Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome.
Genome Biol
; 23(1): 126, 2022 06 10.
Article
en En
| MEDLINE
| ID: mdl-35681170
ABSTRACT
Existing methods for computational prediction of transcription factor (TF) binding sites evaluate genomic regions with similarity to known TF sequence preferences. Most TF binding sites, however, do not resemble known TF sequence motifs, and many TFs are not sequence-specific. We developed Virtual ChIP-seq, which predicts binding of individual TFs in new cell types, integrating learned associations with gene expression and binding, TF binding sites from other cell types, and chromatin accessibility data in the new cell type. This approach outperforms methods that predict TF binding solely based on sequence preference, predicting binding for 36 TFs (MCC>0.3).
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Transcriptoma
/
Secuenciación de Inmunoprecipitación de Cromatina
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Genome Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Canadá