Name
Title | ||
|---|---|---|
CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data. |
Abstract | ||
|---|---|---|
Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from single cells) by mapping accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of cis-REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence (reference or personal genotypes) with ATAC-seq cut sites and read pileups. CoRE-ATAC was trained on 4 cell types (n = 6 samples/replicates) and accurately predicted known cis-RE functions from 7 cell types (n = 40 samples) that were not used in model training (mean average precision = 0.80, mean F1 score = 0.70). CoRE-ATAC enhancer predictions from 19 human islet samples coincided with genetically modulated gain/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred cis-RE function from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped with known functional annotations in sorted immune cells, which established the efficacy of these models to study cis-RE functions of rare cellswithout the need for cell sorting. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in cis-RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1371/journal.pcbi.1009670 | PLoS Comput. Biol. |
Keywords | DocType | Volume |
deep learning,ATAC-seq,snATAC-seq,<italic>cis</italic>-REs,enhancers,insulators | Journal | 17 |
Issue | ISSN | Citations |
12 | 1553-7358 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asa Thibodeau | 1 | 0 | 0.34 |
| Shubham Khetan | 2 | 0 | 0.34 |
| Alper Eroglu | 3 | 0 | 0.34 |
| Ryan Tewhey | 4 | 0 | 0.34 |
| Michael L. Stitzel | 5 | 2 | 1.09 |
| Duygu Ucar | 6 | 347 | 19.69 |