Rnaseq Pipeline

# 1) Alignment-based workflow
fastqc raw/*.fastq.gz -o qc/raw/
STAR --runMode genomeGenerate --genomeDir genome_index --genomeFastaFiles genome.fa --sjdbGTFfile genes.gtf
STAR --genomeDir genome_index --readFilesIn sample_R1.fastq.gz sample_R2.fastq.gz --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate
featureCounts -a genes.gtf -o counts.txt sample.bam

# 2) Lightweight quantification workflow
fastqc raw/*.fastq.gz -o qc/raw/
salmon index -t transcripts.fa -i transcriptome_index
salmon quant -i transcriptome_index -l A -1 sample_R1.fastq.gz -2 sample_R2.fastq.gz -o sample_quant

# 3) Differential expression handoff
library(DESeq2)
dds <- DESeqDataSetFromMatrix(countData = counts, colData = coldata, design = ~ condition)
dds <- DESeq(dds)
res <- results(dds)

Recommended Workflow

1. Start with sample metadata, contrasts, reference assembly, and annotation versions fixed in writing.
2. Run raw-read QC first and decide whether trimming is actually necessary.
3. Choose one quantification branch deliberately: splice-aware alignment plus counting, or transcript-level pseudoalignment.
4. Build a count matrix or abundance table only after confirming library strandedness and annotation compatibility.
5. Run DE with proper biological replicates, then produce PCA, heatmap, MA, and volcano summaries.
6. Finish with enrichment or pathway analysis only after validating the upstream statistical model.

Guardrails

• Never mix genome assembly and annotation releases.
• Strandedness mistakes will corrupt counts; verify it before cohort-scale quantification.
• Differential expression without biological replication is not a real DE workflow.
• Batch effects, low-count filtering, and contrast specification should be decided before interpreting significant genes.
• This meta-skill should delegate command-level details to the individual tool skills instead of duplicating every CLI option inline.