k-dense-ai/scientific-agent-skills

pacsomatic

Prepares and launches nf-core/pacsomatic matched tumor-normal PacBio HiFi genomics workflows from unaligned BAM inputs.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill pacsomatic

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

pacsomatic

When to use

Use scripts/run_pacsomatic.py to prepare one matched PacBio HiFi tumor/normal pair, generate a samplesheet and reproducible launch artifacts, and launch locally or submit the Nextflow driver to a scheduler. The pipeline realigns input BAMs; this helper targets unaligned HiFi BAMs and optional PacBio .pbi indexes. Do not substitute short reads or treat a BAM filename as evidence of platform, matched identity, or methylation information.

The reviewed upstream dev commit is 24c84cb371b0339c1d65a4de9451671945e19772. GitHub had no releases or tags on 2026-10-01, despite the internal manifest saying 1.0.0. The helper pins that commit by default for nf-core/pacsomatic; it does not invent a release tag. This is a source-reviewed development workflow, not a clinically validated assay. See references/pacsomatic_guide.md for sources and scientific checks.

Workflow

  1. Obtain distinct tumor and normal BAM paths, patient ID, distinct sample IDs,

output directory, and exactly one reference mode: --fasta or --genome. IDs and BAM/PBI/FASTA paths must have no whitespace. Local inputs must be nonempty regular files. Remote BAM/PBI/FASTA URIs are passed through without downloading or authenticating; use managed filesystem/cloud credentials, never embed secrets or signed URLs in generated files.

  1. Confirm PacBio HiFi read groups and sample identity from acquisition metadata;

confirm that MM/ML modification tags needed for methylation have been retained. Verify reference sequence/contig compatibility for every annotation resource.

  1. Generate artifacts with --dry-run. This performs helper checks and writes

files, but does not invoke the pipeline, validate BAM contents, check remote availability, resolve every pipeline parameter, or verify biological suitability. Missing runtime tools are warnings here. --dry-run cannot be combined with --run/--submit, cloning, or environment creation.

  1. Review samplesheet, generated params YAML, script, pipeline revision, profiles,

and branch-specific resources/skips. Existing artifacts require explicit --overwrite; input files can never be artifact targets. config.yaml is an operator reference, not an automatically loaded configuration file.

  1. For execution, select the actual runtime with --use-current-path or an

existing --conda-env. Load cluster modules before invoking the helper; --module-load only repeats those commands in the generated script. No Conda YAML is bundled; creating an environment needs --conda-env-file explicitly.

  1. Use --run only for requested execution. For HPC, distinguish the outer

launcher scheduler (--executor) from Nextflow's per-task process.executor, configured by a site profile or --nextflow-config. Driver CPU/memory requests do not constrain task resources. Read references/config-and-output.md.

  1. Report artifact paths, revision, checks/warnings, run type, submission ID if

present, and a concrete next QC or failure-triage step. Scheduler acceptance is not pipeline completion. Keep the output directory and work/cache state stable for --resume; scripts run with the output directory as their cwd.

Examples

Run these from the repository root. Paths and site settings are illustrative; local tests use synthetic placeholders only, not human genomic data.

bash
python skills/pacsomatic/scripts/run_pacsomatic.py \
  --tumor-bam /data/P001_T.bam --normal-bam /data/P001_N.bam \
  --patient-id P001 --tumor-sample-id P001_T --normal-sample-id P001_N \
  --outdir /results/P001 --fasta /refs/GRCh38.fa \
  --profile apptainer --use-current-path --dry-run

After reviewing artifacts, a Slurm launch can use the same inputs plus the following options (replace --dry-run with --run):

text
--executor slurm --queue compute --project my_account
--cpus 2 --memory-gb 8 --walltime 48:00
--nextflow-config /configs/slurm.config --overwrite --run

Those resources are for the driver, assuming the reviewed infrastructure config sets process.executor = 'slurm' and suitable task queue/resources. The helper normalizes 48:00 to Slurm 48:00:00 (48 hours). Do not add a sanger profile unless actually using that institution's LSF infrastructure.

Custom pipeline parameters go in --params-file; infrastructure goes in --nextflow-config (-c). The helper's explicit input/outdir/reference options win over params-file values. --extra-args is tokenized and shell-quoted, but cannot override these managed inputs/configuration options. Keep paths inside external params/config files absolute because the launcher cwd is the outdir.

Verification and references

bash
uv run skills-ref validate skills/pacsomatic
python tests/run_all.py --isolated pacsomatic

The standard-library suite checks local artifact behavior, path protections, CLI modes, runtime failures and mocked scheduler submissions. Native Nextflow checks use a tiny local workflow; they do not establish that pacsomatic's full containerized scientific pipeline succeeds on a given dataset or cluster.

같은 저장소의 Skills

더 많은 Skills

모든 Skills
k-dense-ai
커뮤니티

13c-metabolic-flux

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments

설치 수
0
GitHub Stars
5.5만
업데이트
10월 19일
k-dense-ai
커뮤니티

adaptyv

Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, fluorescence, epitope binning, and enzyme activity workflows, including code using adaptyv or FoundryClient.

설치 수
0
GitHub Stars
5.5만
업데이트
10월 19일
k-dense-ai
커뮤니티

aeon

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

설치 수
0
GitHub Stars
5.5만
업데이트
10월 19일
k-dense-ai
커뮤니티

alphagenome

Looks up precomputed AlphaGenome Atlas effects for any GRCh38 single-nucleotide variant (AVI score with Phred and 18 SHAP feature attributions, plus raw and quantile scores for RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation and contact-map tracks), scores variants or scans windows on demand with the AlphaGenome model for human and mouse (variant scoring, in silico mutagenesis, REF-versus-ALT track prediction), and builds Atlas website deep links. Use when t

설치 수
0
GitHub Stars
5.5만
업데이트
10월 19일