Pavel-Kravchenko

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Split CDS into codons, extract k-mers, sort sequences by GC%/length, and pack gene coordinates into tuples/namedtuples. Use when looping over genes/codons/SNPs/BED intervals, computing sliding-window GC%, or detecting gene overlaps in Python.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Vectorize bioinformatics math with NumPy — RPKM/CPM/TPM normalization, per-gene z-scores, broadcasting over genes x samples matrices, position weight matrices (PWM/PSSM) for motif scoring, and O(n) sliding-window GC content via cumsum. Use when normalizing count matrices, computing per-row/per-column statistics on…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Build Python classes with getitem/contains/call/slots and mixins for sequence databases, sliceable sequences, motif scorers, low-memory variants. Use for custom bio classes or dunder/OOP/slots code.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Apply //, %, and 'in' operators to DNA/protein data — codon math, reading frames, GC precedence, stop-codon lookups. Use when computing GC content, finding reading frames, filtering by QC, or fixing precedence bugs.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Manipulate bio DataFrames with pandas — loc/iloc selection, boolean/query filtering, groupby agg vs transform, left-join annotation merges, CSV/TSV expression-matrix I/O, wide/long melt. Use when indexing/filtering a gene or sample table, merging expression data with an annotation or clinical table, computing…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Foundational Python for biology - count nucleotides, compute GC content, use f-strings/loops/functions, and set up biopython/pandas/numpy via pip/venv/conda. Use when a beginner asks how to start with Python for bioinformatics, write a first DNA-parsing script, calculate GC content from a raw string, or install…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Match DNA/RNA/protein patterns with Python re — ORFs, restriction sites, IUPAC primers, PROSITE motifs, FASTA headers. Use when finding start/stop codons, tandem repeats, or parsing headers/BLAST output with regex.

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Manipulate DNA/RNA/protein sequences as raw Python strings: reverse complement via str.maketrans/translate, transcription, codon/ORF extraction, motif and restriction-site scanning with find()/re, and hand-rolled FASTA parsing without Biopython. Use when writing sequence utilities from scratch, debugging off-by-one…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Compute and verify calculus results in Python with sympy and scipy.integrate — symbolic diff/integrate/series/limit and Fourier coefficients, quad/dblquad/tplquad, trapezoid/Simpson on sampled data, solveivp, numerical gradients/Hessians, convergence tests and Jacobians. Use when computing an AUC (pharmacokinetics…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Solve linear-algebra tasks in bioinformatics with numpy.linalg and scipy.linalg — linear systems (solve vs inv, lstsq, nnls), rank with tolerance, null spaces, eig vs eigh, SVD/PCA, QR, Cholesky, expm, condition numbers, generalized eigenproblems. Use when computing steady-state fluxes or conservation laws from a…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Choose and verify classical statistical tests from first principles — test-selection table with assumptions and exact/asymptotic null distributions, tie corrections, correct scipy.stats calls (binomtest, wilcoxon, mannwhitneyu, kruskal, kstest, chisquare ddof, chi2contingency, levene, ttestind equalvar=False), CI…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Apply classical mechanics, oscillations and thermodynamics to biological problems in Python — velocity-Verlet integration and energy conservation, solveivp oscillator models, inertia tensors and radius of gyration from coordinates, normal modes, Boltzmann populations, diffusion/MSD, Carnot and free-energy (ΔG = ΔH −…

Pavel-Kravchenko/Bioinformatics

Skill Claude CodeCodex

Derive and verify probability-theory results — expectations/variances/characteristic functions of standard laws, densities of transformed variables, convolutions, Bayes posteriors, limit-theorem approximations (Poisson vs normal, continuity correction, Berry–Esseen) — with sympy.stats, scipy and simulation templates.…