776 Nucleic Acids Research, 2009, Vol. 37, No. 3 abstract mention and gene perturbation counts were higher for genes with MorbidMap associations (t-test, P < 10 4). This was consistent with our expectation that genes associated to disease would be the subject of deeper study. However, genes that had been perturbed numerous times were not necessarily linked to disease. For example, out of the 100 most perturbed genes, 54 were not linked to disease in OMIM MorbidMap, including at the top such genes as jun oncogene, interleukin 4, ﬁbroblast growth factor 2 (basic), colony stimulating factor 2 (granulo- cyte–macrophage), colony stimulating factor 3 (granulo- cyte) and mitogen-activated protein kinase 3. DISCUSSION Perturbations are relevant for areas like the study of gene– disease associations, protein–protein interactions (PPI) or gene regulatory networks. Gene–disease association extraction studies have largely focused on simply detecting associations rather than characterizing them (7,14). PPI and gene regulation extraction studies have side-stepped perturbation types, without considering anything further than the experimental technique (15,16). We note that PPI relationships are frequently devoid of perturbations and the focus is on how a PPI was detected, which does not necessarily involve a causative relationship. The Proteomics Standards Initiative – Molecular Interactions (PSI-MI) ontology (6) is a comprehensive eﬀort to describe molecular interactions. This ontology, while including a detailed experimental preparation section, lacks expressivity in describing perturbations generically. Similarly to Bundschus et al. (7), it includes a section on expression level with entries ‘under expressed’, ‘over expressed’ and ‘physiological’. The lack of a general framework for recognizing, char- acterizing and classifying perturbations is surprising when one considers their importance in phenomena encountered experimentally. Researchers with interest in characterizing previous and current perturbation work on a biological system face the challenge of a naturalist trying to deal with animal species without a Linnaean taxonomy. This is reﬂected in the methodological landscape that was set in the early literature in the ﬁelds of biomedical ontologies and text mining. For example, the comprehensive text mining tool MedLEE (17) only considered the ‘state’ of a gene or protein, where the state has an adjectival role such as ‘mutated’ in the phrase ‘mutated X’. Perturbation descriptions, however, should be considered carefully. Observe the diﬀerences between the sentences (i) ‘X acti- vates Y.’ and (ii) ‘Inhibition of X activates Y’. From the point of view of classic PPI extraction both relationships are equivalent and can be represented with the triplet X activate Y. The perturbation in sentence (ii), however, sig- nals that it is likely that protein X is inhibiting protein Y instead. We have called this phenomenon ‘reversal’. Given the results of the present assessment, a review of the rela- tionship data available should be considered under this model. We have focused our methodology in gene–phenotype associations in disease but the principles shown are Table 4. Proposed annotation guidelines Relevance Relevant Irrelevant Direction Increase Decrease Unknown Molecule Gene RNA Protein expression Eﬀect Activity Abundance applicable to other well-known areas, such as PPI, as well as less explored ones such as identiﬁcation of biomar- kers or cellular processes. A perturbation taxonomy, like the one described in Table 4, could capture the diﬀerent dimensions that may be of interest to the inquiring scien- tist. This taxonomy has four annotation types: relevance, direction, molecule and eﬀect. Relevance annotation marks relationships that are irrelevant to the intent of the retrieval query. Direction distinguishes between per- turbations that represent an increase or a decrease over starting levels. Unknown direction annotation is intended for perturbations whose direction cannot be inferred at the sentence level. Molecule annotation characterizes the type of molecule being primarily aﬀected by the perturbation: gene, RNA or protein. Expression annotation is used for a change of expression level without clarifying whether the change is in RNA or protein. Eﬀect annotation diﬀerenti- ates between changes in activity or function and changes in abundance. The following examples illustrate these annotations: A gene mutation is a decrease in gene activ- ity, where the function/activity of a gene is speciﬁcally understood as making a wild-type transcript. Exogenous addition of a gene via viral transfection, plasmid transfor- mation, etc., is an increase in gene abundance. A gene duplication is an increase in gene abundance while a knockout is a decrease in gene abundance. Dominant neg- ative is a mutation in a gene, which indicates that, com- pared to wild-type, it has defective function. Silencing, knock-down and antisense all apply to a decrease in RNA abundance. An antibody blocking a protein decreases the protein activity or function. Interfering with a protein binding another protein is a decrease on a protein’s function or activity. Treatment, incubation, recombination, or synthetic refer to exogenous addition of protein. Perturbations evolve, notably as new techniques are developed and targets are identiﬁed. We expect perturba- tions to be subject to trends and popularity variations similar to those in other aspects of biomedicine (18–20). ACKNOWLEDGEMENTS We would like to thank Michael L. McGlashen for his continued support to the work that led to this manuscript. FUNDING Funding for open access charge: Pﬁzer Systems Biology. Conﬂict of interest statement. None declared.