Research & Science · Python

Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains.

GPTomics/bioSkills

Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing.

GPTomics/bioSkills

Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests.

GPTomics/bioSkills

Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect…

GPTomics/bioSkills

Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content.

GPTomics/bioSkills

Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage.

GPTomics/bioSkills

Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or…

GPTomics/bioSkills

Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts.

GPTomics/bioSkills

Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term…

GPTomics/bioSkills

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout.

GPTomics/bioSkills

Generate consensus sequences and manage reference files using samtools.

GPTomics/bioSkills

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data.

GPTomics/bioSkills

Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror.

GPTomics/bioSkills

Generate pileup data for variant calling using samtools mpileup and pysam.

GPTomics/bioSkills

Production-ready genomics and epigenomics data processing for BixBench questions.

wu-yc/LabClaw

A skill your agent uses when data needs statistical analysis.

Aperivue/medsci-skills

Guide through omicverse's alignment module for SRA downloading, FASTQ quality control, STAR alignment, gene quantification, and single-cell kallisto/bustools pipelines covering both bulk and…

FreedomIntelligence/OpenClaw-Medical-Skills

Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality…

BioTender-max/awesome-bio-agent-skills

Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio.

FreedomIntelligence/OpenClaw-Medical-Skills

Read and write compressed sequence files (gzip, bzip2, BGZF) using Biopython.

FreedomIntelligence/OpenClaw-Medical-Skills

Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin.

FreedomIntelligence/OpenClaw-Medical-Skills

Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design.

FreedomIntelligence/OpenClaw-Medical-Skills

Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt.

FreedomIntelligence/OpenClaw-Medical-Skills

Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools.

FreedomIntelligence/OpenClaw-Medical-Skills

Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches.

FreedomIntelligence/OpenClaw-Medical-Skills

Infer cell-cell communication networks from scRNA-seq data using CellChat, NicheNet, and LIANA for ligand-receptor interaction analysis.

FreedomIntelligence/OpenClaw-Medical-Skills

Read, write, and create single-cell data objects using Seurat (R) and Scanpy (Python).

FreedomIntelligence/OpenClaw-Medical-Skills

Detect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data.

FreedomIntelligence/OpenClaw-Medical-Skills

Quality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python).

FreedomIntelligence/OpenClaw-Medical-Skills

Predict protein structures using modern ML models including AlphaFold3, ESMFold, Chai-1, and Boltz-1.

FreedomIntelligence/OpenClaw-Medical-Skills

Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels.

FreedomIntelligence/OpenClaw-Medical-Skills

Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN.

FreedomIntelligence/OpenClaw-Medical-Skills

Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python).

FreedomIntelligence/OpenClaw-Medical-Skills

Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python).

FreedomIntelligence/OpenClaw-Medical-Skills

Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial).

FreedomIntelligence/OpenClaw-Medical-Skills

A skill your agent uses when planning how many patients or cases a study needs before data collection (power analysis, IRB justification).

Aperivue/medsci-skills

A skill your agent uses when clinical data may contain PHI and must be de-identified before any LLM-assisted analysis.

Aperivue/medsci-skills

A skill your agent uses when a skill fails for a missing tool or the environment needs checking.

Aperivue/medsci-skills

Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs.

pedrohcgs/claude-code-my-workflow

Master skill for Abaqus FEA scripting. An agent skill from Cai-aa/CAE-Agent-Hub.

Cai-aa/CAE-Agent-Hub

面向 Nature Portfolio 与高影响力期刊的证据驱动科研绘图技能。用于从原始或汇总数据设计单图与多面板 figure、选择合适图形语法、编写 Python/R 绘图代码、重绘现有图件、生成机制示意图草案、撰写图注并导出可编辑 SVG/PDF 与高分辨率 TIFF/PNG；同时检查数据完整性、颜色可访问性、统计标注和最终尺寸可读性。触发场景包括 Nature…

LeonChaoX/qinyan-academic-skills

Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susierss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS.

GPTomics/bioSkills

Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L.

GPTomics/bioSkills

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with QSNP…

GPTomics/bioSkills

Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes.

GPTomics/bioSkills

Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2…

GPTomics/bioSkills

Queries ClinVar for variant pathogenicity classifications, ClinGen VCEP curations, and somatic-vs-germline interpretations via REST API, weekly VCF, or bulk XML.

GPTomics/bioSkills

Resolves rsIDs, navigates RsMergeArch/SNPHistory merge chains, and converts between rsID, SPDI, HGVS, and VCF representations using the dbSNP Build 156 JSON architecture.

GPTomics/bioSkills