Alphagenome Single Variant Analysis
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
Aligns bisulfite-converted (WGBS, RRBS, PBAT) and enzymatic (EM-seq) short reads to an in-silico C-T/G-A-converted reference with Bismark (Bowtie2 or HISAT2), preparing the genome index, choosing…
$ npx skills add GPTomics/bioSkills --skill bio-methylation-bismark-alignment -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-methylation-bismark-alignment --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/methylation-analysis/bismark-alignment .claude/skills/bio-methylation-bismark-alignment && 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-methylation-bismark-alignment" agent skill from https://github.com/GPTomics/bioSkills/tree/main/methylation-analysis/bismark-alignment into .claude/skills/bio-methylation-bismark-alignment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-methylation-bismark-alignment", 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/methylation-analysis/bismark-alignmentType 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-methylation-bismark-alignment -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-methylation-bismark-alignment --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/methylation-analysis/bismark-alignment .agents/skills/bio-methylation-bismark-alignment && 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-methylation-bismark-alignment" agent skill from https://github.com/GPTomics/bioSkills/tree/main/methylation-analysis/bismark-alignment into .agents/skills/bio-methylation-bismark-alignment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-methylation-bismark-alignment", 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-methylation-bismark-alignment -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-methylation-bismark-alignment --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/methylation-analysis/bismark-alignment .cursor/skills/bio-methylation-bismark-alignment && 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-methylation-bismark-alignment" agent skill from https://github.com/GPTomics/bioSkills/tree/main/methylation-analysis/bismark-alignment into .cursor/skills/bio-methylation-bismark-alignment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-methylation-bismark-alignment", 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 methylation-analysis/bismark-alignment--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-methylation-bismark-alignment -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-methylation-bismark-alignment --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/methylation-analysis/bismark-alignment .gemini/skills/bio-methylation-bismark-alignment && 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-methylation-bismark-alignment" agent skill from https://github.com/GPTomics/bioSkills/tree/main/methylation-analysis/bismark-alignment into .gemini/skills/bio-methylation-bismark-alignment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-methylation-bismark-alignment", 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-methylation-bismark-alignmentInstalls 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-methylation-bismark-alignment -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/methylation-analysis/bismark-alignment .github/skills/bio-methylation-bismark-alignment && 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-methylation-bismark-alignment" agent skill from https://github.com/GPTomics/bioSkills/tree/main/methylation-analysis/bismark-alignment into .github/skills/bio-methylation-bismark-alignment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-methylation-bismark-alignment", 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-methylation-bismark-alignment -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-methylation-bismark-alignment --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/methylation-analysis/bismark-alignment .opencode/skills/bio-methylation-bismark-alignment && 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-methylation-bismark-alignment" agent skill from https://github.com/GPTomics/bioSkills/tree/main/methylation-analysis/bismark-alignment into .opencode/skills/bio-methylation-bismark-alignment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-methylation-bismark-alignment", 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.
bio-methylation-bismark-alignmentAligns bisulfite-converted (WGBS, RRBS, PBAT) and enzymatic (EM-seq) short reads to an in-silico C-T/G-A-converted reference with Bismark (Bowtie2 or HISAT2), preparing the genome index, choosing…
Bio Methylation Bismark Alignment is an agent skill from GPTomics/bioSkills. Aligns bisulfite-converted (WGBS, RRBS, PBAT) and enzymatic (EM-seq) short reads to an in-silico C-T/G-A-converted reference with Bismark (Bowtie2 or HISAT2), preparing the genome index, choosing the directional vs non-directional vs PBAT strand flag, deduplicating WGBS/EM-seq (never RRBS), and bounding bisulfite conversion efficiency with unmethylated lambda and methylated pUC19 spike-ins. Covers why the library protocol (not the aligner) decides whether calls are meaningful, why incomplete conversion…
Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/bismark_basic.sh` and `usage-guide.md`).
It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.
2 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.
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.
Ships script files (Shell), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Bio Methylation Bismark Alignment loads about 4.8k tokens when it runs. Until then it costs about 238 tokens; SKILL.md has 2,113 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.
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.
The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 2,113 words, ~4,844 tokens.
.claude/skills/bio-methylation-bismark-alignment/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.Reference examples tested with: Bismark 0.24+, Bowtie2 2.5+, HISAT2 2.2+, Trim Galore 0.6.10+, samtools 1.19+.
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flags and defaultsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
The genome build and the aligner backend ARE the versions that matter. The bisulfite index is built once per genome FASTA with a specific backend (--bowtie2 vs --hisat2); the index must match the backend used at alignment time, and the FASTA build (hg38 vs T2T-CHM13) fixes every downstream coordinate. EM-seq uses the identical aligners and flags as bisulfite - only the upstream chemistry and the coverage/efficiency expectations change.
"Align my bisulfite sequencing reads" -> Confirm the library type to pick the strand flag, trim the chemistry-specific artifacts, then map the C->T-converted reads to a C->T/G->A-converted reference - because the protocol and conversion, not the aligner, decide whether the calls mean anything.
bismark_genome_preparation --bowtie2 genome/ then bismark --genome genome/ -1 R1.fq.gz -2 R2.fq.gz -o out/Scope: short-read bisulfite (WGBS/RRBS/PBAT) and enzymatic (EM-seq) alignment, the genome index, the strand/library flag, deduplication, and conversion QC. Methylation extraction from the BAM (XM tag, MethylDackel, cytosine reports) -> methylation-calling. Per-CpG/DMR statistics -> differential-cpg-testing, dmr-detection. Long-read native MM/ML modification calling -> long-read-sequencing/nanopore-methylation. Adapter trimming mechanics -> read-qc/adapter-trimming.
A bisulfite (or EM-seq) run never reads methylation. It reads which cytosines SURVIVED deamination, against a 3-letter genome deliberately depleted of cytosines, as a C-vs-T choice. Every methylation call is two stacked conditional bets, and both fail silently:
Organize the work around defending these two bets - chemistry control (both directions) and library/strand correctness - not around listing bismark flags. The aligner reports a clean, sorted, indexed BAM whether conversion failed or half the reads went unmapped.
After conversion, unmethylated Cs become Ts, so the read/genome alphabet collapses toward {A,G,T}. A normal aligner would penalize every C->T as a mismatch, so bisulfite aligners convert all Cs to T in BOTH the reads AND the reference, map in the reduced alphabet, then recover methylation by comparing the original read to the original reference. Bismark builds two converted indices (C->T for OT/CTOT, G->A for OB/CTOB) and aligns each read against both. Reduced complexity means more multi-mapping and a lower mapping efficiency (~50-70% for WGBS vs >95% for ordinary DNA) - this is expected, not a bug. The same collapse means a sample CpG->TpG variant aligns with no extra mismatch and is scored as an unmethylated CpG: methylation at a C/T-polymorphic site is a hypothesis until SNP-aware (Bis-SNP, BISCUIT).
Bisulfite PCR generates four strand species: OT (original top), OB (original bottom), CTOT (complement of OT), CTOB (complement of OB). The library protocol decides which exist, and the flag must match or reads vanish silently:
| Library | Strands sequenced | Bismark flag | Dedup? | Trim Galore special-case |
|---|---|---|---|---|
| WGBS (directional) | OT, OB | (default) | YES | M-bias end-clip |
| EM-seq (directional) | OT, OB | (default) | YES | M-bias end-clip (gentler) |
| RRBS | OT, OB | (default) | NO | --rrbs (MspI fill-in) |
| PBAT / scBS-seq | CTOT, CTOB | --pbat | usually NO | aggressive 5' clip (random priming) |
| non-directional | all four | --non_directional | YES | M-bias end-clip (Trim Galore --non_directional is RRBS-only, needs --rrbs) |
PBAT does bisulfite conversion FIRST then tags by random priming, so its reads originate from CTOT/CTOB - the OPPOSITE of directional. PBAT needs --pbat for strand reasons; it is unrelated to RRBS. A non-directional library run as directional silently loses ~half its reads; PBAT run as directional maps near zero.
| Tool | Citation | Strategy | When |
|---|---|---|---|
| Bismark | Krueger & Andrews 2011 Bioinformatics 27:1571 | 3-letter, Bowtie2/HISAT2 backend | de-facto standard; self-contained (index + align + dedup + extractor); teach this |
| bwa-meth | Pedersen 2014 arXiv:1401.1129 | 3-letter, BWA-MEM | lean clinical/cfDNA; handles indels/clipping; pairs with MethylDackel for calling |
| BISCUIT | Zhou 2024 Nucleic Acids Res 52:e32 | 3-letter, BWA-derived | when SNPs / allele-specific methylation are needed alongside (joint genetic+epigenetic) |
| gemBS | Merkel 2019 Bioinformatics 35:737 | 3-letter, GEM3 | population-scale; the ENCODE WGBS pipeline mapper |
| abismal / methylpy | de Sena Brandine & Smith 2021 NAR Genom Bioinform 3:lqab115 | 2-letter (purine/pyrimidine) | memory-constrained, large cohorts |
All produce a BAM whose methylation is recovered by a SEPARATE caller (Bismark extractor, MethylDackel, or the tool's own). Alignment and calling are two steps.
| Scenario | Recommended | Why |
|---|---|---|
| Standard WGBS or EM-seq, mammalian | Bismark default (directional) + dedup | OT/OB only; the common case |
| RRBS | trim_galore --rrbs then Bismark default, NO dedup | MspI fixed ends look like (but are not) PCR duplicates |
| PBAT / scBS-seq | bismark --pbat | reads come from CTOT/CTOB, not OT/OB |
| Non-directional library | bismark --non_directional | all four strands present; default loses half |
| Precious low-input (cfDNA / FFPE / single-cell) | prefer EM-seq or TAPS upstream | bisulfite degrades 84-96% of input; same aligners apply |
| Need SNPs / allele-specific methylation | -> bwa-meth + Bis-SNP, or BISCUIT | C/T SNPs masquerade as methylation in 3-letter space |
| Large mammalian genome, low RAM | bismark --hisat2 (index must match) | HISAT2 backend is lighter than Bowtie2 |
| Extract per-CpG methylation from the BAM | -> methylation-calling | this skill stops at the deduplicated, M-bias-clipped BAM |
| Long-read ONT/PacBio modBAM (MM/ML tags) | -> long-read-sequencing/nanopore-methylation | native modification calling, not bisulfite |
Goal: Build the bisulfite-converted index once per genome, with the backend that alignment will use.
Approach: Place the reference FASTA(s) in a folder, run bismark_genome_preparation with the chosen backend; it writes Bisulfite_Genome/ containing the C->T and G->A converted indices.
bismark_genome_preparation --bowtie2 genome/ # or --hisat2 for large genomes, lower RAM
# genome/ holds the FASTA (e.g. hg38.fa); writes genome/Bisulfite_Genome/{CT_conversion,GA_conversion}
# The backend chosen here MUST match the bismark alignment backend below.Goal: Remove adapters and the library-specific end artifacts before alignment so they do not become spurious methylation calls.
Approach: Run Trim Galore (Cutadapt wrapper). Add --rrbs for RRBS (clips the MspI end-repair fill-in), --non_directional for non-directional, or extra 5' clipping for PBAT. Bismark itself does not trim. Mechanics live in read-qc/adapter-trimming.
trim_galore --paired R1.fq.gz R2.fq.gz # WGBS / EM-seq (auto-detect adapter, -q 20)
trim_galore --rrbs --paired R1.fq.gz R2.fq.gz # RRBS: extra 2 bp off 3' R1 (+ 5' R2) = MspI fill-in
trim_galore --clip_r2 6 --paired R1.fq.gz R2.fq.gz # PBAT/scBS: random-priming bias at 5' (amount from M-bias)bismark --genome genome/ -1 R1_val_1.fq.gz -2 R2_val_2.fq.gz \
--bowtie2 \ # must match the index backend; --hisat2 if prepared that way
--parallel 4 \ # instances PER direction; total threads scale up several-fold per instance
-o out/ # writes *_bismark_bt2_pe.bam + *_PE_report.txt (mapping efficiency, %meth per context)
# Add --pbat for PBAT/scBS, or --non_directional for non-directional libraries (NOT both).Goal: Remove PCR duplicates from random-fragmentation libraries, while leaving RRBS untouched.
Approach: deduplicate_bismark removes reads sharing mapping coordinate + strand. Run it on the by-name (unsorted) Bismark BAM, before extraction. For RRBS, SKIP it: every fragment starts at an MspI cut site, so identical coordinates are biologically distinct molecules, not PCR copies (apparent duplication ~90-95% is real data).
deduplicate_bismark --paired --bam out/sample_R1_bismark_bt2_pe.bam # WGBS/EM-seq ONLY
# RRBS: do NOT run this. UMI-tagged RRBS can dedup by UMI+coordinate; optical dups can still be removed.
samtools sort out/sample_R1_bismark_bt2_pe.deduplicated.bam -o out/sample.sorted.bam # IGV/downstream
samtools index out/sample.sorted.bamGoal: Bound both conversion error directions before believing any methylation level.
Approach: Spike unmethylated lambda phage (measures under-conversion -> false hyper) AND CpG-methylated pUC19 (measures over-conversion -> false hypo). Align each spike-in genome separately and read off context methylation. With no spike-in, sample CHH methylation is a weak fallback (somatic tissue only; confounded in ESCs/neurons/plants).
bismark_genome_preparation --bowtie2 lambda/ # lambda: residual %meth = non-conversion rate (target <=1%)
bismark --genome lambda/ -1 R1.fq.gz -2 R2.fq.gz -o lambda_qc/
# pUC19 (CpG-methylated): fraction of CpGs called UNmethylated = over-conversion (expect ~96-98% methylated)Spike-ins are naked, fully accessible DNA that denature completely, so their conversion is an OPTIMISTIC upper bound. Real genomic conversion is region-dependent: GC-rich CpG islands and structured regions denature less, under-convert more, and inflate apparent methylation exactly where the biology is. Treat the spike-in number as a floor; a rising per-GC-bin CHH rate flags local under-conversion.
Trigger: running PBAT/scBS or a non-directional library without --pbat/--non_directional. Mechanism: PBAT reads come from CTOT/CTOB and non-directional from all four strands, but the default tries only OT/OB. Symptom: near-zero (PBAT) or ~halved (non-directional) mapping efficiency on a clean-looking run. Fix: confirm the kit/protocol directionality, pass the matching flag; never reach for -N 1 first.
Trigger: no conversion control, or only a lambda (under-conversion) control. Mechanism: an unmethylated C surviving deamination is indistinguishable from real 5mC. Symptom: globally elevated methylation, worst in GC-rich CpG islands. Fix: report BOTH a lambda non-conversion rate (<=1%) and a pUC19 over-conversion rate; add per-GC CHH as an internal check.
Trigger: running deduplicate_bismark on RRBS. Mechanism: MspI cuts give every fragment a fixed start, so distinct molecules share coordinates. Symptom: ~90-95% of reads discarded, coverage decimated. Fix: skip coordinate dedup for RRBS; use UMIs if dedup is required.
Trigger: RRBS aligned without trim_galore --rrbs. Mechanism: end-repair fills MspI overhangs with unmethylated dCTP, creating artificial cytosines at fragment ends. Symptom: artificial hypomethylation clustered at MspI sites. Fix: trim_galore --rrbs; Bismark aligns RRBS fine but does NOT fix this trimming artifact.
Trigger: calling methylation off raw read ends. Mechanism: end-repair fills 5' overhangs with unmethylated dCTP, worst at the start of R2. Symptom: an M-bias plot (methylation vs read position) shows a dip/spike at the ends instead of a flat line. Fix: read the M-bias plot, clip the affected ends; extraction --ignore/--clip mechanics live in methylation-calling.
Trigger: index prepared with --bowtie2, alignment run with --hisat2 (or vice versa). Mechanism: the two backends use incompatible converted indices. Symptom: Bismark errors or fails to find the index. Fix: prepare and align with the same backend.
| Threshold | Source | Rationale |
|---|---|---|
| Lambda non-conversion <=1% | manufacturer spec; field standard | residual apparent methylation on unmethylated spike-in = false-positive floor (EM-seq v2 ~<=0.5%) |
| pUC19 ~96-98% methylated | manufacturer spec | bounds over-conversion -> false hypo; lambda alone cannot see this direction |
| WGBS mapping efficiency ~50-70% | Krueger & Andrews 2011; 3-letter complexity | reduced alphabet costs uniqueness; below this, diagnose (library flag > trimming > reference > biology) |
| EM-seq mapping efficiency typically higher | Vaisvila 2021 | no chemical fragmentation -> flatter coverage; WGBS expectations are too pessimistic |
| Bisulfite degrades 84-96% of input | Grunau 2001 Nucleic Acids Res 29:e65 | only ~4-16% of molecules survive intact; the reason low-input fails and EM-seq/TAPS exist |
-N = 0 (seed mismatches) | Bismark manual | default; -N 1 raises sensitivity AND mis-mapping - last resort, not the low-mapping fix |
--rrbs clips 2 bp | Trim Galore guide | the MspI end-repair fill-in length; confirm on the installed version |
| Error / symptom | Cause | Solution |
|---|---|---|
| Near-zero mapping efficiency | PBAT run as directional | add --pbat |
| ~Half the reads unmapped | non-directional run as directional | add --non_directional |
| RRBS loses ~90% of reads | deduplicated by coordinate | skip deduplicate_bismark for RRBS |
| Globally high methylation | incomplete conversion (no/one-sided control) | lambda + pUC19 spike-ins; check per-GC CHH |
| Artificial hypomethylation at MspI sites | --rrbs trimming omitted | trim_galore --rrbs |
| FastQC per-base content / GC FAIL | expected for converted libraries (C depleted) | not a defect; do not "fix" a healthy bisulfite library |
| 0% sites at C/T variants | C/T SNP read as unmethylated CpG | SNP-aware calling (Bis-SNP/BISCUIT) or mask known C/T SNPs |
| Bismark cannot find the index | backend mismatch with genome prep | re-prep or align with the matching --bowtie2/--hisat2 |
| Output named "5mC" | standard BS and EM-seq report 5mC+5hmC summed | label the sum; oxBS/TAB pairing is needed to separate (see methylation-calling) |
© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files in methylation-analysis/bismark-alignment of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
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.
Bio Methylation Bismark Alignment 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 Methylation Bismark Alignment this skillGPTomics/bioSkills | 1.2k | 1 repos | ~4.8k | Automated safety check: Pass | MIT | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Clinvar Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.9k | Automated safety check: Notes | Apache-2.0 | |
| Metabolic Study Planneraiming-lab/AutoResearchClaw | 15k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Dbsnp Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.4k | Automated safety check: Notes | Apache-2.0 |
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
google-deepmind/science-skills
A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
google-deepmind/science-skills
A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.
aiming-lab/AutoResearchClaw
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
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.
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
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.
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Categories
Aligns bisulfite-converted (WGBS, RRBS, PBAT) and enzymatic (EM-seq) short reads to an in-silico C-T/G-A-converted reference with Bismark (Bowtie2 or HISAT2), preparing the genome index, choosing…. Bio Methylation Bismark Alignment is an agent skill from GPTomics/bioSkills. Aligns bisulfite-converted (WGBS, RRBS, PBAT) and enzymatic (EM-seq) short reads to an in-silico C-T/G-A-converted reference with Bismark (Bowtie2 or HISAT2), preparing the genome index, choosing the directional vs non-directional vs PBAT strand flag, deduplicating WGBS/EM-seq (never RRBS), and bounding bisulfite conversion efficiency with unmethylated lambda and methylated pUC19 spike-ins.
Bio Methylation Bismark Alignment fits situations like: aligning bisulfite; preparing a bisulfite genome; choosing the strand flag; diagnosing low mapping efficiency.
Run `npx skills add GPTomics/bioSkills --skill bio-methylation-bismark-alignment -a claude-code`. Or copy the skill folder (methylation-analysis/bismark-alignment in GPTomics/bioSkills) into .claude/skills/bio-methylation-bismark-alignment in your project. Claude Code loads it when a task matches its description.
Run `npx skills add GPTomics/bioSkills --skill bio-methylation-bismark-alignment -a codex`. Or copy the skill folder (methylation-analysis/bismark-alignment in GPTomics/bioSkills) into .agents/skills/bio-methylation-bismark-alignment in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-methylation-bismark-alignment -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-methylation-bismark-alignment, .gemini/skills/bio-methylation-bismark-alignment, .github/skills/bio-methylation-bismark-alignment and .opencode/skills/bio-methylation-bismark-alignment in your project.
Going by SKILL.md and its folder, Bio Methylation Bismark Alignment needs a shell for the scripts in its folder. Our summary lists: A Bash shell.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
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.
Bio Methylation Bismark Alignment is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Bio Methylation Bismark Alignment: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.