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Load when summarising STAR / HISAT2 / Salmon alignment-rate logs in bulk RNA-seq. Skip if data is raw FASTQ (use bulkrna-read-qc) or already counted (use bulkrna-qc), or for genome-DNA alignment (use genomics-alignment).
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Operate MedSAM2 for promptable segmentation of 3D medical images and medical videos, including CT lesion propagation, MRI volumes, RECIST-guided prompts, efficient CPU-oriented variants, training, and 3D Slicer integration. Use when generating or validating volumetric masks from sparse prompts or propagating masks through image slices or video frames.
Build reproducible healthcare imaging pipelines with Project MONAI for DICOM, NIfTI, pathology, and multidimensional imaging tasks including preprocessing, augmentation, training, sliding-window inference, evaluation, model bundles, labeling, and deployment. Use when implementing medical image classification, segmentation, registration, detection, generative, or foundation-model workflows in PyTorch.
Operate Google TxGemma prediction and chat models for therapeutic property prediction across small molecules, proteins, nucleic acids, diseases, targets, and cell lines. Use when formatting Therapeutics Data Commons tasks, choosing TxGemma model size or variant, running local or Model Garden inference, fine-tuning on private therapeutic data, or evaluating TxGemma in drug-discovery workflows.
Evaluate and operate released Profluent OpenCRISPR gene-editing systems, especially OpenCRISPR-1, for controlled research workflows using its published Cas9-like protein, compatible guide RNA designs, protocols, licensing, specificity testing, and experimental validation. Use when comparing OpenCRISPR-1 with SpCas9, planning nonclinical editing studies, or assessing use in nuclease, nickase, deactivated, base, prime, or epigenome-editing contexts.
Operate CZI TranscriptFormer cross-species generative single-cell models to produce cell embeddings, contextual gene embeddings, likelihoods, zero-shot classifiers, disease-state representations, and regulatory analyses from raw-count AnnData files. Use when selecting TF-Sapiens, TF-Exemplar, or TF-Metazoa, processing in- or out-of-distribution species, or scaling embedding extraction across GPUs.
Operate ByteDance Protenix-v2 for open biomolecular structure prediction of proteins, antibodies, nucleic acids, ligands, and complexes using JSON inputs, MSA and template features, constraints, and inference-time sampling. Use when running Protenix locally or through its server, comparing AlphaFold3-style open models, or building reproducible co-folding evaluations.
| name | bulkrna-read-alignment |
| description | Load when summarising STAR / HISAT2 / Salmon alignment-rate logs in bulk RNA-seq. Skip if data is raw FASTQ (use bulkrna-read-qc) or already counted (use bulkrna-qc), or for genome-DNA alignment (use genomics-alignment). |
| version | 0.3.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["bulkrna","alignment","STAR","HISAT2","Salmon","mapping-rate","strandedness"] |
| requires | ["numpy","pandas","matplotlib"] |
Run after the aligner / quantifier finishes, on the log file produced by
STAR (Log.final.out), HISAT2 (.log), or Salmon (meta_info.json).
Yields a one-page mapping-rate summary, strandedness inference, and a
gene-body coverage profile — the QC bridge between raw FASTQ and the
count matrix.
| Input | Format | Required |
|---|---|---|
| Aligner log | Log.final.out (STAR), .log (HISAT2), meta_info.json (Salmon) | yes (or --demo) |
| Output | Path | Notes |
|---|---|---|
| Mapping summary figure | figures/mapping_summary.png | unique vs multi vs unmapped |
| Gene body coverage | figures/gene_body_coverage.png | 5'→3' bias profile |
| Alignment composition | figures/alignment_composition.png | breakdown of mapping outcomes |
| Stats table | tables/alignment_stats.csv | parsed numeric metrics |
| Report | report.md + result.json | always |
bulkrna_read_alignment.py:305-311): log.final.out → STAR; meta_info → Salmon; otherwise → HISAT2.report.md + tables/alignment_stats.csv.bulkrna_read_alignment.py:305-311 dispatches by input_path.name.lower() — anything that is neither log.final.out nor meta_info (case-insensitive substring) is silently parsed as a HISAT2 log. A renamed STAR log will produce nonsense. Pass --method star explicitly if your STAR file isn't named conventionally..bam or .sam file as --input will not raise — the parser just finds zero matchable lines and reports an empty stats dict. Sanity-check result.json["summary"]["total_reads"] is non-zero before trusting any downstream summary.--demo mode (bulkrna_read_alignment.py:142-150). The 5'→3' bias profile in demo runs is a fixed reproducible curve, not derived from real input — useful for layout previews but not for assessing real RNA degradation.python omicsclaw.py run bulkrna-read-alignment --demo
python omicsclaw.py run bulkrna-read-alignment --input Log.final.out --output results/
references/parameters.md — every CLI flag and tuning hintreferences/methodology.md — STAR / HISAT2 / Salmon parsers, strandedness inferencereferences/output_contract.md — exact output directory layoutbulkrna-read-qc (upstream FASTQ QC), bulkrna-qc (downstream count-matrix QC), genomics-alignment (DNA-alignment sibling: BAM/SAM, not log files)