Pavel-Kravchenko/Bioinformatics

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Fit Bayesian models with PyMC/Bambi/ArviZ: NUTS sampling, prior/posterior checks, HDI intervals, hierarchical GLMMs, LOO/WAIC comparison. Use when doing Bayesian inference, hierarchical modeling, or MCMC diagnostics.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Parse/write FASTA, FASTQ, SAM/BAM, VCF, BED, GFF/GTF with pysam and pure Python; decode SAM FLAG/CIGAR; reconcile 0-based vs 1-based coordinates. Use for custom format parsers or off-by-one coordinate bugs.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Fit Michaelis-Menten Vmax/Km with scipy curvefit, convert absorbance to concentration via Beer-Lambert, and model enzyme inhibition. Use when analyzing enzyme assays or estimating Km, Vmax, kcat, or Ki.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Classify tumor RNA-seq into subtypes (melanoma Tirosh/Harbst on TCGA-SKCM): log1p/z-score, PCA/t-SNE, hierarchical clustering, random forest, Kaplan-Meier survival. Use when subtyping cBioPortal expression data.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

BLAST-identify unknown DNA/CDS with Biopython, QC/translate sequences, build NJ/UPGMA trees, and scan protein motifs. Use for sequence-to-discovery capstones, unknown-sequence ID, or FASTA-BLAST-tree pipelines.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Annotate scRNA-seq Leiden/Louvain clusters into cell types via canonical marker scoring, SingleR reference correlation, and CellTypist logistic-regression classification on an AnnData/SingleCellExperiment object. Use when doing cell type annotation, labeling clusters, scoring marker genes on a UMAP, running SingleR or…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

FASTQ-to-peaks ChIP-seq pipeline: Bowtie2 align, Picard dedup, MACS2/MACS3 narrow/broad peak calling, FRiP/IDR QC, deepTools bamCoverage/heatmaps. Use for ChIP-seq/CUT&RUN peak calling or FRiP/NRF/IDR QC.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Normalize CITE-seq ADT counts (CLR/DSB) and build WNN graphs joining RNA+protein or RNA+ATAC with muon/Seurat FindMultiModalNeighbors. Use for CITE-seq, 10x Multiome, or paired RNA+ADT/ATAC single-cell integration.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Classify germline variant pathogenicity with ACMG/AMP 5-tier criteria (PVS1/PS1-4/PM1-6/PP1-5/BA1/BS1-4/BP1-7), query ClinVar via NCBI E-utilities, and filter by gnomAD population frequency to draft a clinical variant report. Use when doing ACMG classification, deciding Pathogenic/Likely Pathogenic/VUS/Likely…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Call CNVs from binned read-depth: GC-bias normalization, circular binary segmentation (CBS), log2-ratio-to-CN-state calling, tumor-purity correction. Use for CNV/copy-number segmentation, log2 ratio analysis, or gain/deletion/amplification calls.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Generate normalized bigWig coverage tracks from BAM with deepTools bamCoverage/bamCompare (RPKM/CPM/RPGC), summarize with multiBamSummary, and plot TSS/region signal with computeMatrix + plotHeatmap/plotProfile; pyBigWig for programmatic access. Use when normalizing BAM to bigWig, computing ChIP/input log2 ratio…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Harmonize multi-omics data (RNA-seq, proteomics, methylation, metabolomics) before integration — per-layer normalization, KNN/half-minimum missing-value imputation, PCA/PVCA batch-effect detection, and ComBat correction with pandas/scikit-learn. Use when prepping matrices for MOFA2/DIABLO/mixOmics, fixing missing…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Train PyTorch CNN/LSTM/Transformer/VAE on DNA/protein sequences: one-hot encoding, motif filters, saliency. Use when classifying sequences, predicting TF binding sites, denoising scRNA-seq, or choosing DL vs ML.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Find ChIP-seq/ATAC-seq peaks that gain or lose signal between conditions using DiffBind (dba.count/dba.normalize/dba.analyze with DESeq2 or edgeR) on a consensus peak set, then annotate hits to genes/promoters with ChIPseeker annotatePeak. Use when comparing TF binding or histone marks across conditions, building…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Compute PCA/UMAP embeddings and Leiden clusters for scRNA-seq with scanpy/Seurat; tune npcs/nneighbors/resolution, find markers via rankgenesgroups. Use for UMAP plots, clustering single-cell data, or picking PCs/resolution.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Call DMRs from WGBS/RRBS beta values via BSmooth smoothing/t-stats or DSS/methylKit (R); annotate to promoters/CpG islands, correlate with RNA-seq log2FC. Use for DMR calling, DSS callDMR, or methylation-expression integration.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Dock ligands into a receptor with AutoDock Vina: build PDBQT files (Open Babel/RDKit), set the grid box, run vina, parse/rank poses by affinity and RMSD. Use for molecular docking, virtual screening, redocking a co-crystal ligand, or PDB/SMILES to PDBQT conversion.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Fit Michaelis-Menten/Hill kinetics with scipy curvefit; get Vmax/Km/kcat with bootstrap CIs, classify enzyme inhibition type. Use for enzyme assay data, saturation curves, Ki estimation, kcat/Km efficiency.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Compute DNA methylation age (Horvath/Hannum/GrimAge/PhenoAge elastic-net clocks) from 450K/EPIC beta values and epigenetic age acceleration (EAA). Use for DNAm clock scoring or EAA vs smoking/BMI/disease/mortality tests.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Read FCS 2.0/3.0/3.1 files with FlowKit/flowio, apply spillover compensation, logicle/arcsinh transforms, build gating hierarchies, and compute population statistics. Use when analyzing flow cytometry data, .fcs files, panels, compensation matrices, or gating trees.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Profile MetaCyc pathways/genes from metagenomes with HUMAnN3, test differential abundance via MaAsLin2, detect AMR genes with AMRFinderPlus. Use for metagenomic functional annotation, pathway abundance, or AMR profiling.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Infer TF-target regulatory networks via correlation, ARACNE mutual information, and GENIE3 random-forest importance; find feed-forward loops; validate against TRRUST. Use when building a GRN or asked about GENIE3, ARACNE, or regulons.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Simulate restriction digests, overhang compatibility, and primer Tm (Wallace/SantaLucia NN) in Python; plot agarose gel bands. Use when planning cloning, enzyme compatibility, or PCR primer design for a target Tm.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Implement OLC and de Bruijn assembly algorithms, compute N50/L50/NG50 stats, and run SPAdes/Flye/hifiasm on Illumina/HiFi/ONT reads. Use for k-mer graphs, comparing assemblers, or a FASTQ-to-contigs pipeline.