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データ・研究 · SKILL.md

specialized-analyses-and-graphics

specialized-analyses-and-graphics

Routes Biopython motif, restriction enzyme, clustering, phenotype, population genetics, graphics, protein-analysis, and long-tail module tasks.

Independent GitHub snapshot · Automated classification · Original documentation governs

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GitHub

name: specialized-analyses-and-graphics description: "Routes Biopython motif, restriction enzyme, clustering, phenotype, population genetics, graphics, protein-analysis, and long-tail module tasks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Specialized Analyses and Graphics

Use this sub-skill when the user asks for Biopython workflows outside the core sequence/alignment/structure/database paths: motif/PWM/PSSM work, restriction enzyme analysis, clustering numeric data, phenotype microarray data, GenePop population genetics, GenomeDiagram or other graphics, protein property calculations, and long-tail modules such as CAPS, COMPASS, NMR, SCOP, Pathway, and Bio.Data constants.

Route here for

  • Creating, parsing, formatting, reverse-complementing, scoring, and scanning motifs with Bio.motifs, including JASPAR flat files and PWM/PSSM thresholds.
  • Restriction enzyme searches, digests, enzyme batches, circular-vs-linear maps, and Analysis filters with Bio.Restriction.
  • Numeric clustering with Bio.Cluster: distance matrices, k-means/medians, k-medoids, hierarchical clustering, SOMs, centroids, PCA, masks, and distance-code choices.
  • Phenotype Microarray records with Bio.phenotype: pm-json/pm-csv, PlateRecord, WellRecord, interpolation, control subtraction, and optional curve fitting.
  • Population-genetics data in GenePop format with Bio.PopGen.GenePop.
  • Bio.Graphics and Bio.Graphics.GenomeDiagram diagrams when optional graphics dependencies are available.
  • Protein summaries with Bio.SeqUtils.ProtParam.ProteinAnalysis and related Bio.SeqUtils/Bio.Data helpers.
  • Long-tail Biopython modules where this sub-skill is the closest owner: Bio.CAPS, Bio.Compass, Bio.NMR, Bio.SCOP, and Bio.Pathway.

Route away

  • Core Seq, SeqRecord, SeqFeature, coordinate, translation, transcription, and basic SeqUtils tasks: use ../sequence-objects-and-features/SKILL.md.
  • File conversion with SeqIO, AlignIO, compression, indexing, or general format tables: use ../file-io-and-format-conversion/SKILL.md.
  • Alignments, BLAST/SearchIO, phylogenetic trees, and external aligner guidance: use ../alignment-search-and-phylogeny/SKILL.md.
  • PDB/mmCIF structure parsing, writing, superposition, and structure geometry: use ../structural-bioinformatics/SKILL.md.
  • Online JASPAR SQL database access, Entrez/KEGG/web services, BioSQL, credentials, and network policy: use ../web-databases-and-biosql/SKILL.md.

First steps

  1. Identify the target module and whether the request is offline, network/database-backed, or graphics/optional-dependency-backed.
  2. For motifs or restriction enzymes, read references/motif-restriction-reference.md before writing code.
  3. For clustering, phenotype, GenePop, graphics, protein-analysis, or long-tail modules, read references/specialized-workflows.md.
  4. If an import, format, optional dependency, JASPAR, graphics, fitting, or clustering error appears, read references/troubleshooting.md.
  5. When validating an installed Biopython environment for this sub-skill, run the offline smoke check:
python scripts/specialized_modules_smoke.py

The smoke script uses only tiny in-memory data and avoids network calls, credentials, graphics rendering, database servers, and original repository files.

Operating cautions

  • Treat Bio.Graphics, ReportLab bitmap rendering, JASPAR SQL database access, phenotype curve fitting, and external/web tools as optional until explicitly available.
  • Prefer deterministic clustering examples by supplying initialid for k-means/k-medians/k-medoids or by using hierarchical clustering/distance matrices.
  • Keep motif read() for exactly-one-motif inputs and parse() for multi-motif inputs.
  • Restriction cut locations are biological one-based cut positions; do not reinterpret them as zero-based Python slice offsets without documenting the conversion.
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