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Enformer — transformer-based deep learning model from DeepMind that predicts gene expression, chromatin accessibility, histone modifications, and TF binding from 196,608 bp raw DNA sequence at 128 bp
Use with AI
Install the MCP server or CLI to instantly fetch Enformer documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/enformer
Use when working with Borzoi — a sequence-to-function DNA foundation model — for predicting genome-wide regulatory activity from DNA sequences. Borzoi predicts RNA-seq, ATAC-seq, ChIP-seq, and histone
2 shared topics • 1 shared operation
BPNet — deep learning framework for learning base-resolution regulatory sequence features from genomics assays (ChIP-seq, ATAC-seq, CUT&RUN). Uses dilated convolutional neural networks to predict TF b
2 shared topics • 1 shared operation
DVC (Data Version Control) — Git-based version control for data, models, and ML experiments. Provides data versioning with .dvc files, ML pipeline definition via dvc.yaml DAGs, experiment tracking and
1 shared topic • 1 shared operation
MACS2 (Model-based Analysis of ChIP-Seq) — the standard peak caller for ChIP-seq and ATAC-seq experiments. Identifies transcription factor binding sites (narrow peaks), histone modification domains (b
1 shared topic • 1 shared operation
Use when working with pybedtools — pybedtools — Python interface to BEDTools
1 shared topic • 1 shared operation