القائمة
Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is generated.
Orchestrate the full metabolic flux analysis pipeline from model loading to phenotype prediction and publication figures. Triggers when the user provides an organism name, BIGG model ID, or custom reaction list and wants end-to-end metabolic modelling run automatically.
| name | biology-biopython |
| description | Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA/RNA/protein sequences or biological databases. |
| metadata | {"category":"domain","trigger-keywords":"sequence,FASTA,genome,protein,BLAST,phylogenetic,biopython,bioinformatics,gene,DNA,RNA","applicable-stages":"9,10,12","priority":"4","version":"1.0","author":"researchclaw","references":"adapted from K-Dense-AI/claude-scientific-skills"} |
from Bio.Seq import Seq; seq = Seq("ATGCGA")seq.complement(); Reverse complement: seq.reverse_complement()seq.transcribe() (DNA to RNA)seq.translate() (DNA/RNA to protein)from Bio.SeqUtils import gc_fraction; gc_fraction(seq)from Bio.SeqUtils import molecular_weightfor rec in SeqIO.parse("file.fasta", "fasta"): ...for rec in SeqIO.parse("file.gb", "genbank"): ...rec = SeqIO.read("file.fasta", "fasta")SeqIO.write(records, "output.fasta", "fasta")SeqIO.convert("input.gb", "genbank", "output.fasta", "fasta")idx = SeqIO.index("large.fasta", "fasta") for random accessfrom Bio.Blast import NCBIWWW; result = NCBIWWW.qblast("blastn", "nt", seq)from Bio.Blast import NCBIXML; records = NCBIXML.parse(result)Entrez.email before any NCBI accessEntrez.email = "your@email.com"handle = Entrez.esearch(db="pubmed", term="query")handle = Entrez.efetch(db="nucleotide", id="ID", rettype="fasta")from Bio import Phylo; tree = Phylo.read("tree.nwk", "newick")Phylo.draw(tree) or Phylo.draw_ascii(tree)for clade in tree.find_clades(): ...tree.distance(clade1, clade2)parser = PDBParser(); structure = parser.get_structure("id", "file.pdb")structure.get_atoms()MMCIFParser() instead of PDBParser()SeqIO.parse as an iterator — it exhausts after one passSeqIO.index() not SeqIO.to_dict() to avoid memory issues