GSE227455
GSE GEO Potentially relatedRBP Footprint Grand Challenge: An evaluation of novel computational approaches to RNA-binding protein target prediction from structural data
Summary
The Froehlking team uses ADAR eCLIP data to train an generative model to predict ADAR binding site.
Overall Design
Protein-bound nucleotides have been shown to display high eCLIP, and in some systems and for select nucleotides, low icSHAPE reactivities at the same time. However, obtaining both eCLIP and icSHAPE data is expensive. We thus built a machine learning model that can be used to generate sequences characteristic of protein-bound regions from experimental data, even if some information is missing, and to predict protein binding sites (details can be found in Materials and Methods of associated publication). ADAR eCLIP in K562 cells.
Analysis
View Data ProcessingSupplementary Files (4)
GEO Samples (4)
SRA Experiments (4) and Runs (4)
Total: 3500 MBRuns (1)
| Run | Spots | Bases | Size (MB) | Files | Link |
|---|---|---|---|---|---|
| SRR23874044 | 21655170 | 1645792920 | 576.0 | SRA |
Runs (1)
| Run | Spots | Bases | Size (MB) | Files | Link |
|---|---|---|---|---|---|
| SRR23874043 | 64203155 | 4879439780 | 1658.0 | SRA |
Runs (1)
| Run | Spots | Bases | Size (MB) | Files | Link |
|---|---|---|---|---|---|
| SRR23874042 | 28276589 | 2149020764 | 744.0 | SRA |
Runs (1)
| Run | Spots | Bases | Size (MB) | Files | Link |
|---|---|---|---|---|---|
| SRR23874041 | 19799055 | 1504728180 | 522.0 | SRA |
Potentially Related Publications (1)
This accession was text-mined from the PMC full text of these publications. It may be referenced for comparison or reused from another study rather than generated for them.