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Building promoter aware transcriptional regulatory networks using siRNA perturbation and deepCAGE

Perturbation and time-course data sets, in combination with computational approaches, can be used to infer transcriptional regulatory networks which ultimately govern the developmental pathways and responses of cells. Here, we individually knocked down the four transcription factors PU.1, IRF8, MYB...

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Detalles Bibliográficos
Autores principales: Vitezic, Morana, Lassmann, Timo, Forrest, Alistair R. R., Suzuki, Masanori, Tomaru, Yasuhiro, Kawai, Jun, Carninci, Piero, Suzuki, Harukazu, Hayashizaki, Yoshihide, Daub, Carsten O.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3001087/
https://www.ncbi.nlm.nih.gov/pubmed/20724440
http://dx.doi.org/10.1093/nar/gkq729
Descripción
Sumario:Perturbation and time-course data sets, in combination with computational approaches, can be used to infer transcriptional regulatory networks which ultimately govern the developmental pathways and responses of cells. Here, we individually knocked down the four transcription factors PU.1, IRF8, MYB and SP1 in the human monocyte leukemia THP-1 cell line and profiled the genome-wide transcriptional response of individual transcription starting sites using deep sequencing based Cap Analysis of Gene Expression. From the proximal promoter regions of the responding transcription starting sites, we derived de novo binding-site motifs, characterized their biological function and constructed a network. We found a previously described composite motif for PU.1 and IRF8 that explains the overlapping set of transcriptional responses upon knockdown of either factor.