioinformatics.oxfordjournals.org/cgi/content/full/21/18/3674)

Annotation is performed by applying an annotation rule (AR) to the obtained ontologies. The rule seeks to find the most specific annotations with a certain level of reliability. This process is adjustable in specificity and stringency.

For each candidate GO an annotation score (AS) is computed. The AS is composed of two additive terms. The first, direct term (DT), represents the highest hit similarity of this GO weighted by a factor corresponding to its EC. By employing ECs, B2G promotes the assignment of annotations with experimental evidence and penalizes electronic annotations or low traceability. The EC weights have been taken following recommendations of the GO Consortium and can be modified if desired. The second term (AT) of the AS provides the possibility of abstraction. This is defined as annotation to a parent node when several child nodes are present in the GO candidate collection. This term multiplies the number of total GOs unified at the node by a user defined GO weight factor that controls the possibility and strength of abstraction. Finally, the AR selects the lowest term per branch that lies over a user defined threshold. In an analytical form, DT, AT and the AR terms are defined as follows:

Application overview: The figure shows schematically a typical run of B2G. Used symbols are described in the embedded legend. Numbered circles denote the major application steps. From the left to the right these are (1) Blasting: a group of selected sequences is blasted against either the NCBI or custom databases, (2) Mapping: GO terms are mapped on the blast results using annotation files provided by the GO Consortium that are downloaded on a monthly basis at the Blast2GO server, (3) Annotation: sequences are annotated using an annotation rule that takes parameters provided by the user, (4) Statistical analysis: optionally, analysis of GO term distribution differences between groups of sequences can be performed and (5) Visualization: annotation and statistics results can be visualized on the GO DAG. At each of these steps, different charts are available to evaluate the progress of the analysis and data can be saved and exported in different formats.

• Comparison of Gene Ontology(GO) Tools for Automatic Function Prediction(AFP)