Agent skill

Bio Transcription Translation

by GPTomics in GPTomics/bioSkills

Transcribe DNA to RNA and translate to protein using Biopython, with NCBI codon-table selection, CDS validation, and six-frame ORF finding.

MITAuto-check passedMedia & Creative

Install Bio Transcription Translation

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-transcription-translation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-transcription-translation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/sequence-manipulation/transcription-translation .claude/skills/bio-transcription-translation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-transcription-translation
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.4k tokens
SKILL.md length
1,231 words
Files
5
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Transcribe DNA to RNA and translate to protein using Biopython, with NCBI codon-table selection, CDS validation, and six-frame ORF finding.

  • Converting a CDS
  • SKILL.md covers Version Compatibility, The Governing Principle, Required Import and Transcription Is a String…, plus 12 more sections
  • Runs Python scripts from its folder; calls pip
  • ORF to its amino-acid sequence

What it does

Bio Transcription Translation is an agent skill from GPTomics/bioSkills. Transcribe DNA to RNA and translate to protein using Biopython, with NCBI codon-table selection, CDS validation, and six-frame ORF finding. Use when converting a CDS or ORF to its amino-acid sequence, selecting a non-standard (mitochondrial, bacterial, ciliate) genetic code, validating a coding sequence, or scanning all reading frames.

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `examples/basic_conversion.py`, `examples/codon_tables.py` and `examples/orf_finding.py`).

It sits in Media & Creative, covering Transcription, Translation and Bioinformatics. It works with Biopython and NCBI. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Converting a CDS
  • ORF to its amino-acid sequence
  • Selecting a non-standard (mitochondrial
  • Ciliate) genetic code

Example prompts

  • “/bio-transcription-translation”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • ncbi.nlm.nih.gov

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Transcription Translation loads about 3.4k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 1,231 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,231 words, ~3,392 tokens.

Download SKILL.mdSave it as .claude/skills/bio-transcription-translation/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
bio-transcription-translation
description
Transcribe DNA to RNA and translate to protein using Biopython, with NCBI codon-table selection, CDS validation, and six-frame ORF finding. Use when converting a CDS or ORF to its amino-acid sequence, selecting a non-standard (mitochondrial, bacterial, ciliate) genetic code, validating a coding sequence, or scanning all reading frames.
tool_type
python
primary_tool
Bio.Seq

Version Compatibility

Reference examples tested with: BioPython 1.83+

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Transcription and Translation

"Translate my DNA sequence to protein" -> Transcribe DNA to RNA and translate to protein, choosing the right genetic code and validating the reading frame.

  • Python: Seq.translate(), Seq.transcribe(), Bio.Data.CodonTable (BioPython)

The Governing Principle

The single most dangerous bug in translation is silent: a valid-but-wrong table= argument produces a plausible wrong protein with no error. Translating human mitochondrial DNA with the default Standard code (table 1) inserts * where UGA actually codes Trp and truncates at AGA/AGG (which are stops in vertebrate mito). The protein looks real and nothing complains. By contrast, an unknown table id or name raises KeyError (loud). Only valid-but-wrong tables corrupt silently.

The defense: when the input is a complete coding sequence, pass cds=True. It converts silent traps into loud TranslationError exceptions by validating start, length, and stop. Use it for ORF validation rather than trusting a clean-looking output.

Since the Biopython 1.78 alphabet removal, transcribe(), back_transcribe(), and translate() perform NO type checking. Transcribing a protein or translating the wrong strand returns silent garbage. Confirm the molecule and strand before converting.

Required Import

python
from Bio.Seq import Seq
from Bio.Data import CodonTable

Transcription Is a String Operation, Not Biology

transcribe() is a pure T->U replacement on the coding (sense) strand; back_transcribe() is U->T. Neither performs splicing, intron removal, 5' capping, or poly-A addition. The Biopython tutorial states plainly that all transcribe does is replace T with U.

python
coding_dna = Seq('ATGCGATCGATCG')
rna = coding_dna.transcribe()        # Seq('AUGCGAUCGAUCG'), T->U only
back = rna.back_transcribe()         # Seq('ATGCGATCGATCG'), U->T only

True biological transcription starts from the template strand, so reverse-complement first:

python
template = Seq('CGATCGATCGCAT')
mrna = template.reverse_complement().transcribe()

Translation accepts DNA or RNA directly, so explicit transcription is rarely needed before translate().

Translation Basics

python
coding_dna = Seq('ATGTTTGGT')
coding_dna.translate()               # Seq('MFG'), from DNA
Seq('AUGUUUGGU').translate()         # Seq('MFG'), from RNA
Stop-Codon Behavior

translate() substitutes stop_symbol (default '*') for EVERY in-frame stop, so internal stops appear as * mid-protein. to_stop=True instead halts at the first in-frame stop and does NOT append the symbol.

python
seq = Seq('ATGTTTGGTTAAGGG')
seq.translate()                      # Seq('MFG*G'), stop shown, translation continues
seq.translate(to_stop=True)          # Seq('MFG'), halts at first stop

NCBI Codon Tables: Which Code, and the Consequence of the Wrong One

Biopython exposes every NCBI genetic code by integer id or registered name. Selecting the wrong one is the #1 silent bug (see governing principle).

IDNameKey reassignments vs StandardWhen it matters
1Standardnone (baseline)Most nuclear genes
2Vertebrate MitochondrialAGA/AGG -> STOP; AUA -> Met; UGA -> TrpHuman/vertebrate mtDNA (4 stops: UAA, UAG, AGA, AGG)
3Yeast MitochondrialCUN (all four CU*) -> Thr; AUA -> Met; UGA -> TrpCTG -> Thr lives HERE, not table 12
4Mold/Protozoan Mito + Mycoplasma/SpiroplasmaUGA -> Trp (only change)Fungal/protozoan mito; Mycoplasma
5Invertebrate MitochondrialAGA/AGG -> Ser; AUA -> Met; UGA -> TrpInsect/worm mito (AGA/AGG=Ser, not STOP as in table 2)
6Ciliate NuclearUAA/UAG -> Gln; only UGA stays stopTetrahymena, Paramecium (single stop)
11Bacterial/Archaeal/Plastidsame coding as Standard; expanded startsProkaryotes, plastids (differs from 1 mainly in initiation)
12Alternative Yeast NuclearCUG -> Ser (from Leu)Candida CUG-Ser clade

Explicit correction: CTG -> Thr is table 3 (Yeast Mitochondrial). Table 12 is CUG -> Ser. Do not conflate them.

python
seq = Seq('ATGGCCTGA')
seq.translate(table=2)                            # by NCBI integer id
seq.translate(table='Vertebrate Mitochondrial')   # by registered name

CodonTable.unambiguous_dna_by_id[2]
CodonTable.unambiguous_dna_by_name['Vertebrate Mitochondrial']

Validating a Coding Sequence with cds=True

Goal: Translate a complete ORF and have any structural defect raise a loud error instead of producing a silent wrong protein.

Approach: Pass cds=True. It enforces four conditions, each raising Bio.Data.CodonTable.TranslationError on failure: (1) first codon is a start codon for the chosen table; (2) length is a multiple of 3; (3) sequence ends in a stop; (4) no internal in-frame stop. A valid alternative start (GTG/TTG/ATT) is translated as M, biologically correct for fMet initiation. The terminal stop is stripped from the output.

Reference (BioPython 1.83+):

python
cds = Seq('ATGTTTGGTTAA')
cds.translate(cds=True)              # Seq('MFG'), validated, terminal stop removed

alt_start = Seq('GTGTTTGGTTAA')
alt_start.translate(table=11, cds=True)   # Seq('MFG'), GTG start -> M under bacterial code

Start-codon lists differ by table: table 1 = TTG/CTG/ATG; table 2 = ATT/ATC/ATA/ATG/GTG; table 11 = TTG/CTG/ATT/ATC/ATA/ATG/GTG. A start valid under one table fails under another, which is exactly the loud signal cds=True provides.

translate() Parameters and Edge Cases

Signature (the Seq.translate METHOD): translate(table='Standard', stop_symbol='*', to_stop=False, cds=False, gap='-'). The Seq method defaults to gap='-', while both the module-level Bio.Seq.translate(sequence, ...) function and SeqRecord.translate() default to gap=None.

  • Partial codon (length not a multiple of 3): emits a BiopythonWarning and SILENTLY drops the trailing 1-2 bases. Easy to miss in a pipeline. Under cds=True the same condition becomes a loud TranslationError.
  • Gaps: because the Seq method defaults to gap='-', a full gap codon '---' already translates to '-' (e.g. Seq('GTG---GCCATT').translate() -> 'V-AI', no error). A codon mixing gaps and bases ('TT-') raises TranslationError. SeqRecord.translate() instead defaults to gap=None, so even a full '---' codon raises unless gap='-' is passed; mixed gap/base codons still raise. For alignment-derived CDS, preserve codon and alignment semantics with alignment/multiple-alignment or the project's established translation wrapper rather than assuming one gap-normalization policy.
  • Dual-coding stop tables (27 Karyorelict, 28 Condylostoma, 31 Blastocrithidia Nuclear): these reassign a stop codon so it codes both an amino acid and stop, so to_stop=True raises a ValueError (no single truncation point).
python
Seq('ATGTTTGG').translate()          # BiopythonWarning, trailing 'GG' dropped -> Seq('MF')
Seq('ATGTTTGG').translate(cds=True)  # TranslationError: length not a multiple of three
Show full SKILL.md (434 more words)Show less

Selenocysteine and Pyrrolysine Are Silently Lost

Selenocysteine (Sec, one-letter U) is encoded by UGA and pyrrolysine (Pyl, one-letter O) by UAG, both normally stop codons. Recoding requires a SECIS (Sec) or PYLIS (Pyl) element that Biopython does NOT detect. No NCBI table maps UGA->U or UAG->O. Naive translation therefore yields * mid-protein, and to_stop=True SILENTLY truncates the protein at that position. Real selenoproteins (GPX, TXNRD, SELENOP) come out truncated or peppered with *. There is no clean Biopython workaround; flag these genes and handle the recoding event manually.

Six-Frame Translation

Goal: Translate a DNA sequence in all six frames (three forward, three reverse) to expose every possible protein product.

Approach: For each strand, offset by 0, 1, 2 bases, trim to a multiple of 3, and translate.

Reference (BioPython 1.83+):

python
def six_frame_translation(seq):
    frames = []
    for strand, s in [('+', seq), ('-', seq.reverse_complement())]:
        for frame in range(3):
            length = 3 * ((len(s) - frame) // 3)
            fragment = s[frame:frame + length]
            frames.append((strand, frame, fragment.translate()))
    return frames

seq = Seq('ATGCGATCGATCGATCGATCG')
for strand, frame, protein in six_frame_translation(seq):
    print(f'{strand}{frame}: {protein}')

Find All ORFs (Start to Stop)

Goal: Identify all open reading frames (Met to stop) across both strands and all three frames, keeping only those above a minimum length.

Approach: Translate each of the six frames, then scan each translation for Met-to-stop segments meeting the threshold.

Reference (BioPython 1.83+):

python
def find_orfs(seq, min_protein_length=30):
    orfs = []
    for strand, s in [('+', seq), ('-', seq.reverse_complement())]:
        for frame in range(3):
            end = frame + 3 * ((len(s) - frame) // 3)
            trans = str(s[frame:end].translate())
            aa_start = 0
            while True:
                start = trans.find('M', aa_start)
                if start == -1:
                    break
                stop = trans.find('*', start)
                if stop == -1:
                    stop = len(trans)
                orf = trans[start:stop]
                if len(orf) >= min_protein_length:
                    orfs.append((strand, frame, start * 3 + frame, orf))
                aa_start = start + 1
    return orfs

seq = Seq('ATGCGATCGATCGATCGATCGTAA')
for strand, frame, pos, orf in find_orfs(seq, min_protein_length=3):
    print(f'{strand} frame {frame} pos {pos}: {orf}')

Inspect a Codon Table

python
table = CodonTable.unambiguous_dna_by_id[2]
table.start_codons                   # ['ATT', 'ATC', 'ATA', 'ATG', 'GTG']
table.stop_codons                    # ['TAA', 'TAG', 'AGA', 'AGG']
table.forward_table['TGA']           # 'W' under vertebrate mito code

Common Errors

SymptomCauseFix
Plausible protein, wrong residues, no errorValid-but-wrong table= (e.g. mito DNA on table 1)Select the organism's NCBI table; use cds=True to validate
* mid-protein or premature truncationSelenoprotein/pyrrolysine UGA/UAG, or wrong table where UGA=TrpUse correct mito table for UGA=Trp; Sec/Pyl recoding is not automatic
TranslationError: First codon ... is not a start codoncds=True on a sequence not starting at a valid start for that tableTrim to the true start, or pick the table whose starts include it
TranslationError: ... is not a multiple of threecds=True on a partial CDSTrim to a full ORF; without cds=True this only warns and drops trailing bases
TranslationError: Extra in frame stop codon foundInternal stop under cds=TrueWrong frame, wrong table, or genuine internal stop; re-check frame/table
Garbage protein from a protein inputtranscribe()/translate() on a non-nucleotide Seq (no type checks since 1.78)Verify molecule type before converting
KeyErrorUnknown table id or nameUse a valid NCBI id (1-6, 9-16, 21-31) or registered name

Decision Tree

Need to convert a sequence?
├── DNA <-> RNA (string-level T<->U)?
│   ├── coding strand to RNA -> seq.transcribe()
│   ├── RNA back to DNA      -> seq.back_transcribe()
│   └── template strand to mRNA -> seq.reverse_complement().transcribe()
├── DNA/RNA to protein?
│   ├── alignment-derived CDS    -> alignment/multiple-alignment (codon-aware), or project wrapper
│   ├── complete CDS to validate -> translate(cds=True) [loud on defects]
│   ├── stop at first stop only  -> translate(to_stop=True)
│   ├── non-standard organism    -> translate(table=N)  [pick from the table above]
│   └── show internal stops      -> translate()  [* per stop]
└── Unknown coding regions? -> six-frame translation, then scan M...* for ORFs
  • seq-objects - Create and inspect Seq objects before translation
  • reverse-complement - Strand handling for six-frame translation and template-strand transcription
  • codon-usage - Analyze codon bias and adaptation in coding sequences
  • sequence-io/read-sequences - Parse GenBank/FASTA records and CDS features for translation

References

The genetic-code tables and their organism assignments follow the NCBI Taxonomy "The Genetic Codes" page, compiled by Andrzej (Anjay) Elzanowski and Jim Ostell at NCBI (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi). This is a maintained web resource; cite it as the NCBI page rather than as a journal article.

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in sequence-manipulation/transcription-translation of GPTomics/bioSkills.

  • SKILL.md
  • examples/basic_conversion.py
  • examples/codon_tables.py
  • examples/orf_finding.py
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

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Works with

Questions about Bio Transcription Translation

What does Bio Transcription Translation do?

Transcribe DNA to RNA and translate to protein using Biopython, with NCBI codon-table selection, CDS validation, and six-frame ORF finding. Bio Transcription Translation is an agent skill from GPTomics/bioSkills. Transcribe DNA to RNA and translate to protein using Biopython, with NCBI codon-table selection, CDS validation, and six-frame ORF finding.

When should I use Bio Transcription Translation?

Bio Transcription Translation fits situations like: converting a CDS; ORF to its amino-acid sequence; selecting a non-standard (mitochondrial; ciliate) genetic code.

How do I install Bio Transcription Translation in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-transcription-translation -a claude-code`. Or copy the skill folder (sequence-manipulation/transcription-translation in GPTomics/bioSkills) into .claude/skills/bio-transcription-translation in your project. Claude Code loads it when a task matches its description.

How do I install Bio Transcription Translation in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-transcription-translation -a codex`. Or copy the skill folder (sequence-manipulation/transcription-translation in GPTomics/bioSkills) into .agents/skills/bio-transcription-translation in your project. Codex loads it when a task matches its description.

Can I use Bio Transcription Translation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-transcription-translation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-transcription-translation, .gemini/skills/bio-transcription-translation, .github/skills/bio-transcription-translation and .opencode/skills/bio-transcription-translation in your project.

What does Bio Transcription Translation need to run?

Going by SKILL.md and its folder, Bio Transcription Translation needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Bio Transcription Translation access the network?

SKILL.md names 1 domain. As links in the text: ncbi.nlm.nih.gov. This is read from the text; nothing was executed.

Is Bio Transcription Translation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Transcription Translation use?

Bio Transcription Translation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Transcription Translation use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Transcription Translation?

Skills that share tags, products or a category with Bio Transcription Translation: Youtube Publish (Andonywang123/Epost, 197 stars), Video Translation (NoizAI/skills, 526 stars), Asc Localize Metadata (CamilleScholtz/swmpc, 239 stars) and Edu Chem Video (wy51ai/edulab, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Transcription Translation?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.