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J Biomed Semantics. 2014 Jun 3;5(Suppl 1 Proceedings of the Bio-Ontologies Spec Interest G):S4. doi: 10.1186/2041-1480-5-S1-S4. eCollection 2014.

The influence of disease categories on gene candidate predictions from model organism phenotypes.

Author information

1
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA Hinxton, UK.
2
Institute for Medical Genetics and Human Genetics, Universitaetsklinikum Charite, Augustenburger Platz 1, 13353 Berlin, Germany.
3
Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, CA 94720 Berkeley, USA.
4
Ontology Development Group, OHSU Library, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd, OR 97239 Portland, USA.

Abstract

BACKGROUND:

The molecular etiology is still to be identified for about half of the currently described Mendelian diseases in humans, thereby hindering efforts to find treatments or preventive measures. Advances, such as new sequencing technologies, have led to increasing amounts of data becoming available with which to address the problem of identifying disease genes. Therefore, automated methods are needed that reliably predict disease gene candidates based on available data. We have recently developed Exomiser as a tool for identifying causative variants from exome analysis results by filtering and prioritising using a number of criteria including the phenotype similarity between the disease and mouse mutants involving the gene candidates. Initial investigations revealed a variation in performance for different medical categories of disease, due in part to a varying contribution of the phenotype scoring component.

RESULTS:

In this study, we further analyse the performance of our cross-species phenotype matching algorithm, and examine in more detail the reasons why disease gene filtering based on phenotype data works better for certain disease categories than others. We found that in addition to misleading phenotype alignments between species, some disease categories are still more amenable to automated predictions than others, and that this often ties in with community perceptions on how well the organism works as model.

CONCLUSIONS:

In conclusion, our automated disease gene candidate predictions are highly dependent on the organism used for the predictions and the disease category being studied. Future work on computational disease gene prediction using phenotype data would benefit from methods that take into account the disease category and the source of model organism data.

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