pavel-kravchenko
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- ▌ Python Bio Data Types · pavel-kravchenkoUse Python's int, float, str, bool, and None types to represent and validate biological data (sequence lengths, GC content, DNA/RNA strings, missing annotations) and convert between them when parsing text records. Use when writing beginner Python for bioinformatics, explaining type() output, fixing float-equality bugs in p-value/GC-content comparisons, or converting split() fields from a FASTA/GFF/tab-delimited line into int/float.
- ▌ Python Bio Decorators · pavel-kravchenkoWrite @decorators (functools.wraps, @lru_cache, factories) to time, validate, and memoize bio functions. Use for pipeline timing/logging, DNA/protein alphabet checks, caching codon/alignment calls, or decorator stacking.
- ▌ Python Bio Generators · pavel-kravchenkoWrite Python generators (yield, itertools) for streaming FASTA/FASTQ readers, sliding-window GC/k-mer scans, and lazy translation pipelines that skip loading whole files into memory. Use for large FASTA/FASTQ parsing or MemoryError on genomic data.
- ▌ Algo Hash Tables Bloom · pavel-kravchenkoImplement Python hash tables (chaining, open addressing, rehashing) and Bloom filters for set membership. Use when building a hash table from scratch, resolving hash collisions, sizing a Bloom filter, or checking k-mer/key set membership under memory limits.
- ▌ Algo Intro Memoization · pavel-kravchenkoSpeed up exponential recursion (Fibonacci, alignment counting, coin change) to linear time via dict cache or lru_cache. Use when recursion is slow, or asked to memoize, add @lru_cache, or explain overlapping subproblems.
- ▌ Bio Applied Hla Typing · pavel-kravchenkoType HLA-A/B/C/DRB1 with OptiType/arcasHLA and predict peptide-MHC binding (NetMHCpan %Rank_EL/IC50) to rank neoantigens. Use for HLA typing, MHC binding, pVACseq, HLA LOH, or HLA-B*57:01 screening.
- ▌ Bio Applied Proteomics · pavel-kravchenkoCompute peptide b/y ion masses, run trypsin/PMF search, quantify LFQ protein abundance (volcano plots), and calculate PTM shifts and protein inference. Use for MS/MS peptide ID, PMF search, LFQ quantification, or PTM analysis.
- ▌ Bio Applied Qiime2 16s · pavel-kravchenkoRun QIIME2 16S amplicon workflows: import FASTQ, DADA2 denoise to ASVs, SILVA taxonomy, alpha/beta diversity, ANCOM-BC. Use when analyzing 16S/amplicon microbiome data or .qza/.qzv pipelines.
- ▌ Bio Core Gene Ontology · pavel-kravchenkoDecode GO evidence codes (IDA/IEA/TAS), propagate GO annotations via the true path rule, and run ORA (hypergeometric test + BH-FDR) in Python/R. Use when doing GO enrichment, evidence-code checks, or DAG annotation propagation.
- ▌ Bio Core Phylogenetics · pavel-kravchenkoBuild NJ/UPGMA trees from aligned FASTA with BioPython Bio.Phylo, score p-distance/JC69/K2P models, and parse Newick with bootstrap support. Use for tree building from an MSA, Newick I/O, or bootstrap-value interpretation.
- ▌ Genomic LLM Embeddings · pavel-kravchenkoBuild DNA embeddings via k-mer frequency vectors or genomic LMs (Nucleotide Transformer, DNABERT-2, HyenaDNA). Use when embedding DNA for ML, choosing k-mer/BPE tokenization, or probing embedding quality.
- ▌ Python Bio Expressions · pavel-kravchenkoUse Python arithmetic and comparison operators to compute GC content, codon/frame math, protein MW, and primer Tm. Use when calculating GC%, codon counts, reading frames, or fixing operator-precedence bugs in bio scripts.
- ▌ Vision Language Models · pavel-kravchenkoRun VLM inference (Qwen2-VL, LLaVA, CLIP) via Transformers: captioning, VQA, zero-shot classification, chart data extraction. Use when captioning images, answering visual questions, or classifying images with no labels.
- ▌ AI Science Genomic Llms · pavel-kravchenkoEmbed DNA with genomic foundation models (Nucleotide Transformer, HyenaDNA, Evo) via HuggingFace transformers; k-mer tokenize, probe promoter motifs. Use for DNA LLMs, genomic embeddings, or NT/HyenaDNA/Evo choice.
- ▌ Algo Sequence Alignment · pavel-kravchenkoImplement Needleman-Wunsch global and Smith-Waterman local sequence alignment: fill/traceback DP matrices, match/mismatch or BLOSUM62 scoring. Use when coding alignment from scratch or explaining DP traceback algorithms.
- ▌ Bio Applied Assembly Sv · pavel-kravchenkoAssemble ONT/HiFi reads with Flye/Hifiasm, polish with Medaka, QC with QUAST/BUSCO, call SVs (DEL/INS/INV/DUP/BND) with Sniffles2. Use for long-read assembly, N50/BUSCO QC, or nanopore/HiFi SV calling to VCF.
- ▌ Bio Applied Vdj Biology · pavel-kravchenkoCompute TCR/BCR clonotype diversity (Shannon, Simpson, clonality, Chao1, D50) from AIRR/10x VDJ tables; explains IMGT V/D/J nomenclature and CDR3 junctions. Use for repertoire diversity, clonal expansion, or scTCR-seq analysis.
- ▌ Foundations Probability · pavel-kravchenkoModel bioinformatics data with probability distributions (Normal, Binomial, Poisson, Negative Binomial) using scipy.stats — compute PMF/PDF/CDF/SF/PPF, simulate variant allele counts, mutation rates, and RNA-seq overdispersion. Use when doing probability calculations, distribution fitting, p-value derivation from a distribution, VAF/allele-count modeling, or explaining why RNA-seq counts need a negative binomial instead of Poisson.
- ▌ Population Structure Qc · pavel-kravchenkoDetect and correct for population stratification and cryptic relatedness in genotype data using PCA, kinship/IBD estimation, and genomic inflation factor (lambda) diagnostics before running a GWAS. Use when doing ancestry PCA, checking sample relatedness, computing genomic inflation, or QC'ing genotype data for stratification confounding.
- ▌ Protein Language Models · pavel-kravchenkoEmbed proteins with ESM2, predict structure via ESMFold, zero-shot score mutations with ESM-1v, or design sequences via ESM-IF1 (fair-esm). Use for protein embeddings, MSA-free structure, DMS/VUS scoring, fixed-backbone design.
- ▌ Python Bio Control Flow · pavel-kravchenkoWrite if/elif/for/while loops over DNA/RNA/protein strings: codon iteration, stop-codon/motif scanning, GC-content classification. Use when looping over sequences, extracting codons, or debugging an off-by-one loop.
- ▌ Python Bio Dictionaries · pavel-kravchenkoUse Python dict/defaultdict/Counter/set to translate codons, count k-mers, group genes by chromosome, and compare gene lists (union/intersection). Use when translating DNA, counting k-mers, or comparing gene sets.
- ▌ Virology Bioinformatics · pavel-kravchenkoAssemble viral genomes with iVar/minimap2, call intra-host SNVs with LoFreq, assign Nextclade/pangolin lineages. Use when trimming ARTIC primers, building a consensus FASTA, calling minority variants, or assigning a Pango lineage.
- ▌ Algo Binary Search Trees · pavel-kravchenkoImplement/debug a binary search tree in Python: insert, search, delete (3 cases), successor/predecessor, inorder/level-order traversal. Use for BST coding, O(log n) vs O(n) degenerate cases, or choosing AVL/Red-Black.
- ▌ Algo Complexity Analysis · pavel-kravchenkoDerive Big O time/space complexity of loops and recursion via recurrence relations; simplify expressions, compare growth at scale. Use when asked the complexity of code, hunting O(n^2) loops, or worst-case cost.
- ▌ Bio Applied Advanced Ngs · pavel-kravchenkoAssemble genomes de novo: greedy OLC, de Bruijn graph/Eulerian path, N50/L50/NG50 stats, SPAdes/Flye/hifiasm CLI usage. Use when choosing k-mer size, picking an assembler for Illumina/ONT/HiFi reads, or scoring contiguity.
- ▌ Bio Applied Biochemistry · pavel-kravchenkoFit Michaelis-Menten Vmax/Km with scipy curve_fit, convert absorbance to concentration via Beer-Lambert, and model enzyme inhibition. Use when analyzing enzyme assays or estimating Km, Vmax, kcat, or Ki.
- ▌ Bio Applied Dmr Analysis · pavel-kravchenkoCall 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.
- ▌ Bio Applied Ppi Networks · pavel-kravchenkoBuild and analyze protein-protein interaction (PPI) networks from STRING DB with NetworkX: compute degree/betweenness/closeness/eigenvector centrality, classify hub and bottleneck genes, test scale-free topology, and detect network communities/modules (Louvain, greedy modularity). Use when asked to find hub genes, identify drug targets from a network, query the STRING API, build a gene interaction graph, or cluster a PPI network into functional modules.
- ▌ Bio Applied Rdkit Basics · pavel-kravchenkoParse SMILES with RDKit, compute MW/LogP/TPSA/HBD/HBA descriptors and Lipinski Ro5, build Morgan/ECFP4 and MACCS fingerprints, score Tanimoto similarity. Use for cheminformatics, drug-likeness screening, or fingerprint similarity search.
- ▌ Bio Applied Testing Cicd · pavel-kravchenkoWrite pytest tests/fixtures for bio functions and GitHub Actions CI with pytest-cov, ruff, black, mypy. Use when adding tests to a bio tool, writing conftest.py fixtures, or building tests.yml/lint.yml CI workflows.
- ▌ Bio Applied Wgbs Bismark · pavel-kravchenkoAlign WGBS/RRBS bisulfite FASTQ with Bismark, extract per-CpG methylation into beta/M-values. Use for bismark_genome_preparation, deduplicate_bismark, bismark_methylation_extractor, or bismark.cov/CpG_report analysis.
- ▌ Bio Core Blast Searching · pavel-kravchenkoSearch protein/nucleotide sequences for homologs with NCBI BLAST+ (blastp/blastn/blastx/tblastn) via Biopython qblast or a local blastdb; parse E-value/bit-score/identity. Use for BLAST search, homology/similarity search, or FASTA annotation.
- ▌ Bio Core Motif Discovery · pavel-kravchenkoBuild PFM/PPM/PWM from TF binding sites, score/scan DNA with NumPy, pick thresholds, test motif enrichment (Fisher/BH-FDR). Use for ChIP-seq/SELEX motif scoring, IUPAC consensus, or JASPAR/HOCOMOCO matching.
- ▌ Bio Core Sequence Motifs · pavel-kravchenkoBuild PFM/PPM/PWM matrices from aligned sites with NumPy, scan sequences on both strands, compute information content, plot sequence logos, convert PROSITE patterns to regex. Use for motif scanning, TF binding site scoring, or PROSITE-to-regex tasks.
- ▌ Python Collections Regex · pavel-kravchenkoPython Counter/defaultdict/set for k-mer counting, streaming FASTA/FASTQ parsers, and re for restriction sites, codon motifs, PROSITE patterns. Use when extracting k-mers, streaming large sequence files, or regex-matching motifs/headers.
- ▌ AI Science LLM Finetuning · pavel-kravchenkoFine-tune LLMs (Mistral/Llama/Qwen) with LoRA/QLoRA via HuggingFace PEFT, bitsandbytes NF4, and trl SFTTrainer. Use when doing LoRA/QLoRA fine-tuning, PEFT, instruction tuning, or SFTTrainer on limited GPU memory.
- ▌ Algo Linear Binary Search · pavel-kravchenkoImplement Python linear/binary search: first/last occurrence, lower_bound/upper_bound (bisect), rotated-sorted-array search. Use when finding an index, searching sorted data, counting duplicates, or finding an insertion point.
- ▌ Bio Applied Molecular Gnn · pavel-kravchenkoTrain PyTorch Geometric GCN/MPNN on SMILES-derived molecular graphs to predict properties (BBBP, solubility, toxicity); compare vs Morgan-fingerprint RF. Use for GNN property prediction or SMILES-to-graph pipelines.
- ▌ Bio Applied Phylodynamics · pavel-kravchenkoBuild time-scaled phylogenies with TreeTime/Augur, validate clock via root-to-tip regression, interpret BEAST2 skyline plots and phylogeography. Use when dating an outbreak, estimating TMRCA, R0, or Ne(t).
- ▌ Bio Applied Primer Design · pavel-kravchenkoDesign PCR/qPCR primers with primer3-py design_primers/calc_hairpin, Bio.SeqUtils Tm, and blastn specificity checks. Use when designing PCR, qPCR, cloning, or genotyping primers, or checking Tm/dimers/specificity.
- ▌ Bio Applied Qsar Modeling · pavel-kravchenkoBuild QSAR classifiers from ChEMBL IC50 data with RDKit Morgan fingerprints, Random Forest, scaffold splits, and k-NN applicability domain. Use when predicting activity/pIC50 from SMILES or building structure-activity relationship models.
- ▌ Genomic Foundation Models · pavel-kravchenkoChoose/run DNA foundation models (Nucleotide Transformer, HyenaDNA, Evo, Enformer, Borzoi) via transformers: embed sequences, fine-tune, score variants with Enformer ISM. Use for genomic LLM choice or variant scoring.
- ▌ Python Bio Comprehensions · pavel-kravchenkoWrite Python comprehensions/generator expressions to filter, transform, count DNA/RNA/protein sequences (GC%, codons, k-mers, ORFs). Use when refactoring loop-heavy sequence code or streaming FASTA/FASTQ memory-efficiently.
- ▌ Python Bio Data Wrangling · pavel-kravchenkoClean/reshape bio pandas tables — impute NaNs, dedupe replicates, coerce clinical strings to numeric/categorical, melt/pivot wide-long, regex-parse GTF attrs. Use for cleaning expr/clinical dataframes or reshaping.
- ▌ Python Bio Error Handling · pavel-kravchenkoHandle malformed FASTA/GFF via try/except/else/finally, custom exceptions, raise-from chaining. Use for parsers crashing on bad input, strict vs lenient FASTA parsing, KeyError/IndexError/ValueError, or batches skipping bad records.
- ▌ Structural Bioinformatics · pavel-kravchenkoParse PDB structures with Bio.PDB; compute RMSD/TM-score via Kabsch superposition; run DSSP/Ramachandran and PWM/PROSITE scans; GO/KEGG enrichment. Use when parsing PDB files, computing RMSD, or enriching genes via GO/KEGG.
- ▌ Advanced String Structures · pavel-kravchenkoBuild tries, Aho-Corasick, and suffix arrays with Kasai LCP to index DNA/text and match many patterns in one pass. Use for genome motif scanning, k-mer indexing, longest-repeat search, or BWA/FM-index groundwork.
- ▌ AI Science Esm2 Embeddings · pavel-kravchenkoGenerate ESM2 protein embeddings (fair-esm/transformers) and predict structure with ESMFold. Use when embedding sequences, scoring mutations zero-shot, annotating protein function, or doing fast MSA-free structure prediction.
- ▌ AI Science Splicing Models · pavel-kravchenkoScore splicing variant effects with SpliceAI/Pangolin delta scores (DS_AG/DS_AL/DS_DG/DS_DL) and AlphaGenome. Use when scoring a VCF for splice disruption, interpreting DS thresholds, or ranking cryptic splice-site variants.
- ▌ Algo Graph Representations · pavel-kravchenkoBuild graph structures (adjacency matrix/list, edge list) in Python/NumPy for PPI, GRN, and metabolic networks. Use when representing a graph, picking sparse vs dense storage, loading an edge-list file, or prepping for BFS/DFS/Dijkstra/MST.
- ▌ Bio Applied Flow Cytometry · pavel-kravchenkoRead 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.
- ▌ Bio Applied Metabolic Flux · pavel-kravchenkoRun flux balance analysis (FBA/FVA) on genome-scale metabolic models (E. coli core, Recon3D, AGORA2) with COBRApy; simulate single/double gene knockouts and integrate RNA-seq expression via GIMME/iMAT. Use when predicting metabolic fluxes, finding essential genes or drug targets, doing synthetic lethality screens, or building transcriptomics-constrained metabolic models from SBML/JSON GEMs.
- ▌ Bio Applied Ont Processing · pavel-kravchenkoBasecall ONT POD5/FAST5 signal with Dorado (fast/hac/sup, duplex, 5mC/5hmC), QC with NanoStat/NanoPlot, filter with NanoFilt, and align with Minimap2 map-ont. Use for nanopore raw-signal processing, Q-score/length read filtering, N50 computation, or a POD5-to-aligned-BAM pipeline.
- ▌ Bio Applied Sc Integration · pavel-kravchenkoCorrect batch effects and integrate multiple scRNA-seq AnnData datasets with Harmony (harmonypy), scVI, or BBKNN; quantify mixing with LISI/ASW/kBET and transfer cell-type labels via KNN or scANVI. Use when merging samples from different batches/donors/labs/10x runs, when a UMAP shows batch-driven clustering instead of biology, or when mapping query cells onto a reference atlas (Azimuth/CELLxGENE Census).
- ▌ Bio Core Protein Structure · pavel-kravchenkoParse PDB files with BioPython Bio.PDB, compute distance/angle/dihedral/RMSD, superimpose structures (Kabsch), assign DSSP secondary structure. Use for protein structure analysis, RMSD/alignment, contact maps, Ramachandran plots.
- ▌ Foundations Bash Scripting · pavel-kravchenkoWrite robust Bash scripts to batch-process FASTQ/BAM/VCF/FASTA files: variables, set -euo pipefail error handling, loops over sample sheets, functions, traps, and awk/sed text processing. Use when automating a multi-sample pipeline, writing a shell wrapper around samtools/bcftools/fastqc/blast, validating CLI input files, or debugging a script that fails silently or mishandles filenames with spaces.
- ▌ Foundations R Fundamentals · pavel-kravchenkoRead/write R syntax for bioinformatics (vectors, data.frame, matrices, d/p/q/r distributions, DESeq2). Use when porting Python to R, debugging R from a paper/pipeline, or running DESeq2/edgeR/Seurat scripts.
- ▌ Graphs Dynamic Programming · pavel-kravchenkoImplement BFS/DFS, Dijkstra, Kruskal/Prim MST, topological sort, and DP (knapsack, Needleman-Wunsch, Smith-Waterman) in Python. Use for from-scratch alignment, PPI shortest paths, phylogenetic MST, gene-panel knapsack selection.
- ▌ Python Bio File Operations · pavel-kravchenkoRead/write FASTA, FASTQ, CSV/TSV (BED), JSON, and pickle files in Python using open()/context managers, csv.DictReader/DictWriter, and streaming generators for large genomics files. Use when parsing a FASTA/FASTQ file, writing sequences back out with line wrapping, reading/writing gene expression CSV or BED/TSV files, loading GenBank ORIGIN sequence, saving Python objects with pickle, or handling files too large to fit in memory.
- ▌ AI Science Geneformer Scgpt · pavel-kravchenkoTokenize scRNA-seq via Geneformer gene-rank or scGPT expression-bin encoding; annotate cell types, simulate in-silico knockouts. Use for foundation-model cell annotation, Geneformer/scGPT tokenization, or perturbation prediction.
- ▌ Algo Naive Pattern Matching · pavel-kravchenkoBrute-force O(n*m) sliding-window search for all overlapping matches of a pattern/motif/primer in text or DNA/protein strings, pure Python. Use for one-off exact search, or to benchmark the naive baseline before KMP/Rabin-Karp/Boyer-Moore.
- ▌ Bio Applied Coverage Tracks · pavel-kravchenkoGenerate 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 tracks, making TSS metagene heatmaps, or querying bigWig values in Python.
- ▌ Bio Applied Enzyme Kinetics · pavel-kravchenkoFit Michaelis-Menten/Hill kinetics with scipy curve_fit; get Vmax/Km/kcat with bootstrap CIs, classify enzyme inhibition type. Use for enzyme assay data, saturation curves, Ki estimation, kcat/Km efficiency.
- ▌ Bio Applied Genome Assembly · pavel-kravchenkoImplement 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.
- ▌ Bio Applied Network Modules · pavel-kravchenkoDetect PPI/co-expression modules with NetworkX/python-louvain/leidenalg (Louvain, Leiden, modularity Q) and WGCNA eigengenes. Use when clustering a gene network, computing WGCNA modules, or testing DEG/pathway enrichment on network communities.
- ▌ Bio Applied Tf Footprinting · pavel-kravchenkoDetect TF footprints in ATAC-seq via Tn5 offset correction, insertion-profile aggregation, footprint scoring, and pybedtools intersect/slop/closest. Use when doing TF footprinting, ATAC-seq Tn5 bias correction, footprint scoring, or motif-site meta-profile plots.
- ▌ Clinical Modeling Workflows · pavel-kravchenkoClassify variants via ACMG/AMP + CADD/REVEL/SpliceAI; dock ligands with AutoDock Vina; set up GROMACS MD; run Scanpy scRNA-seq QC/clustering. Use for variant classification, docking, MD setup, or scRNA-seq.
- ▌ Linear Tree Hash Structures · pavel-kravchenkoImplement Python linked lists, stacks/queues, BST/AVL/Red-Black trees, hash tables, Bloom filters with Big-O tradeoffs. Use when choosing a data structure, k-mer hash counting, VCF dedup Bloom filters, or interval trees.
- ▌ Python Bio Context Managers · pavel-kravchenkoBuild Python context managers (__enter__/__exit__, @contextmanager, sqlite3) for safe FASTA I/O, temp cleanup, DB transactions. Use for leaked file handles, temp files surviving crashes, or with-compatible readers/writers.
- ▌ Bio Applied Assembly Binning · pavel-kravchenkoAssemble shotgun metagenomic reads with MEGAHIT, bin contigs with MetaBAT2/CONCOCT/MaxBin2+DAS_Tool, grade MAGs with CheckM/MIMAG tiers. Use for metagenome assembly, contig binning, or MAG recovery.
- ▌ Bio Applied Bio Data Formats · pavel-kravchenkoParse/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.
- ▌ Bio Applied Capstone Project · pavel-kravchenkoBLAST-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.
- ▌ Bio Applied Chipseq Pipeline · pavel-kravchenkoFASTQ-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.
- ▌ Bio Applied Isoform Analysis · pavel-kravchenkoAlign ONT/PacBio long reads with Minimap2 splice, call isoforms with bambu (NDR), test differential isoform usage with DRIMSeq. Use for long-read transcriptomics, novel isoform calling, or DTU/isoform-switch analysis.
- ▌ Bio Applied Ngs Fundamentals · pavel-kravchenkoDecode Phred+33 FASTQ quality scores, compute FastQC-style per-position QC stats, and sliding-window trim reads in Python. Use when parsing FASTQ, decoding quality ASCII, or choosing Illumina/PacBio/Nanopore.
- ▌ Bio Applied Rna Seq Analysis · pavel-kravchenkoBulk RNA-seq — STAR/HISAT2/featureCounts or Salmon/kallisto quantification, TPM/DESeq2 size-factor normalization, DESeq2/pydeseq2 DE testing. Use for RNA-seq design, count matrices, or DE analysis with DESeq2, edgeR, pydeseq2.
- ▌ Bio Applied Scatac Chromatin · pavel-kravchenkoTF-IDF normalize scATAC-seq peak matrices and run LSI/SVD, dropping depth-correlated component 1, via SnapATAC2 or Signac/Seurat. Use when clustering 10x fragments.tsv.gz, running LSI/UMAP on ATAC data, or linking peaks to genes.
- ▌ Bio Applied Workflow Engines · pavel-kravchenkoWrite Snakemake rules/wildcards/config and Nextflow DSL2 processes/channels; run nf-core pipelines (rnaseq, sarek) on SLURM/AWS/GCP. Use when building a Snakefile, DSL2 workflow, or nf-core samplesheet.
- ▌ Genomics To Structure Triage · pavel-kravchenkoRoute coding variants to AlphaFold2/3 or RoseTTAFold and rank by missense/expression/rarity evidence weighted by pLDDT/PAE confidence. Use when triaging variants for structure prediction or picking AlphaFold vs RoseTTAFold.
- ▌ AI Science Zero Shot Mutation · pavel-kravchenkoScore protein point mutations zero-shot with ESM-1v/ESM-2 masked-LM log-odds, ensembled, benchmarked on ProteinGym DMS. Use when predicting mutation effects, ranking missense variants, scoring VUS fitness with no labels.
- ▌ Bio Applied Clinical Genomics · pavel-kravchenkoClassify 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 Benign/Benign calls, looking up a variant in ClinVar, or writing a clinical genomics/diagnostic report.
- ▌ Bio Applied Epigenetic Clocks · pavel-kravchenkoCompute 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.
- ▌ Bio Applied Immune Repertoire · pavel-kravchenkoAnalyze TCR/BCR repertoires with scirpy: import MiXCR/10x/AIRR clonotypes, define clonotypes, compute clonal expansion/diversity/VDJ usage. Use when analyzing scTCR-seq/scBCR-seq, clonotype tables, or CDR3 spectratypes.
- ▌ Bio Applied Virtual Screening · pavel-kravchenkoDock ligand libraries with AutoDock Vina/meeko, filter hits by ADMET (Lipinski, LogS, hERG), rank by composite docking+QSAR score in pandas. Use when docking SMILES/SDF vs a target or prioritizing virtual screening hits.
- ▌ Bio Core Biological Databases · pavel-kravchenkoFetch sequences via NCBI Entrez (esearch/efetch/elink), UniProt REST, and RCSB PDB APIs with BioPython/urllib. Use when picking a database, decoding accession prefixes (NM_/XM_/GSE/SRR), or cross-linking a gene NCBI-UniProt-PDB.
- ▌ Bio Core Biopython Essentials · pavel-kravchenkoManipulate Seq/SeqRecord objects, parse FASTA/FASTQ/GenBank with SeqIO, query NCBI via Entrez, and run PairwiseAligner in Biopython. Use for sequence I/O, translation, reverse complement, GC content, or NCBI fetch in Python.
- ▌ Bio Core Comparative Genomics · pavel-kravchenkoBuild dot plots, detect synteny/rearrangements, classify orthologs vs paralogs, compute pan-genomes; pick MUMmer/LASTZ/minimap2 for alignment. Use when comparing genomes, reading a dot plot, or finding synteny/orthologs.
- ▌ Bioinformatics Workflows Cicd · pavel-kravchenkoBuild reproducible, resumable bioinformatics pipelines with Snakemake rules or Nextflow DSL2 processes, run nf-core pipelines, and add pytest unit tests plus GitHub Actions CI. Use when writing a Snakefile, defining Nextflow processes/channels, scaling a pipeline to SLURM/AWS/GCP, containerizing tools with conda/Docker/Singularity, or setting up pytest fixtures and CI/CD for a genomics codebase.
- ▌ Foundations Statistics Python · pavel-kravchenkoRun scipy.stats/statsmodels tests (t-test, Mann-Whitney, ANOVA, chi-square, Fisher's exact, Pearson/Spearman) on expression/count/genotype data. Use when comparing groups, correcting p-values (FDR), or computing power/sample size in Python.
- ▌ Python Bio Data Visualization · pavel-kravchenkoBuild volcano/MA plots, clustermap heatmaps, and multi-panel GridSpec figures with matplotlib/seaborn. Use when plotting DE results, expression data, or QC distributions, or fixing savefig, log-axis, colormap bugs.
- ▌ AI Science Enformer Regulatory · pavel-kravchenkoPredict CAGE/DNase/ATAC/ChIP-seq tracks from raw DNA with Enformer/Borzoi, run in-silico mutagenesis (ISM), and score noncoding variant effects. Use when predicting enhancer/promoter activity from sequence, running ISM, scoring a noncoding SNP, or prioritizing GWAS/eQTL variants.
- ▌ Alphafold Structure Prediction · pavel-kravchenkoPredict protein 3D structure with AlphaFold2/ColabFold/ESMFold, fetch precomputed models from the AlphaFold DB, and interpret pLDDT/PAE confidence metrics and Cα RMSD. Use when predicting a structure from sequence, asking "how confident is this AlphaFold model", downloading an AF-*.pdb from alphafold.ebi.ac.uk, comparing predicted vs crystal structures, or triaging RFdiffusion/ProteinMPNN design candidates by confidence.
- ▌ Bio Applied Data Harmonization · pavel-kravchenkoHarmonize 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 values in proteomics MS data, or removing batch effects confounded with sequencing date/site before joint analysis.
- ▌ Bio Applied Mirna Seq Pipeline · pavel-kravchenkoTrim adapters (cutadapt), align to miRBase with Bowtie, quantify with featureCounts, run DESeq2/CPM DE testing and seed-match target prediction. Use for miRNA-seq/small RNA FASTQ processing or miRNA target prediction.
- ▌ Bio Applied Molecular Modeling · pavel-kravchenkoCompute force-field energy terms (bond/LJ/Coulomb), run energy minimization, and QC MD/homology models (RMSD, RMSF, Ramachandran) in NumPy. Use for force-field, minimization, or homology/docking validation.
- ▌ Bio Applied Single Cell Scanpy · pavel-kravchenkoRun a full scRNA-seq analysis in scanpy on an AnnData/10x/h5ad matrix — QC filtering (pct_counts_mt, n_genes_by_counts), normalize_total/log1p, HVG selection, PCA, neighbors/UMAP, Leiden clustering, and rank_genes_groups marker detection. Use when processing single-cell RNA-seq count matrices, clustering cells, annotating PBMC/tissue cell types from markers, or building a scanpy QC-to-UMAP-to-clusters pipeline.
- ▌ Bio Applied Structural Methods · pavel-kravchenkoParse PDB CRYST1/header for unit cell, space group, resolution, R-factors; apply symmetry operators; pick X-ray vs cryo-EM vs NMR. Use when checking structure quality, parsing CRYST1, or choosing a method.
- ▌ Bio Applied Variant Annotation · pavel-kravchenkoAnnotate a VCF's consequence/HGVS/impact with Ensembl VEP or snpEff, then join gnomAD AF, ClinVar, and dbNSFP scores. Use when annotating a VCF, running VEP/snpEff, or parsing CSQ/ANN fields.
- ▌ Bio Core Chromatogram Analysis · pavel-kravchenkoParse Sanger .ab1/.abi chromatograms with BioPython, extract Phred quality/trace channels, plot traces, quality-trim, and flag het double-peaks. Use for .ab1/.scf files or detecting het SNPs/mixed peaks.