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SkillsMP has collected 213 skills from Pavel-Kravchenko/Bioinformatics. Open a skill to review its source and details.
Pavel-Kravchenko/BioinformaticsShowing 40 of 213 collected skills.
Restore images (denoising, inpainting, super-resolution) via DDRM SVD data-consistency projection with DDIM diffusion sampling in NumPy. Use for cryo-EM/MRI restoration or inverse problems y=Hx with a diffusion prior.
Choose/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.
Build 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.
Route 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.
Implement 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.
Analyze scTCR/BCR-seq with scirpy on 10x VDJ contigs, type HLA with OptiType, and score neoantigens with NetMHCpan/pVACseq. Use when doing clonotype/repertoire analysis, HLA typing, or building neoantigen pipelines.
Implement 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.
Write set -euo pipefail bash pipelines, parse FASTA/FASTQ/VCF/GTF/BED with grep/awk/sed and BAM with samtools, and run git workflows. Use when writing/debugging shell scripts or fixing git/BOM/CRLF issues.
Basecall ONT POD5 with Dorado, align with Minimap2, assemble with Flye/Hifiasm, call SVs with Sniffles2. Use when basecalling nanopore reads, doing long-read assembly, SV calling, or ONT methylation/isoform analysis.
Parse LC-MS mzML with pyOpenMS, PQN/LOESS-normalize feature tables, match m/z to HMDB/GNPS by ppm, run COBRApy FBA. Use when doing metabolomics preprocessing, metabolite ID, feature QC, MSEA enrichment, or flux modeling.
Compute 16S/ITS amplicon diversity (Shannon, Simpson, Bray-Curtis, UniFrac), PCoA/NMDS ordination, PERMANOVA on OTU/ASV tables from QIIME2/DADA2. Use for 16S microbiome analysis.
Run Bowtie2 decontamination, Kraken2/Bracken classification, HUMAnN3 pathways, and MEGAHIT/MetaBAT2/CheckM MAG recovery on shotgun metagenomes. Use for WMS/WGS metagenomics, microbiome profiling, or MAG binning.
Build PPI networks from STRING with NetworkX, find hub genes via centrality, detect Louvain modules, infer GRNs with GENIE3. Use for protein interaction networks, hub/bottleneck genes, network communities, GRN inference.
Detect 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,…
Embed 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.
Write Python decorators/context managers/dataclasses and query gene/variant tables with sqlite3/pandas SQL (JOIN, GROUP BY, HAVING). Use for retry/caching/validation wrappers or SQL against Ensembl/UCSC-style schemas.
Build Python classes for Gene/DNA/RNA/Protein records with __eq__/__lt__/__hash__, @property validation, ABCs, and @classmethod parsers (from_fasta_string). Use when modeling genes/FASTA/GFF as objects or asked about Python OOP, inheritance, dataclasses.
Write 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.
Build 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.
Write 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.
Use 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…
Build 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.
Clean/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.
Write @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.
Use 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.
Handle 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.
Use 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.
Read/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…
Write Python `def` functions for bio scripts — ORF finders, reverse-complement, Hamming distance, *args/**kwargs, @lru_cache. Use for a mutable-default-argument bug, *args/**kwargs signatures, or reusable sequence helpers.
Write 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.
Stream FASTA/FASTQ and generate k-mers/codons lazily with Python generators, custom __iter__/__next__ classes, and itertools. Use when parsing multi-GB sequence files without loading them fully into RAM or chaining filter-trim-translate pipelines.
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.
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…
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.
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.
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…
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…
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.
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…
Python set ops (union/intersection/difference) and collections.Counter for gene-list comparisons and k-mer/codon counting. Use when comparing gene lists, finding shared orthologs, computing k-mer Jaccard similarity, or tallying GC/codon usage.