Application of topic models to a compendium of ChIP-Seq datasets uncovers recurrent transcriptional regulatory modules.
Bioinformatics
; 36(8): 2352-2358, 2020 04 15.
Article
en En
| MEDLINE
| ID: mdl-31899481
ABSTRACT
MOTIVATION The availability of thousands of genome-wide coupling chromatin immunoprecipitation (ChIP)-Seq datasets across hundreds of transcription factors (TFs) and cell lines provides an unprecedented opportunity to jointly analyze large-scale TF-binding in vivo, making possible the discovery of the potential interaction and cooperation among different TFs. The interacted and cooperated TFs can potentially form a transcriptional regulatory module (TRM) (e.g. co-binding TFs), which helps decipher the combinatorial regulatory mechanisms. RESULTS:
We develop a computational method tfLDA to apply state-of-the-art topic models to multiple ChIP-Seq datasets to decipher the combinatorial binding events of multiple TFs. tfLDA is able to learn high-order combinatorial binding patterns of TFs from multiple ChIP-Seq profiles, interpret and visualize the combinatorial patterns. We apply the tfLDA to two cell lines with a rich collection of TFs and identify combinatorial binding patterns that show well-known TRMs and related TF co-binding events. AVAILABILITY AND IMPLEMENTATION A software R package tfLDA is freely available at https//github.com/lichen-lab/tfLDA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Factores de Transcripción
/
Secuenciación de Inmunoprecipitación de Cromatina
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos