Cargando…
A neural network based model effectively predicts enhancers from clinical ATAC-seq samples
Enhancers are cis-acting sequences that regulate transcription rates of their target genes in a cell-specific manner and harbor disease-associated sequence variants in cognate cell types. Many complex diseases are associated with enhancer malfunction, necessitating the discovery and study of enhance...
Autores principales: | Thibodeau, Asa, Uyar, Asli, Khetan, Shubham, Stitzel, Michael L., Ucar, Duygu |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6207744/ https://www.ncbi.nlm.nih.gov/pubmed/30375457 http://dx.doi.org/10.1038/s41598-018-34420-9 |
Ejemplares similares
-
CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data
por: Thibodeau, Asa, et al.
Publicado: (2021) -
I-ATAC: interactive pipeline for the management and pre-processing of ATAC-seq samples
por: Ahmed, Zeeshan, et al.
Publicado: (2017) -
AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data
por: Thibodeau, Asa, et al.
Publicado: (2021) -
maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks
por: Cazares, Tareian A., et al.
Publicado: (2023) -
ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference
por: Pranzatelli, Thomas J. F., et al.
Publicado: (2018)