git:20260321.d9029d0 to git:20260920.ce7840d

---

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

```

```

### Stage Summary

| Stage | Tool | Input | Output | Reference |

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

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

## Input Requirements

### Required Files

## Library Strandedness

## QC Thresholds

### 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 |

## Execution

```bash

```

### SLURM HPC

```bash

```

```bash

```

## Cloud Cost Estimates

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

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

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

## Output Directory Structure

```

results/

star/

rsem/

signal/

qc/

```

## Common Pitfalls

### 1. Insufficient Memory for STAR

### 2. Wrong Strandedness Setting

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

### 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.

### 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

## Pipeline Scripts

## 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

- **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",

"replicates": 2,

"status": "released"

}

```

### Step 2: List FASTQ files

```

```

Expected output:

```json

{

"files": [

{"accession": "ENCFF200RN1", "output_type": "reads", "paired_end": "1", "biological_replicates": [1], "file_size_mb": 3200},

{"accession": "ENCFF201RN2", "output_type": "reads", "paired_end": "2", "biological_replicates": [1], "file_size_mb": 3300}

]

}

```

### Step 3: Run the RNA-seq pipeline

```bash

```

Key pipeline steps:

### Step 4: Validate output quality

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

### Step 5: Use expression data with ENCODE epigenomic data

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

{

"total": 35,

"results": [

{"accession": "ENCSR300RNA", "assay_title": "RNA-seq", "biosample_summary": "liver", "status": "released"}

]

}

```

### 2. Check for existing gene quantifications

```

```

Expected output:

```json

{

"files": [

{"accession": "ENCFF400GEQ", "output_type": "gene quantifications", "file_format": "tsv", "file_size_mb": 5.2}

]

}

```

### 3. Download expression data

```

```

Expected output:

```json

{

"downloaded": 1,

"md5_verified": true,

"files": ["/data/rnaseq/quantification/ENCFF400GEQ.tsv"]

}

```

## 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:

- **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"