EMERGE: a flexible modelling framework to predict genomic regulatory elements from genomic signatures.
Nucleic Acids Res
; 44(5): e42, 2016 Mar 18.
Article
em En
| MEDLINE
| ID: mdl-26531828
ABSTRACT
Regulatory DNA elements, short genomic segments that regulate gene expression, have been implicated in developmental disorders and human disease. Despite this clinical urgency, only a small fraction of the regulatory DNA repertoire has been confirmed through reporter gene assays. The overall success rate of functional validation of candidate regulatory elements is low. Moreover, the number and diversity of datasets from which putative regulatory elements can be identified is large and rapidly increasing. We generated a flexible and user-friendly tool to integrate the information from different types of genomic datasets, e.g. ATAC-seq, ChIP-seq, conservation, aiming to increase the ease and success rate of functional prediction. To this end, we developed the EMERGE program that merges all datasets that the user considers informative and uses a logistic regression framework, based on validated functional elements, to set optimal weights to these datasets. ROC curve analysis shows that a combination of datasets leads to improved prediction of tissue-specific enhancers in human, mouse and Drosophila genomes. Functional assays based on this prediction can be expected to have substantially higher success rates. The resulting integrated signal for prediction of functional elements can be plotted in a build-in genome browser or exported for further analysis.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Mapeamento Cromossômico
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Elementos Facilitadores Genéticos
/
Genoma
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Drosophila melanogaster
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
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Humans
Idioma:
En
Revista:
Nucleic Acids Res
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
Holanda