version: "1.0.1" name: bulkrna-read-qc description: Load when checking raw FASTQ quality (Phred / GC / adapter / Q20-Q30) before alignment in bulk RNA-seq. Skip when reads are already aligned (use bulkrna-read-alignment); counted (use bulkrna-qc); single-cell FASTQ (use sc-fastq-qc). trigger: FASTQ QC, read quality, Phred, FastQC, adapter, GC content, Q20, Q30 tags:

• bulkrna
• FASTQ
• QC
• Phred
• GC-content
• adapter
• read-quality

bulkrna-read-qc

When to use

Read raw Phred+33 FASTQ before alignment. This implements selected quality metrics, not the complete FastQC suite. Use bulkrna-read-alignment for logs.

Use from a step

from skills._sdk.notebook import load_skill, write_output
library = load_skill('bulkrna-read-qc')
data = library.demo_data(random_state=42)
result = library.quality_control(data)
write_output(result, 'tables/qc_summary.csv')

For real files, pass read_fastq, read_log or read_reference as appropriate to read_input(..., reader=...). examples/example_step.py is executable.

API

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read_fastq(path: str | Path, *, max_reads: int=100000) -> pd.DataFrame

Read FASTQ records; pass this function as reader= to read_input.

:param path: FASTQ path; a .gz suffix selects gzip decompression. :param max_reads: CLI limit 100000; increase for a larger leading-read sample. :returns: Sequence and Phred+33 quality strings as rows. :raises ValueError: Records are truncated, malformed, or have unequal sequence/quality lengths.

quality_control(reads: pd.DataFrame, *, max_reads: int=100000) -> pd.DataFrame

Compute a new one-row quality summary for Phred+33 reads.

:param reads: sequence and quality string columns; input rows are not changed. :param max_reads: CLI default 100000 leading records; increase to inspect more reads. :returns: Scalar QC metrics and adapter counts; run_info retains per-base distributions. :raises ValueError: Reads, lengths or Phred+33 scores are invalid.

run_info(result: pd.DataFrame, *, keep: bool=True) -> dict

Read full quality distributions and sampling diagnostics.

:param result: Output of quality_control. :param keep: True preserves attrs; False removes diagnostics before serialization. :returns: A separate dictionary containing metrics and sampling provenance. :raises TypeError: The result is not a DataFrame.

quality_figure(result: pd.DataFrame)

Plot mean and interquartile quality by read position.

:param result: QC result retaining its diagnostic attrs. :returns: A matplotlib Figure without writing files. :raises KeyError: Per-base diagnostics are absent.

gc_figure(result: pd.DataFrame)

Plot GC fractions across sampled reads.

:param result: QC result retaining its diagnostic attrs. :returns: A matplotlib Figure without writing files. :raises KeyError: GC diagnostics are absent.

demo_data(*, random_state: int=42) -> pd.DataFrame

Generate valid synthetic FASTQ records in memory.

:param random_state: CLI seed 42; change for another simulation without altering global RNG state. :returns: Five thousand 150-base reads with quality strings of the same length. :raises ValueError: The seed is invalid.

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Methods and parameters

read_fastq reads the leading 100000 records by default and accepts .gz. quality_control computes per-base quality, Q20/Q30, GC, lengths and adapter motif hits from sequence/quality strings. It does not trim reads.

Gotchas

• read_fastq rejects truncated FASTQ and unequal sequence/quality lengths.
• quality_control assumes Phred+33, not the older Phred+64 encoding.
• adapter_rate sums motif hits and can exceed 100% if a read contains multiple adapters.

Inputs and outputs

The CLI writes these artifacts; functions return DataFrames and Figures without writing them:

• tables/qc_summary.csv
• figures/per_base_quality.png
• figures/gc_content.png
• figures/read_length_distribution.png
• figures/quality_score_distribution.png
• report.md
• result.json
• reproducibility/commands.sh
• demo_reads.fastq only with --demo.

CLI

python skills/bulkrna/bulkrna-read-qc/bulkrna_read_qc.py --demo --output /tmp/bulkrna_read_qc

For real files use --input <file>; trajectory placement also needs --reference <file>.

See also

• references/methodology.md
• references/parameters.md
• references/output_contract.md

Dependencies

matplotlib, numpy, pandas