jaspar-database
JASPAR Database
Overview
JASPAR (https://jaspar.elixir.no/) is the gold-standard open-access database of curated, non-redundant transcription factor (TF) binding profiles stored as position frequency matrices (PFMs). JASPAR 2024 contains 1,210 non-redundant TF binding profiles for 164 eukaryotic species. Each profile is experimentally derived (ChIP-seq, SELEX, HT-SELEX, protein binding microarray, etc.) and rigorously validated.
Key resources:
- JASPAR portal: https://jaspar.elixir.no/
- REST API: https://jaspar.elixir.no/api/v1/
- API docs: https://jaspar.elixir.no/api/v1/docs/
- Python package:
jaspar(via Biopython) or direct API
When to Use This Skill
Use JASPAR when:
- TF binding site prediction: Scan a DNA sequence for potential binding sites of a TF
- Regulatory variant interpretation: Does a GWAS/eQTL variant disrupt a TF binding motif?
- Promoter/enhancer analysis: What TFs are predicted to bind to a regulatory element?
More from k-dense-ai/claude-scientific-skills
scientific-writing
Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.
136scientific-critical-thinking
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
115scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
114polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
113scientific-brainstorming
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
111scientific-slides
Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.
109