菜单
Sequence-based deep learning (chromBPNet, tangermeme, TF-MoDISco) for ATAC-seq: Tn5 bias correction, variant effect prediction, and de novo motif discovery.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Load BioMaster as one integrated skill-driven bioinformatics assistant.
BioMaster conversational bioinformatics assistant; starts workflows only after execution intent or empty activation.
Automated and marker-guided single-cell cell type annotation using CellTypist, marker review, reference transfer, and confidence-aware label curation.
Ligand-receptor communication inference in single-cell or spatial data with sender-receiver summaries and cautious interpretation.
Workflow for paired or integrated single-cell RNA and ATAC analysis with multimodal latent spaces and regulatory interpretation.
Workflow for spatial transcriptomics preprocessing, domain detection, deconvolution, neighborhood analysis, and publication-ready spatial maps.
| name | bioskills-deep-learning-atac |
| description | Sequence-based deep learning (chromBPNet, tangermeme, TF-MoDISco) for ATAC-seq: Tn5 bias correction, variant effect prediction, and de novo motif discovery. |
| domain | single-cell |
| primary_tool | chromBPNet |
| methods | ["chromBPNet","bpnet-lite","tangermeme","tfmodisco-lite","DeepLIFT","scBasset","EnFormer","Borzoi","variant-scorer"] |
| macro_tags | ["deep-learning","atac-seq","variant-effect","motif-discovery","bias-correction","sequence-model"] |
| micro_tags | ["chromBPNet","bpnet-lite","tangermeme","tfmodisco-lite","DeepLIFT","scBasset","EnFormer","Borzoi","variant-scorer","tool-selection","environment-setup","version-check","data-prep","chromosome-splits","qc","bias-model","tn5-bias","cnn-training","accessibility-model","bias-correction","variant-effect","ism","deeplift","shap","tfmodisco","motif-discovery","validation","reconciliation","cross-tool"] |
| source | bioskills-deep-learning-atac |
| status | active |
| enabled | true |
