Claude Skills by Pavel-Kravchenko

RNA-seq workflow — experimental design, alignment vs pseudo-alignment, count normalization (TPM), differential expression setup, and key pitfalls

Single-Cell Batch Correction and Dataset Integration with NumPy

Single-Cell ATAC-seq: Chromatin Accessibility with NumPy

CRISPR screen quality control and normalization: library distribution QC, Gini index, replicate correlation, and count normalization. Use when processing CRISPR screen count matrices.

scRNA-seq QC and preprocessing: AnnData construction, QC metrics, MAD filtering, normalization strategies

Full scanpy scRNA-seq workflow: QC, normalization, HVG, PCA, UMAP, Leiden clustering, marker gene detection

SNP calling pipeline: Trimmomatic, HISAT2/BWA-MEM2 alignment, samtools/bcftools variant calling, ANNOVAR annotation

Spatial transcriptomics: Visium data loading, spatial QC, spatial neighbor graphs, Moran's I for spatially variable genes, tissue visualization with Squidpy

Statistical testing for bioinformatics: distributions, hypothesis testing, multiple testing correction (Bonferroni, BH/FDR), parametric vs non-parametric test selection

Proteomics and structural methods: mass spectrometry ionization, MS/MS b/y ion calculation, trypsin digestion, database search engines, peptide mass fingerprinting

Taxonomic profiling of shotgun metagenomes: host decontamination, Kraken2 classification, Bracken abundance re-estimation

Testing and CI/CD for bioinformatics: pytest patterns, fixtures, GitHub Actions workflows, bioinformatics-specific test strategies

TF footprinting from ATAC-seq — Tn5 insertion profiles, footprint score calculation, pybedtools interval operations, accumulation plots.

scRNA-seq trajectory analysis: pseudotime (DPT), PAGA graph abstraction, and RNA velocity (scVelo). Decision guide, key parameters, and pitfalls.

Variant calling pipeline, VCF format, genotype decoding, and SNP analysis with GATK/bcftools

SARS-CoV-2 lineage classification (Pango), Freyja wastewater deconvolution, spike protein mutation tracking, and surveillance pipeline tools

V(D)J Recombination and Adaptive Immune Receptors

Viral genome assembly pipeline — ARTIC amplicon sequencing, iVar/LoFreq variant calling, quasispecies/minority variant detection, QC thresholds, and key pitfalls

Virtual screening for drug discovery: pharmacophore modeling, docking score filtering, and ADMET prediction. Use when computationally screening compound libraries.

WGBS/RRBS Processing with Bismark with Bismark

Snakemake and Nextflow/nf-core workflow patterns for genomics pipelines — rules, wildcards, config, cluster execution, and comparison table.

Programmatic access to NCBI (Entrez), UniProt, GEO, and SRA — accession types, API patterns, rate limits, and cross-database linking with BioPython

BioPython essentials — Seq/SeqRecord objects, SeqIO file I/O, Entrez API, pairwise alignment with PairwiseAligner.

BLAST: Sequence Similarity Searching with BLAST+

Sanger sequencing chromatogram analysis — reading .ab1 files with BioPython, Phred quality scores, trace visualization, and quality-based trimming

Dot plots, synteny analysis, genomic rearrangement detection, ortholog/paralog distinction, pan-genome concepts, and tool selection for pairwise genome alignment.

- Build the standard genetic code programmatically and translate DNA to protein - Understand codon degeneracy and compute codon usage statistics (RSCU, CAI) - Perform virtual restriction enzyme digest

Sequence motifs and protein domains — PWM construction, PWM scanning, information content, sequence logos, PROSITE patterns, and Pfam/HMMER concepts

Gene Ontology structure, evidence codes, enrichment analysis with hypergeometric test and BH correction

Hi-C Analysis: 3D Genome Organization with Matplotlib

PWM construction, scoring, threshold selection, and motif scanning for transcription factor binding sites

Progressive MSA algorithms, tool selection by dataset size, guide tree construction, and profile alignment

DNA helix forms (A/B/Z), groove geometry, nearest-neighbor thermodynamics, RNA secondary structure elements and dot-bracket notation

Pairwise Sequence Alignment with NumPy

Gene Ontology and pathway enrichment — GO structure, hypergeometric/Fisher test, ORA vs GSEA, Benjamini-Hochberg FDR, KEGG/Reactome API patterns.

Phylogenetics with BioPython: distance models (p-distance, JC69, K2P), UPGMA vs NJ tree construction, Newick parsing, and bootstrap interpretation.

Protein structure analysis with BioPython Bio.PDB — SMCRA hierarchy, distance/RMSD calculations, DSSP secondary structure assignment.

PWM/PFM/PPM construction, scoring, scanning, information content, sequence logos, and PROSITE pattern conversion.

Snakemake, Nextflow DSL2, GitHub Actions CI, and pytest patterns for bioinformatics pipelines.

BioPython Seq/SeqRecord/SeqIO, NCBI Entrez API, UniProt queries, and biological format conversion

Cheminformatics and drug discovery — RDKit molecular representations, fingerprints, Tanimoto similarity, QSAR modeling with ChEMBL data, AutoDock Vina docking, ADMET prediction, graph neural networks for molecules

ChIP-seq processing pipeline, peak calling with MACS3, differential binding with DiffBind, peak annotation with ChIPseeker, deepTools visualization

ACMG/AMP variant classification, in silico predictors, AutoDock Vina docking, GROMACS MD setup, and Scanpy single-cell analysis.

Bisulfite sequencing analysis: WGBS/RRBS processing, DMR calling, and epigenetic clock estimation.

Retrieval-augmented generation pipelines for document understanding.

Bash scripting essentials for bioinformatics: variables, conditionals, loops, and pipeline patterns.

Biostatistics fundamentals: descriptive statistics, distributions, hypothesis testing, and confidence intervals for biological data. Use when analyzing experimental results.

Every bioinformatics pipeline starts with reading data from files. FASTA sequences, GenBank records, PDB coordinates, GFF annotations -- they all live as bytes on disk. When your Python script reads g

Git for bioinformatics — setup, staging workflow, .gitignore, history navigation, undo operations, and branches.

Linux command-line essentials for bioinformatics: navigation, file ops, pipes, grep, file format inspection, and vim survival guide.