deep-learning-genomics

Predict transcription factor binding and variant effects from genomic sequences.

6|2|Updated Jun 11, 2026

Search Tags:#deep learning #sequence analysis #genomic analysis #variant effect prediction #transcription factor binding

Ready to run

npx skills add https://github.com/pradyumnasagar/open-research-skills --skill deep-learning-genomics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-learning-genomics
Source: https://github.com/pradyumnasagar/open-research-skills/tree/main/skills/machine-learning-bio/deep-learning-genomics
Command: npx skills add https://github.com/pradyumnasagar/open-research-skills --skill deep-learning-genomics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, keras, tensorflow, biotite, pyfaidx, scikit-bio, pybedtools, dna-nlp, hyena-dna, transformers, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates genomic sequence analysis using deep learning models, providing insights into transcription factor binding, variant effects, and functional annotations.

Core Features & Use Cases

Predict Transcription Factor Binding: Analyze DNA sequences to predict transcription factor binding sites.
Variant Effect Scoring: Score the effect of genetic variants in noncoding regions.
Genome Annotation: Generate per-base functional annotations for the entire genome.
Use Case: For a researcher studying gene regulation, this Skill can predict where transcription factors are likely to bind in the genome, aiding in understanding gene expression patterns.

Quick Start

Analyze the DNA sequence for potential transcription factor binding sites using the deep-learning-genomics skill.

Frequently Asked Questions about deep-learning-genomics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I predict transcription factor binding sites using deep learning on DNA sequences?▼

To predict transcription factor binding sites, this Skill applies deep learning models to analyze DNA sequences, identifying likely binding locations to aid in understanding gene expression patterns and gene regulation.

Can I score the effect of genetic variants in noncoding regions with deep learning?▼

Yes, you can score variant effects in noncoding regions. The Skill utilizes deep learning frameworks to evaluate genetic variants and predict their functional impact on genomic sequences.

What deep learning frameworks are supported for genomic sequence analysis?▼

Genomic sequence analysis is supported by PyTorch, Keras, and TensorFlow. These deep learning frameworks are integrated alongside sequence handling libraries like Biotite, pyfaidx, scikit-bio, and pybedtools.

Does this approach work with transformer models like Hyena-DNA for genome annotation?▼

Yes, the Skill leverages transformer architectures including Hyena-DNA to generate per-base functional annotations across the entire genome, providing comprehensive genome-wide functional insights.

What's the best way to generate genome-wide functional annotations from DNA sequences?▼

The best way to generate genome annotations is by applying deep learning models to DNA sequences, which produces per-base functional predictions across the entire genome for research tasks.

Do I need pyfaidx and Biotite to handle genomic sequences for variant effect prediction?▼

Yes, sequence handling libraries such as Biotite, pyfaidx, scikit-bio, and pybedtools are required dependencies for processing genomic sequences before applying deep learning models for variant effect prediction.