“multiqc”的搜索结果

Validate FASTQ sequencing files, run local quality checks, and interpret reports to decide whether trimming is needed. Optionally remove adapters or low-quality read ends while preserving original reads and their quality reports.

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• Check sequencing quality before downstream analysis
• Summarize quality reports across multiple samples
• Assess whether adapter or quality trimming is needed

Plan or run bulk RNA-seq processing from sequencing reads to count matrices, tables of expression counts across samples. Check sample metadata, reference consistency, read mapping, and quantification quality, and produce summaries for downstream analysis.

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• Generate count matrices from FASTQ files
• Check reference files and sample metadata
• Review mapping and quantification quality

Aggregates QC from 150+ bioinformatics tools into one interactive HTML report. Scans FastQC, samtools, STAR, HISAT2, Trim Galore, featureCounts, Kallisto, Salmon, Picard, GATK logs; merges per-sample stats with plots. For NGS pipeline-wide QC. Use FastQC directly for single-sample; MultiQC for multi-sample reporting.

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Automated sequencing quality control and advanced visualization wrapping FastQC, MultiQC, and custom chart generation. Exposes an MCP stdio server for live AI integration alongside a ClawBio CLI runner.

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Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.

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• Generate expression counts from FASTQ files
• Compare gene expression between sample groups
• Plan quality checks and statistical analysis together

Organize ATAC-seq, ChIP-seq, CUT&RUN, or CUT&Tag inputs and route them to suitable analysis workflows. Covers initial quality checks and preparation for finding peaks, genomic regions with concentrated signal, and comparing them across samples.

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• Choose an analysis workflow for an epigenomics assay
• Check FASTQ inputs and sample-sheet readiness
• Organize control and biological replicate information

Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.

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Think and work like an expert Bioinformatics Engineer. Use when a task calls for Bioinformatics Engineer judgment. Builds production genomics DAGs in Nextflow/nf-core, Snakemake, and WDL/Cromwell with digest-pinned containers, GIAB regression CI (nf-test, pytest), QC-gated MultiQC runbooks, and CLIA-grade provenance—distinct from analyst-focused bioinformatician DE/GWAS reasoning.

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Generates standardized quality control reports by aggregating metrics from FastQC, alignment, and other tools using MultiQC. Use when summarizing QC metrics across samples, creating shareable quality reports, or building automated QC pipelines.

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Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.

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