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Load when checking raw FASTQ quality (Phred / GC / adapter / Q20-Q30) before alignment in bulk RNA-seq. Skip if reads are already aligned (use bulkrna-read-alignment) or counted (use bulkrna-qc), or for single-cell FASTQ (use sc-fastq-qc).
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
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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-qc |
| description | Load when checking raw FASTQ quality (Phred / GC / adapter / Q20-Q30) before alignment in bulk RNA-seq. Skip if reads are already aligned (use bulkrna-read-alignment) or counted (use bulkrna-qc), or for single-cell FASTQ (use sc-fastq-qc). |
| version | 0.3.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["bulkrna","FASTQ","QC","Phred","GC-content","adapter","read-quality"] |
| requires | ["numpy","pandas","matplotlib"] |
Run as the first step on raw bulk RNA-seq FASTQ files (single or paired-end) before aligning. Reports per-base Phred quality, GC content, adapter contamination signals, read length distribution, and Q20/Q30 fractions — the metrics needed to decide whether trimming is worth the trouble.
| Input | Format | Required |
|---|---|---|
| FASTQ file | .fastq or .fastq.gz | yes (or --demo) |
| Output | Path | Notes |
|---|---|---|
| QC report | report.md + result.json | always |
| Per-base quality | figures/per_base_quality.png | Phred profile across read positions |
| GC distribution | figures/gc_content.png | per-read GC% histogram |
| Length distribution | figures/read_length_dist.png | for variable-length / trimmed input |
.gz per bulkrna_read_qc.py:70).report.md + result.json..gz detection is filename-suffix only (bulkrna_read_qc.py:70 checks .endswith(".gz")). A gzipped file misnamed without .gz will be opened as text and silently produce garbage; rename or symlink before running.python omicsclaw.py run bulkrna-read-qc --demo
python omicsclaw.py run bulkrna-read-qc --input reads.fastq.gz --output results/
references/parameters.md — every CLI flag and tuning hintreferences/methodology.md — Phred decoding, sampling strategy, adapter detectionreferences/output_contract.md — exact output directory layoutbulkrna-read-alignment (downstream after alignment), bulkrna-qc (downstream after counting), sc-fastq-qc (single-cell sibling), genomics-qc (genomic-DNA sibling)