Translation Diff Export
Devolutions/UniGetUI
Compares UniGetUI JSON locale files against English, identifies untranslated or source-changed keys, and generates patch, reference, and handoff files for a target language.
Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions.
Install Bio Ribo Seq Translation Efficiency
$ npx skills add GPTomics/bioSkills --skill bio-ribo-seq-translation-efficiency -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-ribo-seq-translation-efficiency --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ribo-seq/translation-efficiency .claude/skills/bio-ribo-seq-translation-efficiency && rm -rf skills-srcUse ~/.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/
Install the "bio-ribo-seq-translation-efficiency" agent skill from https://github.com/GPTomics/bioSkills/tree/main/ribo-seq/translation-efficiency into .claude/skills/bio-ribo-seq-translation-efficiency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-ribo-seq-translation-efficiency", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GPTomics/bioSkills/tree/main/ribo-seq/translation-efficiencyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GPTomics/bioSkills --skill bio-ribo-seq-translation-efficiency -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-ribo-seq-translation-efficiency --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/ribo-seq/translation-efficiency .agents/skills/bio-ribo-seq-translation-efficiency && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bio-ribo-seq-translation-efficiency" agent skill from https://github.com/GPTomics/bioSkills/tree/main/ribo-seq/translation-efficiency into .agents/skills/bio-ribo-seq-translation-efficiency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-ribo-seq-translation-efficiency", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-ribo-seq-translation-efficiency -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-ribo-seq-translation-efficiency --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/ribo-seq/translation-efficiency .cursor/skills/bio-ribo-seq-translation-efficiency && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bio-ribo-seq-translation-efficiency" agent skill from https://github.com/GPTomics/bioSkills/tree/main/ribo-seq/translation-efficiency into .cursor/skills/bio-ribo-seq-translation-efficiency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-ribo-seq-translation-efficiency", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GPTomics/bioSkills.git --path ribo-seq/translation-efficiency--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GPTomics/bioSkills --skill bio-ribo-seq-translation-efficiency -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-ribo-seq-translation-efficiency --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/ribo-seq/translation-efficiency .gemini/skills/bio-ribo-seq-translation-efficiency && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bio-ribo-seq-translation-efficiency" agent skill from https://github.com/GPTomics/bioSkills/tree/main/ribo-seq/translation-efficiency into .gemini/skills/bio-ribo-seq-translation-efficiency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-ribo-seq-translation-efficiency", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GPTomics/bioSkills bio-ribo-seq-translation-efficiencyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GPTomics/bioSkills --skill bio-ribo-seq-translation-efficiency -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/ribo-seq/translation-efficiency .github/skills/bio-ribo-seq-translation-efficiency && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bio-ribo-seq-translation-efficiency" agent skill from https://github.com/GPTomics/bioSkills/tree/main/ribo-seq/translation-efficiency into .github/skills/bio-ribo-seq-translation-efficiency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-ribo-seq-translation-efficiency", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-ribo-seq-translation-efficiency -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GPTomics/bioSkills bio-ribo-seq-translation-efficiency --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/ribo-seq/translation-efficiency .opencode/skills/bio-ribo-seq-translation-efficiency && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bio-ribo-seq-translation-efficiency" agent skill from https://github.com/GPTomics/bioSkills/tree/main/ribo-seq/translation-efficiency into .opencode/skills/bio-ribo-seq-translation-efficiency/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-ribo-seq-translation-efficiency", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
bio-ribo-seq-translation-efficiency- GitHub stars
- 1.2k
- Used in
- 1 other repo
- Token cost
- ~2.5k tokens
- SKILL.md length
- 973 words
- Files
- 3
- Skills in repo
- 559
- Repo updated
- First seen
- Licence
- MIT
At a glance
Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions.
- Separating translational from transcriptional regulation
- SKILL.md covers Version Compatibility, The central trap: a ratio is…, Mode of regulation: control vs… and Differential-TE tool selection, plus 8 more sections
- Runs Python scripts from its folder; calls pip
- Distinguishing genuine translational control from buffering
What it does
Bio Ribo Seq Translation Efficiency is an agent skill from GPTomics/bioSkills. Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions. Use when separating translational from transcriptional regulation, distinguishing genuine translational control from buffering, or choosing between riborex, Xtail, anota2seq, and DESeq2 interaction models.
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/calculate_te.py` and `usage-guide.md`).
It sits in Writing & Content, covering Translation. 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
- Separating translational from transcriptional regulation
- Distinguishing genuine translational control from buffering
- Choosing between riborex
- DESeq2 interaction models
Example prompts
- “/bio-ribo-seq-translation-efficiency”
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
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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 Ribo Seq Translation Efficiency loads about 2.5k tokens when it runs. Until then it costs about 94 tokens; SKILL.md has 973 words of instructions outside code blocks.
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). 973 words, ~2,493 tokens.
Download SKILL.mdSave it as
.claude/skills/bio-ribo-seq-translation-efficiency/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- bio-ribo-seq-translation-efficiency
- description
- Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions. Use when separating translational from transcriptional regulation, distinguishing genuine translational control from buffering, or choosing between riborex, Xtail, anota2seq, and DESeq2 interaction models.
- tool_type
- mixed
- primary_tool
- riborex
Version Compatibility
Reference examples tested with: riborex 2.4+, xtail 1.1+, anota2seq 1.24+, DESeq2 1.42+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>')then?function_nameto verify parameters - Python:
pip show <package>thenhelp(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.
Translation Efficiency
"Calculate translation efficiency from my Ribo-seq and RNA-seq" -> Compute footprint density relative to mRNA density per gene and test which genes change translation independently of transcription, distinguishing real translational control from buffering.
- R:
riborex(DESeq2/edgeR backend),Xtail, oranota2seqfor differential TE - Python: per-gene TE ratio for ranking/visualization only
TE = RPF density / mRNA density over the SAME region. Both assays must come from matched samples and be counted over the CDS. TE isolates translational regulation and is a relative translation-rate proxy at steady state; occupancy is not protein output.
The central trap: a ratio is for ranking, not testing
The naive per-gene ratio (TPM_ribo/TPM_rna) is fine for ranking and plots but WRONG for differential testing: it ignores count heteroskedasticity, treating a gene with 5 reads like one with 5000. Differential TE is NOT "compute TE per condition then test the difference" -- it is a CONDITION x ASSAY INTERACTION on raw counts with proper negative-binomial dispersion modeling, where log2FC(TE) = log2FC(RPF) - log2FC(mRNA). The whole differential-TE field exists to do this interaction correctly.
Mode of regulation: control vs buffering
When both assays move, there are distinct biological modes that a single TE fold-change cannot separate:
| Mode | RPF | mRNA | TE | Meaning |
|---|---|---|---|---|
| mRNA abundance | up | up | ~flat | transcriptional, not translational |
| translation (forwarded) | up | flat | up | genuine translational control -> protein changes |
| buffering | flat | up | down | translation absorbs the mRNA change, protein held constant |
Buffering (a homeostatic mechanism) and genuine translational activation can produce the SAME |log2FC(TE)|. Calling a buffered gene "translationally activated" is a wrong conclusion. Only anota2seq formally names the mode, by regressing translated mRNA on total mRNA (analysis of partial variance).
Differential-TE tool selection
| Tool | Statistic | Names buffering | Best when |
|---|---|---|---|
| riborex | wraps DESeq2/edgeR/Voom on a merged interaction design | no | fast drop-in for DESeq2 users |
| Xtail | two pipelines (FC-vs-FC, ratio-vs-ratio), reports the more conservative | partial (won't call a buffered gene a hit) | conservative differential-TE calls + clean plots |
| anota2seq | per-mRNA APV + random variance model | YES | mode-of-regulation biology; the postdoc-grade choice |
| RiboDiff | NB GLM, shared dispersion by default | no | few replicates; CLI pipeline |
| DESeq2 interaction | ~assay+condition+assay:condition, Wald or LRT | no (post-hoc) | full control, custom contrasts, batch terms |
Quick per-gene TE (ranking screen only)
Goal: Rank genes by TE for a quick look, not for inference.
Approach: Normalize both assays, take the log2 ratio over the CDS with a pseudocount.
import numpy as np
def log2_te(ribo_cds_tpm, rna_cds_tpm, pseudocount=0.1):
'''Per-gene log2 TE for ranking/plots. Both inputs counted over the CDS.
Pseudocount 0.1 TPM avoids log(0) and dampens low-count noise. Not for testing.
'''
return np.log2((ribo_cds_tpm + pseudocount) / (rna_cds_tpm + pseudocount))Count BOTH assays over the CDS. Using full-transcript RNA against CDS-only RPF introduces a UTR-length confound (long-UTR genes look low-TE). Exclude the first ~15 and last ~5 codons of the CDS so initiation and termination peaks do not dominate the RPF count.
Differential TE with riborex
Goal: Test differential TE reusing a familiar DE engine.
Approach: Pass CDS count matrices and condition vectors; riborex builds the interaction design internally and returns DESeq2-format results.
library(riborex)
# rna_counts / ribo_counts: genes x samples integer CDS counts
res <- riborex(rnaCntTable = rna_counts, riboCntTable = ribo_counts,
rnaCond = c("ctrl", "ctrl", "treat", "treat"),
riboCond = c("ctrl", "ctrl", "treat", "treat"),
engine = "DESeq2")
sig <- res[which(res$padj < 0.05), ] # log2FoldChange is the TE changeEngines are "DESeq2" (default), "edgeR", "edgeRD", "Voom" (Voom is single-factor only).
Differential TE with anota2seq (names the mode)
Goal: Separate translation, buffering, and mRNA-abundance regulation.
Approach: Provide translated (RPF) and total (RNA) matrices, run the pipeline, then classify each gene's mode.
library(anota2seq)
ads <- anota2seqDataSetFromMatrix(dataP = ribo_counts, dataT = rna_counts,
phenoVec = c("ctrl", "ctrl", "treat", "treat"),
dataType = "RNAseq", normalize = TRUE)
ads <- anota2seqRun(ads, useRVM = TRUE)
ads <- anota2seqRegModes(ads) # one mode per gene: translation > abundance > buffering
translation_hits <- anota2seqGetOutput(ads, analysis = "translation",
output = "selected", selContrast = 1)Show full SKILL.md (409 more words)Show less
Differential TE with a DESeq2 interaction
Goal: Full control over the interaction model.
Approach: Merge RPF and RNA counts, fit the interaction, and select the interaction coefficient by name from resultsNames (never hardcode it).
library(DESeq2)
counts <- cbind(ribo_counts, rna_counts)
coldata <- data.frame(
condition = factor(rep(c("ctrl", "ctrl", "treat", "treat"), 2)),
assay = factor(rep(c("ribo", "rna"), each = 4)))
dds <- DESeqDataSetFromMatrix(counts, coldata, ~ assay + condition + assay:condition)
dds <- DESeq(dds)
# The interaction name is auto-generated from factor levels; pick it programmatically.
# DESeq2 renders interaction coefficients with a DOT (e.g. assayrna.conditiontreat),
# while main effects use underscores -- so match the dot, not the formula's colon.
nm <- grep("\\.", resultsNames(dds), value = TRUE)
res_te <- results(dds, name = nm)Size factors are estimated PER ASSAY (ribo among ribo, RNA among RNA); the implicit assumption is that the median gene's TE is unchanged. If a global translational shift is expected (e.g. mTOR inhibition), median normalization is violated and spike-ins are needed to anchor absolute scale.
Confounders to check
mRNA isoform switching changes the CDS/UTR counting region between conditions; UTR changes that alter uORF usage can make a main-ORF TE change SECONDARY to uORF regulation rather than direct translational control. Cross-check called ORFs and uORFs (see orf-detection) before attributing a TE shift to the main ORF.
Common Errors
| Symptom | Cause | Fix |
|---|---|---|
| Low-count genes dominate the hit list | t-test/ratio on log-TE | Use count-based GLM (riborex/Xtail/anota2seq/DESeq2) |
| Long-UTR genes systematically low TE | RNA counted over full transcript, RPF over CDS | Count both over the CDS |
results(dds, name='conditiontreat.assayribo') errors | Hardcoded interaction name | Select from resultsNames(dds) by the "." term (interaction coefficients render with a dot, not the formula's colon) |
| Unstable dispersion or anota2seq RVM warnings | Too few replicates (n=2 as in the examples) | Use >=3 replicates per condition per assay; n=2 is illustrative only |
| Buffered gene reported as translationally activated | Single TE fold-change cannot separate modes | Use anota2seq mode-of-regulation |
| TE shifts vanish or invert globally | Global translational change breaks median normalization | Add spike-ins; do not assume median TE unchanged |
| Initiation peak inflates RPF counts | Whole-CDS counting including start/stop peaks | Trim first ~15 / last ~5 codons |
Related Skills
- ribosome-periodicity - Calibrate P-site offsets for CDS footprint counts
- orf-detection - Rule out uORF-driven (secondary) TE changes
- rna-quantification/featurecounts-counting - Generate matched RNA-seq CDS counts
- differential-expression/deseq2-basics - Count-based DE foundations
References
- Li W, Wang W, Uren PJ, Penalva LOF, Smith AD. 2017. Riborex: fast and flexible identification of differential translation from Ribo-seq data. Bioinformatics 33(11):1735-1737. doi:10.1093/bioinformatics/btx047
- Xiao Z, Zou Q, Liu Y, Yang X. 2016. Genome-wide assessment of differential translations with ribosome profiling data. Nat Commun 7:11194. doi:10.1038/ncomms11194
- Oertlin C, Lorent J, Murie C, Furic L, Topisirovic I, Larsson O. 2019. Generally applicable transcriptome-wide analysis of translation using anota2seq. Nucleic Acids Res 47(12):e70. doi:10.1093/nar/gkz223
- Zhong Y, Karaletsos T, Drewe P, et al. 2017. RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints. Bioinformatics 33(1):139-141. doi:10.1093/bioinformatics/btw585
- Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. doi:10.1186/s13059-014-0550-8
© 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 2 other files in ribo-seq/translation-efficiency of GPTomics/bioSkills.
- SKILL.md
- examples/calculate_te.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.
Compare with similar skills
Bio Ribo Seq Translation Efficiency next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bio Ribo Seq Translation Efficiency this skillGPTomics/bioSkills | 1.2k | 1 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Translation Diff ExportDevolutions/UniGetUI | 26k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Sync Translationssymfony/symfony | 31k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Translation Diff ImportDevolutions/UniGetUI | 26k | — | ~750 | Automated safety check: Pass | MIT | |
| Translation Diff TranslateDevolutions/UniGetUI | 26k | — | ~934 | Automated safety check: Pass | MIT | |
| Generate Translationspayloadcms/payload | 45k | — | ~1.1k | Automated safety check: Pass | MIT |
Similar skills
Sync Translations
symfony/symfony
Synchronize translation catalogs across maintained Symfony branches: find messages that newer branches added to the English catalogs but that are still missing from the oldest maintained branch…
Translation Diff Import
Devolutions/UniGetUI
Merges translated key-value pairs from a UniGetUI JSON localization patch back into the full language file and validates the merged result.
Translation Diff Translate
Devolutions/UniGetUI
Translates a sparse UniGetUI JSON language patch, writes completed entries into the working copy, preserves placeholders and terminology, and prepares the patch for merge-back.
Generate Translations
payloadcms/payload
A skill your agent uses when new translation keys are added to packages to generate new translations strings
InkOS Creative Harness
Narcooo/inkos
Drives long-form fiction, scripts, storyboards, interactive films and long-document translation through InkOS, with every change made by a typed action.
More from GPTomics/bioSkills
All 559 skills in this repoBio Alignment Io
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
bioSkills Installer
GPTomics/bioSkills
Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.
Bio Write Sequences
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
Amplicon Primer Clipping
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
Alignment Filtering with samtools and pysam
GPTomics/bioSkills
Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.
Bio Alignment Indexing
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Related
Categories
Questions about Bio Ribo Seq Translation Efficiency
What does Bio Ribo Seq Translation Efficiency do?
Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions. Bio Ribo Seq Translation Efficiency is an agent skill from GPTomics/bioSkills. Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions.
When should I use Bio Ribo Seq Translation Efficiency?
Bio Ribo Seq Translation Efficiency fits situations like: separating translational from transcriptional regulation; distinguishing genuine translational control from buffering; choosing between riborex; DESeq2 interaction models.
How do I install Bio Ribo Seq Translation Efficiency in Claude Code?
Run `npx skills add GPTomics/bioSkills --skill bio-ribo-seq-translation-efficiency -a claude-code`. Or copy the skill folder (ribo-seq/translation-efficiency in GPTomics/bioSkills) into .claude/skills/bio-ribo-seq-translation-efficiency in your project. Claude Code loads it when a task matches its description.
How do I install Bio Ribo Seq Translation Efficiency in Codex?
Run `npx skills add GPTomics/bioSkills --skill bio-ribo-seq-translation-efficiency -a codex`. Or copy the skill folder (ribo-seq/translation-efficiency in GPTomics/bioSkills) into .agents/skills/bio-ribo-seq-translation-efficiency in your project. Codex loads it when a task matches its description.
Can I use Bio Ribo Seq Translation Efficiency 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-ribo-seq-translation-efficiency -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-ribo-seq-translation-efficiency, .gemini/skills/bio-ribo-seq-translation-efficiency, .github/skills/bio-ribo-seq-translation-efficiency and .opencode/skills/bio-ribo-seq-translation-efficiency in your project.
What does Bio Ribo Seq Translation Efficiency need to run?
Going by SKILL.md and its folder, Bio Ribo Seq Translation Efficiency needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Bio Ribo Seq Translation Efficiency access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Bio Ribo Seq Translation Efficiency 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 Ribo Seq Translation Efficiency use?
Bio Ribo Seq Translation Efficiency 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 Ribo Seq Translation Efficiency use?
About 2.5k tokens (SKILL.md is roughly 10k 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 Ribo Seq Translation Efficiency?
Skills that share tags, products or a category with Bio Ribo Seq Translation Efficiency: Translation Diff Export (Devolutions/UniGetUI, 26k stars), Sync Translations (symfony/symfony, 31k stars), Translation Diff Import (Devolutions/UniGetUI, 26k stars) and Translation Diff Translate (Devolutions/UniGetUI, 26k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Bio Ribo Seq Translation Efficiency?
GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,217 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.