GO PaD: the Gene Ontology Partition Database

Abstract

Gene Ontology (GO) has been widely used to infer functional significance associated with sets of genes in order to automate discoveries within large-scale genetic studies. A level in GO's direct acyclic graph structure is often assumed to be indicative of its terms' specificities, although other work has suggested this assumption does not hold. Unfortunately, quantitative analysis of biological functions based on nodes at the same level (as is common in gene enrichment analysis tools) can lead to incorrect conclusions as well as missed discoveries due to inefficient use of available information. This paper addresses these using an informational theoretic approach encoded in the GO Partition Database that guarantees to maximize information for gene enrichment analysis. The GO Partition Database was designed to feature ontology partitions with GO terms of similar specificity. The GO partitions comprise varying numbers of nodes and present relevant information theoretic statistics, so researchers can choose to analyze datasets at arbitrary levels of specificity. The GO Partition Database, featuring GO partition sets for functional analysis of genes from human and 10 other commonly studied organisms with a total of 131 972 genes, is available on the internet at: . The site also includes an online tutorial.

An unsupervised learning method to identify reference intervals from a clinical database

“…A similar effect could occur if there are variations in coding practices within an institution, since a single diagnosis may be represented as several different ICD9 codes (which could get worse with usage of ICD-10). This is a limitation of LIMIT, which could be addressed if ICD9 codes were grouped, for example by using information theoretic approaches [45], into disease categories which have the same level of specificity.…”

Manual curation is not sufficient for annotation of genomic databases

“…As an example of the former: any ontologically structured data point can be characterized with respect to information content (see e.g. Alterovitz et al, 2007, Lord et al, 2003a. Lord et al (2003b) found that this measure, in connection with sequence similarity, uncovered a number of genes in LocusLink that were manually mis-annotated (pp.…”

Assessing identity, redundancy and confounds in Gene Ontology annotations over time

“…Assessment of GO's structure independent of annotation has tended to focus on issues of redundancy within the ontology structure; that is, using different names for the same concept or different concepts for the same name (Alterovitz et al, 2007;Onsongo et al, 2008). To the extent assessment of GO and its annotations are considered together, it is almost exclusively in the context of gene group enrichment analyses (Gross et al, 2012;Grossmann et al, 2007;Jantzen et al, 2011;Yang et al, 2011).…”