القائمة
Skills في هذا المستودع
جمع SkillsMP عدد ٢١٣ من skills من Pavel-Kravchenko/Bioinformatics. افتح أي skill لمراجعة مصدره وتفاصيله.
Pavel-Kravchenko/Bioinformaticsعرض ٤٠ من أصل ٢١٣ skills مجمعة.
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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,…
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية
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…
لغة النص الأصلي: الإنجليزية
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.
لغة النص الأصلي: الإنجليزية