git:20260920.ce7840d to git:20260920.6050003

---

name: pipeline-rnaseq

description: "Execute ENCODE RNA-seq pipeline from FASTQ to gene quantification and signal tracks. Child of pipeline-guide. Provides Nextflow execution with Docker and cloud deployment. Use when processing RNA-seq data with STAR alignment, RSEM/Kallisto quantification, or generating expression matrices. Trigger on: RNA-seq pipeline, gene expression, STAR alignment, RSEM quantification, transcript quantification, TPM, FPKM, RNA processing, run RNA-seq."

---

# ENCODE RNA-seq Pipeline

## When to Use

- User wants to run an RNA-seq processing pipeline from FASTQ to gene quantification

- User asks about "RNA-seq pipeline", "STAR alignment", "RSEM", "gene expression quantification", or "Kallisto"

- User needs to process bulk RNA-seq data with ENCODE-standard 2-pass STAR alignment

- Example queries: "process my RNA-seq FASTQs", "quantify gene expression from RNA-seq", "run STAR and RSEM on my data"

Execute the ENCODE RNA-seq processing pipeline from raw FASTQ files through splice-aware

alignment, gene/transcript quantification, and strand-specific signal track generation.

This skill provides a complete Nextflow DSL2 implementation following ENCODE uniform

analysis standards.

## Overview

RNA-seq measures transcriptome-wide gene expression by sequencing cDNA derived from

cellular RNA. The ENCODE pipeline processes RNA-seq data through quality control,

splice-aware alignment with STAR (2-pass mode), gene and transcript quantification

with RSEM, optional fast pseudoalignment with Kallisto, and generation of strand-specific

signal tracks as bigWig files.

Key design decisions: STAR 2-pass mode for maximum splice junction sensitivity, RSEM

for accurate gene/transcript/isoform quantification including multi-mapped reads,

stranded library protocol (dUTP/rf-stranded) as the ENCODE standard, and paired-end

sequencing with a minimum of 30 million uniquely mapped reads per replicate.

## Key Literature

| Reference | Journal | Year | DOI | Relevance |

|-----------|---------|------|-----|-----------|

| Dobin et al. "STAR: ultrafast universal RNA-seq aligner" | Bioinformatics | 2013 | 10.1093/bioinformatics/bts635 | Splice-aware aligner (~12,000 citations) |

| Li & Dewey "RSEM: accurate transcript quantification from RNA-Seq data" | BMC Bioinformatics | 2011 | 10.1186/1471-2105-12-323 | Gene/transcript quantification (~6,000 citations) |

| Bray et al. "Near-optimal probabilistic RNA-seq quantification" | Nature Biotechnology | 2016 | 10.1038/nbt.3519 | Fast pseudoalignment (~4,000 citations) |

| Wang et al. "RSeQC: quality control of RNA-seq experiments" | Bioinformatics | 2012 | 10.1093/bioinformatics/bts356 | RNA-seq QC suite (~3,500 citations) |

| ENCODE Project Consortium "Expanded encyclopaedias" | Nature | 2020 | 10.1038/s41586-020-2493-4 | ENCODE Phase 3 standards |

| Frankish et al. "GENCODE 2021" | Nucleic Acids Research | 2021 | 10.1093/nar/gkaa1087 | Gene annotation reference |

## Pipeline Stages

```

FASTQ

├─> FastQC (raw reads)

└─> Trim Galore (+ FastQC on the trimmed reads)

├─> Kallisto (optional) ──────────> kallisto/<sample>/abundance.tsv

└─> STAR (2-pass)

├─> transcriptome BAM ─> RSEM ─> <sample>.genes.results / .isoforms.results

├─> bedGraph str1/str2 ─> bedGraphToBigWig ─> signal/<sample>_{plus,minus}.bw

└─> genome BAM ─> RSeQC (infer_experiment, read_distribution,

geneBody_coverage, inner_distance)

MultiQC <── FastQC (raw + trimmed), trimming reports, STAR Log.final.out,

RSEM .stat/, RSeQC infer_experiment + read_distribution

└─> qc/multiqc/multiqc_report.html

```

The Kallisto abundances, the gene body coverage and the inner distance files are

published but are not part of the MultiQC report; read those files directly.

### Stage Summary

| Stage | Tool | Input | Output | Reference |

|-------|------|-------|--------|-----------|

| 1. QC & Trimming | FastQC, Trim Galore | Raw FASTQ | Trimmed FASTQ + FastQC reports | references/01-qc-trimming.md |

| 2. Alignment | STAR (2-pass) | Trimmed FASTQ | Genome BAM + Transcriptome BAM + bedGraph | references/02-star-alignment.md |

| 3. Quantification | RSEM, Kallisto | Transcriptome BAM / trimmed FASTQ | Gene/transcript counts, TPM, FPKM | references/03-quantification.md |

| 4. Signal Tracks | bedGraphToBigWig | STAR bedGraph | Strand-specific bigWig | references/04-signal-tracks.md |

| 5. QC Metrics | RSeQC, MultiQC | Genome BAM, logs | Strandedness, read distribution, gene body coverage | references/05-qc-metrics.md |

## Input Requirements

### Required Files

- **RNA-seq FASTQ**: paired-end reads matched by the `--reads` glob (ENCODE standard;

single-end with `--single_end`)

- **STAR genome index directory** (`--star_index`)

- **RSEM reference prefix** (`--rsem_index`) produced by `rsem-prepare-reference`

- **BED12 gene model for RSeQC** (`--rseqc_bed`)

There is no sample sheet: samples are the pairs that `--reads` matches, and the sample

ID is the shared prefix of each pair. The gene annotation is not a workflow parameter

and there is no `--gtf`; the GTF is consumed when the STAR and RSEM references are built

(references/02 and references/03), so the annotation is fixed by the index you pass in.

## Library Strandedness

`--strandedness` takes one value for the whole run — `reverse` (default), `forward` or

`none` — and is validated before the first task. It drives three things at once: the RSEM

`--strandedness` flag, the kallisto strand flag, and which STAR bedGraph becomes which

signal track.

| Protocol | `--strandedness` | RSEM receives | Kallisto receives | Signal tracks |

|----------|------------------|---------------|-------------------|---------------|

| dUTP (ENCODE standard), Illumina TruSeq Stranded | `reverse` | `--strandedness reverse` | `--rf-stranded` | `<sample>_plus.bw`, `<sample>_minus.bw` |

| Directional ligation (some legacy protocols) | `forward` | `--strandedness forward` | `--fr-stranded` | `<sample>_plus.bw`, `<sample>_minus.bw` |

| SMARTer / SMART-Seq2 and other unstranded kits | `none` | `--strandedness none` | no strand flag | `<sample>_unstranded.bw` |

There is no per-sample strandedness and the workflow does not detect it. RSeQC

`infer_experiment.py` runs as a post-hoc check and writes

`qc/rseqc/<sample>.infer_experiment.txt`. If the library type is unknown, run a first

pass, read that file (references/05 explains the output), and rerun with the correct

`--strandedness` — the RSEM counts, the kallisto abundances and the signal tracks all

depend on it, so a wrong value has to be corrected by rerunning, not by post-processing.

## QC Thresholds

| Metric | Threshold | Produced by |

|--------|-----------|-------------|

| Total sequenced reads | >=30M PE reads | `fastqc/`, `star/<sample>.Log.final.out` |

| Uniquely mapped reads | >=70% of input reads | `star/<sample>.Log.final.out` |

| Multi-mapped reads | <10% | `star/<sample>.Log.final.out` |

| Strandedness agreement | >90% for a stranded library | `qc/rseqc/<sample>.infer_experiment.txt` |

| Exonic rate | >60% | `qc/rseqc/<sample>.read_distribution.txt` |

| Gene body coverage | Relatively uniform (5'/3' bias <1.5) | `qc/rseqc/<sample>.geneBody_coverage.geneBodyCoverage.txt` |

Not computed by this workflow: rRNA rate, library duplication rate (beyond the

sequence-level estimate inside the FastQC report), detected-gene counts, and saturation

curves. references/05-qc-metrics.md gives the commands to run those by hand on the

published BAM and RSEM output.

### Read Depth Guidelines

| Application | Minimum Reads (PE) | Recommended | Notes |

|-------------|-------------------|-------------|-------|

| Gene-level expression | 20M | 30M | ENCODE minimum |

| Transcript-level expression | 40M | 60M | Isoform resolution requires more depth |

| Differential expression | 20M per sample | 30M per sample | 3+ biological replicates per condition |

| Novel junction discovery | 60M | 100M+ | STAR 2-pass mode benefits from depth |

| Fusion detection | 50M | 80M+ | Chimeric reads are rare; needs a separate STAR run (references/02) |

## Execution

Every index flag is shown in the examples below because the defaults are bare names

resolved in the launch directory (`GRCh38_star_index`, `GRCh38_rsem_index/GRCh38`,

`gencode.v38.kallisto.idx`, `hg38_RefSeq.bed` for `--genome GRCh38`). The run stops

before the first task if any of them is missing. `--kallisto_index` is the one exception:

it is not read when `--skip_kallisto` is set.

### Quick Start (local, Docker)

```bash

nextflow run scripts/main.nf -profile local \

--reads 'fastq/*_R{1,2}.fq.gz' \

--genome GRCh38 \

--star_index /ref/GRCh38_star_index \

--rsem_index /ref/GRCh38_rsem_index/GRCh38 \

--kallisto_index /ref/gencode.v38.kallisto.idx \

--rseqc_bed /ref/hg38_RefSeq.bed \

--strandedness reverse \

--outdir results/

```

### SLURM HPC

```bash

nextflow run scripts/main.nf -profile slurm \

--container /path/to/pipeline-rnaseq.sif \

--slurm_queue normal \

--reads 'fastq/*_R{1,2}.fq.gz' \

--genome GRCh38 \

--star_index /ref/GRCh38_star_index \

--rsem_index /ref/GRCh38_rsem_index/GRCh38 \

--kallisto_index /ref/gencode.v38.kallisto.idx \

--rseqc_bed /ref/hg38_RefSeq.bed \

--outdir results/

```

### Cloud

```bash

# Google Cloud Batch

nextflow run scripts/main.nf -profile gcp \

--container us-docker.pkg.dev/<project>/<repo>/pipeline-rnaseq:1.0.0 \

--gcp_project <project> \

--gcp_workdir gs://<bucket>/work \

--reads 'gs://<bucket>/fastq/*_R{1,2}.fq.gz' \

--genome GRCh38 \

--star_index gs://<bucket>/ref/GRCh38_star_index \

--rsem_index gs://<bucket>/ref/GRCh38_rsem_index/GRCh38 \

--kallisto_index gs://<bucket>/ref/gencode.v38.kallisto.idx \

--rseqc_bed gs://<bucket>/ref/hg38_RefSeq.bed \

--outdir gs://<bucket>/results

# AWS Batch

nextflow run scripts/main.nf -profile aws \

--container <account>.dkr.ecr.<region>.amazonaws.com/pipeline-rnaseq:1.0.0 \

--aws_queue <job-queue> \

--aws_workdir s3://<bucket>/work \

--reads 's3://<bucket>/fastq/*_R{1,2}.fq.gz' \

--genome GRCh38 \

--star_index s3://<bucket>/ref/GRCh38_star_index \

--rsem_index s3://<bucket>/ref/GRCh38_rsem_index/GRCh38 \

--kallisto_index s3://<bucket>/ref/gencode.v38.kallisto.idx \

--rseqc_bed s3://<bucket>/ref/hg38_RefSeq.bed \

--outdir s3://<bucket>/results

```

`--outdir` only sets where results are published; Google Batch and AWS Batch stage every

task through the work directory, and the workflow stops with an error if it or the

project/queue is missing.

## Pipeline Parameters

| Parameter | Default | Description |

|-----------|---------|-------------|

| `--reads` | required | Glob matching the FASTQ pairs, e.g. `'fastq/*_R{1,2}.fq.gz'`. Quote it |

| `--genome` | `GRCh38` | `GRCh38` or `mm10`; selects the default index names and nothing else |

| `--outdir` | `./results` | Directory results are published to |

| `--single_end` | `false` | Treat `--reads` as single files; kallisto then runs with the fixed `--single -l 200 -s 20`, and RSeQC `inner_distance.py` is skipped |

| `--strandedness` | `reverse` | `reverse`, `forward` or `none`; one value for the whole run |

| `--skip_kallisto` | `false` | Skip `KALLISTO_QUANT`; `--kallisto_index` is then not read |

| `--star_index` | `GRCh38_star_index` (`mm10_star_index`) | STAR genome directory |

| `--rsem_index` | `GRCh38_rsem_index/GRCh38` (`mm10_rsem_index/mm10`) | RSEM reference **prefix**, not a directory; every file starting with it is staged |

| `--rseqc_bed` | `hg38_RefSeq.bed` (`mm10_RefSeq.bed`) | BED12 gene model used by all four RSeQC modules |

| `--chrom_sizes` | `<star_index>/chrNameLength.txt` | Chromosome sizes for `bedGraphToBigWig` |

### Infrastructure parameters (`nextflow.config`)

| Parameter | Default | Description |

|-----------|---------|-------------|

| `--container` | `encode-toolkit/pipeline-rnaseq:1.0.0` | Image built from `scripts/Dockerfile`. Pass a registry image for `gcp`/`aws`, or a `.sif` file for `slurm` |

| `--max_cpus`, `--max_memory`, `--max_time` | `16`, `64.GB`, `24.h` | Upper bounds applied to every process |

| `--slurm_queue`, `--slurm_account` | `normal`, none | SLURM partition and account |

| `--gcp_project`, `--gcp_workdir` | none (both required for `-profile gcp`) | Google Cloud project and `gs://` work directory |

| `--gcp_location`, `--gcp_disk` | `us-central1`, `200.GB` | Google Batch region and per-task disk |

| `--aws_queue`, `--aws_workdir` | none (both required for `-profile aws`) | AWS Batch job queue and `s3://` work directory |

| `--aws_region`, `--aws_cli_path` | `us-east-1`, `/home/ec2-user/miniconda/bin/aws` | AWS region, and the AWS CLI path inside the Batch AMI |

## Cloud Cost Estimates

| Platform | Instance | Cost/Sample | Time/Sample | Notes |

|----------|----------|-------------|-------------|-------|

| GCP | n1-highmem-8 | ~$3-6 | 2-4 hours | STAR index loading dominates; the `gcp` profile uses spot VMs |

| AWS | r5.2xlarge | ~$3-6 | 2-4 hours | r-series for STAR memory; spot recommended |

| Local | 8 cores, 36 GB | $0 | 3-6 hours | Docker required |

| SLURM | 8 cores, 36 GB | Varies | 2-4 hours | Singularity; pass the `.sif` with `--container` |

with a larger `--genomeSAsparseD` (which shrinks the loaded index at some cost in

mapping speed) or move to a bigger machine. `--limitGenomeGenerateRAM` is a

`genomeGenerate` option and is not a parameter of this workflow.

## Output Directory Structure

```

results/

fastqc/ # FastQC HTML/zip for raw and trimmed reads

trimmed/

<sample>_R1_val_1.fq.gz, <sample>_R2_val_2.fq.gz

<sample>_R1.fq.gz_trimming_report.txt # one per input file

star/

<sample>.Aligned.sortedByCoord.out.bam

<sample>.Aligned.sortedByCoord.out.bam.bai

<sample>.Aligned.toTranscriptome.out.bam # RSEM input

<sample>.Log.final.out

<sample>.SJ.out.tab

<sample>.ReadsPerGene.out.tab # STAR gene counts

<sample>.Signal.UniqueMultiple.str1.out.bg # plus str2 unless --strandedness none

rsem/

<sample>.genes.results # gene_id, TPM, FPKM, expected_count

<sample>.isoforms.results # transcript_id, TPM, FPKM, IsoPct

<sample>.stat/ # RSEM model statistics, read by MultiQC

kallisto/ # absent with --skip_kallisto

<sample>/abundance.tsv

<sample>/run_info.json

signal/

<sample>_plus.bw, <sample>_minus.bw # or <sample>_unstranded.bw

qc/

rseqc/

<sample>.infer_experiment.txt

<sample>.read_distribution.txt

<sample>.geneBody_coverage.*

<sample>.inner_distance.* # paired-end only

multiqc/

multiqc_report.html

multiqc_data/

pipeline_info/

timeline.html, report.html, trace.txt # Nextflow execution reports

```

## Common Pitfalls

### 1. Insufficient Memory for STAR

STAR loads the whole genome index into memory. `STAR_ALIGN` is configured for 36 GB, so a

32 GB machine will not schedule the task at all under `-profile local`. On shared HPC

systems, check the per-job memory limit before submitting.

### 2. Wrong Strandedness Setting

Using incorrect strandedness results in near-zero gene counts. If you see uniformly low

counts, read `qc/rseqc/<sample>.infer_experiment.txt` and rerun with the matching

`--strandedness`. ENCODE dUTP libraries are `reverse` stranded.

### 3. Using FPKM for Cross-Sample Comparison

FPKM values are not comparable across samples because they depend on total library

composition. Use TPM (comparable across samples) or raw counts with DESeq2/edgeR

normalization for differential expression.

### 4. Ignoring Multi-Mapped Reads

RSEM uses an expectation-maximization algorithm to probabilistically assign multi-mapped

reads. This is critical for gene families and repetitive elements. Do not pre-filter

multi-mappers before RSEM quantification.

### 5. Assuming rRNA Contamination Was Checked

High rRNA contamination (>10%) indicates failed rRNA depletion and reduces effective

sequencing depth. This workflow does not measure it — run the manual check in

references/05-qc-metrics.md against `star/<sample>.Aligned.sortedByCoord.out.bam` before

trusting the quantifications.

### 6. Not Using 2-Pass Mode for Novel Junctions

STAR 1-pass mode only uses annotated splice junctions. 2-pass mode first discovers novel

junctions then re-maps, critical for non-model organisms or samples with extensive

alternative splicing. This workflow always runs `--twopassMode Basic`.

## Pipeline Scripts

| File | Description |

|------|-------------|

| `scripts/main.nf` | Nextflow DSL2 pipeline |

| `scripts/nextflow.config` | Execution profiles (local/slurm/gcp/aws) |

| `scripts/Dockerfile` | Docker build with STAR, RSEM, Kallisto, RSeQC |

## ENCODE Data Integration

After running on your own data, compare with ENCODE reference:

```python

# Find matching ENCODE RNA-seq experiments

encode_search_experiments(

assay_title="total RNA-seq",

organ="pancreas",

biosample_type="tissue"

)

# Download ENCODE gene quantifications for comparison

encode_batch_download(

download_dir="/data/encode_reference/",

output_type="gene quantifications",

assay_title="total RNA-seq",

organ="pancreas",

assembly="GRCh38"

)

# Download ENCODE signal tracks for browser visualization

encode_search_files(

file_format="bigWig",

assay_title="total RNA-seq",

organ="pancreas",

output_type="signal of unique reads"

)

```

## Pitfalls & Edge Cases

- **Strandedness must match library prep**: `--strandedness` feeds RSEM, kallisto and the

signal tracks. Using the wrong value can halve gene counts, assign reads to antisense

genes, and swap the plus/minus bigWigs.

- **rRNA contamination**: rRNA >10% wastes sequencing depth. Ribosomal depletion libraries

should have <5%, poly-A selection libraries <1%. The workflow does not measure it; see

references/05-qc-metrics.md for a manual count, or run Picard CollectRnaSeqMetrics

outside the container.

- **STAR 2-pass mode is required**: The first pass discovers novel splice junctions; the

second pass uses them. Single-pass STAR misses tissue-specific or rare splicing events,

reducing sensitivity for differential exon usage.

- **Gene-level vs transcript-level quantification**: RSEM provides transcript-level estimates but gene-level aggregation is more robust for differential expression. Transcript-level analysis requires many more replicates (≥6).

- **TPM normalization is not for cross-sample comparison**: TPM normalizes within a sample but is NOT appropriate for comparing expression across conditions. Use DESeq2 size factors or TMM normalization for differential expression.

- **Batch effects in multi-lab data**: RNA-seq is highly sensitive to library prep method, sequencer, and lab. Always check for batch effects with PCA before combining datasets from different sources.

## Walkthrough: Processing ENCODE RNA-seq from FASTQ to Gene Quantification

**Goal**: Process raw RNA-seq FASTQ files through the ENCODE pipeline to generate gene expression quantifications (TPM/FPKM) and signal tracks.

**Context**: The ENCODE RNA-seq pipeline uses STAR 2-pass alignment and RSEM quantification, producing both gene-level and transcript-level expression estimates.

### Step 1: Find RNA-seq experiment

```

encode_get_experiment(accession="ENCSR000CPR")

```

Expected output:

```json

{

"accession": "ENCSR000CPR",

"assay_title": "RNA-seq",

"biosample_summary": "K562",

"status": "released"

}

```

### Step 2: List FASTQ files

```

encode_list_files(experiment_accession="ENCSR000CPR", file_format="fastq")

```

```json

```

```bash

nextflow run scripts/main.nf -profile local \

--genome GRCh38 \

--star_index /ref/GRCh38_star_index \

--rsem_index /ref/GRCh38_rsem_index/GRCh38 \

--kallisto_index /ref/gencode.v38.kallisto.idx \

--rseqc_bed /ref/hg38_RefSeq.bed \

--strandedness reverse \

--outdir results/

```

Key pipeline steps:

1. FastQC on the raw reads, quality and adapter trimming with Trim Galore (which also runs FastQC on the trimmed reads)

2. STAR 2-pass alignment (splice-aware), writing the genome BAM, the transcriptome BAM, gene counts and bedGraphs

3. RSEM gene and isoform quantification (TPM, FPKM, expected counts)

4. Kallisto transcript quantification (optional, skipped with `--skip_kallisto`)

5. Signal track generation (bedGraph to bigWig)

6. RSeQC (infer_experiment, read_distribution, geneBody_coverage, inner_distance) and MultiQC

| Metric | Threshold | Where to read it |

|---|---|---|

| Uniquely mapped rate | >= 70% | `star/<sample>.Log.final.out` |

| Strandedness agreement | > 90% and matching `--strandedness` | `qc/rseqc/<sample>.infer_experiment.txt` |

| Exonic rate | > 60% | `qc/rseqc/<sample>.read_distribution.txt` |

| Replicate correlation | >= 0.9 | compute yourself from the TPM column of `rsem/<sample>.genes.results` |

Compare gene expression with enhancer marks:

```

encode_search_experiments(assay_title="Histone ChIP-seq", biosample_term_name="K562", target="H3K27ac", organism="Homo sapiens")

```

**Interpretation**: Genes with high TPM AND nearby H3K27ac peaks have validated enhancer-gene connections. Low expression despite nearby enhancer marks suggests poised or tissue-specific regulation.

### Integration with downstream skills

- Gene quantifications feed into -> **peak-annotation** for expression-validated peak targets

- Expression data connects to -> **gtex-expression** for tissue comparison

- Processed data feeds into -> **compare-biosamples** for differential expression analysis

- Pipeline provenance logged by -> **data-provenance**

## Code Examples

### 1. Find RNA-seq experiments for a tissue

```

encode_search_experiments(assay_title="total RNA-seq", organ="liver", organism="Homo sapiens")

```

Expected output:

```json

{

"results": [

}

```

### 2. Check for existing gene quantifications

```

encode_list_files(experiment_accession="ENCSR300RNA", file_format="tsv", output_type="gene quantifications", assembly="GRCh38")

```

```json

```

### 3. Download expression data

```

encode_download_files(file_accessions=["ENCFF400GEQ"], download_dir="/data/rnaseq/quantification")

```

```json

{

}

```

## Integration

| This skill produces... | Feed into... | Purpose |

|---|---|---|

| Gene expression (TPM/FPKM) | **peak-annotation** | Validate enhancer targets with expression data |

| Expression matrix | **gtex-expression** | Compare cell-line vs. tissue expression |

| Differential expression results | **compare-biosamples** | Identify tissue-specific gene regulation |

| Signal tracks (bigWig) | **visualization-workflow** | Display expression signal in genome browser |

| Expression quantifications | **disease-research** | Connect gene expression to disease phenotypes |

| Pipeline run parameters | **data-provenance** | Record STAR/RSEM versions and settings |

| QC metrics | **quality-assessment** | Validate against ENCODE RNA-seq standards |

## Related Skills

- **pipeline-guide** (parent): General pipeline selection and resource assessment

- **quality-assessment**: Deep-dive QC analysis beyond basic metrics

- **integrative-analysis**: Combine RNA-seq with ChIP-seq/ATAC-seq for regulatory inference

- **compare-biosamples**: Compare expression profiles across cell types

- **single-cell-encode**: For scRNA-seq data processing (different pipeline)

- **pipeline-chipseq**: Sibling pipeline for ChIP-seq data

- **pipeline-atacseq**: Sibling pipeline for ATAC-seq data

- **publication-trust**: Verify literature claims backing analytical decisions

## Presenting Results

When reporting RNA-seq pipeline results:

- **Mapping rate**: Report the STAR uniquely mapped rate (>70% expected), multi-mapped rate (<10%), and unmapped rate from `star/<sample>.Log.final.out`

- **Quantification paths**: Provide paths to `rsem/<sample>.genes.results` (TPM, FPKM, expected_count), `rsem/<sample>.isoforms.results`, and `kallisto/<sample>/abundance.tsv` when Kallisto ran

- **Strandedness**: Confirm the orientation reported in `qc/rseqc/<sample>.infer_experiment.txt` matches the `--strandedness` value the run used; if it does not, the run has to be repeated

- **Key QC metrics**: Present the exonic rate from `read_distribution.txt` and the gene body coverage uniformity from `geneBody_coverage.geneBodyCoverage.txt` in a summary table, alongside the MultiQC report at `qc/multiqc/multiqc_report.html`

- **Derived metrics**: Detected-gene counts (TPM>1), rRNA rate, library duplication rate and saturation are not produced by this workflow. Compute them separately if they are needed, and say so when reporting

- **Signal tracks**: Provide paths to `signal/<sample>_plus.bw` and `signal/<sample>_minus.bw` (or `signal/<sample>_unstranded.bw` for an unstranded run)

- **Next steps**: Suggest `integrative-analysis` to combine RNA-seq with ChIP-seq/ATAC-seq for regulatory inference, or `compare-biosamples` for cross-tissue expression comparison

## For the request: "$ARGUMENTS"