| name | bio-rnaseq-qc |
| description | RNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis. |
| tool_type | mixed |
| primary_tool | RSeQC |
Version Compatibility
Reference examples tested with: NCBI BLAST+ 2.15+, numpy 1.26+, picard 3.1+, pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package> then help(module.function) to check signatures
- CLI:
<tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
RNA-seq Quality Control
RNA-seq specific QC metrics beyond general read quality.
"Check RNA-seq alignment quality" → Assess gene body coverage, read distribution (exonic/intronic/intergenic), strand specificity, and rRNA contamination rate.
- CLI:
infer_experiment.py, read_distribution.py (RSeQC)
- CLI:
picard CollectRnaSeqMetrics
rRNA Contamination Detection
High rRNA content indicates failed rRNA depletion or polyA selection.
SortMeRNA (NCBI BLAST+)
sortmerna \
--ref rRNA_databases/smr_v4.3_default_db.fasta \
--reads sample.fastq.gz \
--aligned rRNA_reads \
--other non_rRNA_reads \
--fastx \
--threads 8
rrna_count=$(grep -c "^@" rRNA_reads.fastq 2>/dev/null || echo 0)
total_count=$(zcat sample.fastq.gz | grep -c "^@")
rrna_pct=$(echo "scale=2; $rrna_count / $total_count * 100" | bc)
echo "rRNA: ${rrna_pct}%"
BLAST Against rRNA (NCBI BLAST+)
seqkit sample -n 10000 sample.fastq.gz | seqkit fq2fa > sample_10k.fasta
blastn -query sample_10k.fasta -db rrna_db -outfmt 6 -evalue 1e-10 -max_target_seqs 1 | wc -l
Expected rRNA Levels
| Library Type | Expected rRNA |
|---|
| PolyA selected | < 5% |
| rRNA depleted | < 10% |
| Total RNA | 50-80% |
Strandedness Verification
RSeQC infer_experiment (NCBI BLAST+)
infer_experiment.py -i aligned.bam -r genes.bed
Output Interpretation
Fraction of reads explained by "1++,1--,2+-,2-+": 0.9856 # Forward stranded
Fraction of reads explained by "1+-,1-+,2++,2--": 0.0144 # Reverse (should be low)
Strand Inference
| Tool Setting | 1++,1--,2+-,2-+ | 1+-,1-+,2++,2-- |
|---|
| Forward (dUTP) | ~0 | ~1 |
| Reverse (Illumina) | ~1 | ~0 |
| Unstranded | ~0.5 | ~0.5 |
Salmon Strandedness (NCBI BLAST+)
salmon quant -i index -l A -r sample.fastq.gz -o quant/
grep "library_types" quant/lib_format_counts.json
Gene Body Coverage
Check for 3' or 5' bias indicating RNA degradation.
RSeQC geneBody_coverage (NCBI BLAST+)
geneBody_coverage.py \
-i aligned.bam \
-r housekeeping_genes.bed \
-o coverage
Interpretation
| Pattern | Indicates |
|---|
| Even coverage | Good quality |
| 3' bias | Degradation or polyA artifacts |
| 5' bias | Incomplete reverse transcription |
| Steep drop | Severe degradation |
Read Distribution
RSeQC read_distribution (NCBI BLAST+)
read_distribution.py -i aligned.bam -r genes.bed > distribution.txt
Expected Distribution
| Region | Good Library |
|---|
| CDS_Exons | 60-80% |
| UTRs | 10-20% |
| Introns | 5-20% |
| Intergenic | < 10% |
Transcript Integrity Number (TIN)
Measure of RNA degradation per transcript.
RSeQC tin (NCBI BLAST+)
tin.py -i aligned.bam -r genes.bed > tin_scores.txt
TIN Interpretation
| TIN Score | Quality |
|---|
| > 70 | Good |
| 50-70 | Moderate |
| < 50 | Poor |
Duplication Rate
Picard MarkDuplicates (NCBI BLAST+)
java -jar picard.jar MarkDuplicates \
I=aligned.bam \
O=marked.bam \
M=dup_metrics.txt \
REMOVE_DUPLICATES=false
grep -A 1 "LIBRARY" dup_metrics.txt | tail -1 | cut -f9
RNA-seq Expected Duplication
| Library | Expected |
|---|
| High complexity | < 20% |
| Low input | 20-50% |
| Concerning | > 50% |
Insert Size (Paired-End)
Picard CollectInsertSizeMetrics (NCBI BLAST+)
java -jar picard.jar CollectInsertSizeMetrics \
I=aligned.bam \
O=insert_metrics.txt \
H=insert_histogram.pdf
Saturation Analysis
Subsampling Analysis
for frac in 0.1 0.25 0.5 0.75 1.0; do
samtools view -bs $frac aligned.bam > sub_${frac}.bam
featureCounts -a genes.gtf -o counts_${frac}.txt sub_${frac}.bam
detected=$(awk '$7 > 0' counts_${frac}.txt | wc -l)
echo "$frac: $detected genes"
done
Picard CollectRnaSeqMetrics
Comprehensive RNA-seq metrics from Picard.
java -jar picard.jar CollectRnaSeqMetrics \
I=aligned.bam \
O=rnaseq_metrics.txt \
REF_FLAT=refFlat.txt \
STRAND=SECOND_READ_TRANSCRIPTION_STRAND \
RIBOSOMAL_INTERVALS=rRNA.interval_list
Key Metrics
| Metric | Description |
|---|
| PCT_CODING_BASES | % in coding regions |
| PCT_UTR_BASES | % in UTRs |
| PCT_INTRONIC_BASES | % in introns |
| PCT_INTERGENIC_BASES | % intergenic |
| PCT_RIBOSOMAL_BASES | % rRNA |
| MEDIAN_5PRIME_TO_3PRIME_BIAS | 3' bias |
MultiQC Report
Aggregate all QC metrics.
multiqc fastqc/ star_output/ featurecounts/ -o multiqc_report/
Complete RNA-seq QC Pipeline (NCBI BLAST+)
Goal: Generate a comprehensive RNA-seq QC report covering strandedness, read distribution, gene body coverage, transcript integrity, duplication, and RNA-seq metrics.
Approach: Run RSeQC tools (infer_experiment, read_distribution, geneBody_coverage, TIN) and Picard (MarkDuplicates, CollectRnaSeqMetrics) sequentially, appending all results to a single summary report file.
#!/bin/bash
SAMPLE=$1
BAM=$2
GENES_BED=$3
REF_FLAT=$4
echo "=== RNA-seq QC: $SAMPLE ===" > qc_report.txt
echo -e "\n--- Strandedness ---" >> qc_report.txt
infer_experiment.py -i $BAM -r $GENES_BED >> qc_report.txt
echo -e "\n--- Read Distribution ---" >> qc_report.txt
read_distribution.py -i $BAM -r $GENES_BED >> qc_report.txt
echo -e "\n--- Gene Body Coverage ---" >> qc_report.txt
geneBody_coverage.py -i $BAM -r $GENES_BED -o coverage
echo -e "\n--- TIN Scores ---" >> qc_report.txt
tin.py -i $BAM -r $GENES_BED > tin.txt
awk '{sum+=$3; count++} END {print "Mean TIN:", sum/count}' tin.txt >> qc_report.txt
echo -e "\n--- Duplication ---" >> qc_report.txt
java -jar picard.jar MarkDuplicates I=$BAM O=/dev/null M=dup.txt 2>/dev/null
grep -A 1 "LIBRARY" dup.txt | tail -1 | awk '{print "Duplication rate:", $9}' >> qc_report.txt
echo -e "\n--- RNA-seq Metrics ---" >> qc_report.txt
java -jar picard.jar CollectRnaSeqMetrics I=$BAM O=rnaseq.txt REF_FLAT=$REF_FLAT STRAND=SECOND_READ_TRANSCRIPTION_STRAND 2>/dev/null
grep -A 2 "## METRICS CLASS" rnaseq.txt >> qc_report.txt
cat qc_report.txt
Python QC Summary
import pysam
import numpy as np
from collections import Counter
def rnaseq_qc(bam_file, sample_size=100000):
bam = pysam.AlignmentFile(bam_file, 'rb')
strand_counts = Counter()
insert_sizes = []
for i, read in enumerate(bam.fetch()):
if i >= sample_size:
break
if not read.is_unmapped:
if read.is_read1:
if read.is_reverse:
strand_counts['1-'] += 1
else:
strand_counts['1+'] += 1
if read.is_proper_pair and read.template_length > 0:
insert_sizes.append(read.template_length)
bam.close()
total = sum(strand_counts.values())
print(f'Read 1 forward: {strand_counts["1+"]/total:.2%}')
print(f'Read 1 reverse: {strand_counts["1-"]/total:.2%}')
if insert_sizes:
print(f'Median insert: {np.median(insert_sizes):.0f}')
rnaseq_qc('aligned.bam')
QC Thresholds Summary
| Metric | Good | Warning | Fail |
|---|
| Mapping rate | > 85% | 70-85% | < 70% |
| rRNA % | < 10% | 10-20% | > 20% |
| Exonic % | > 60% | 40-60% | < 40% |
| Duplication | < 20% | 20-40% | > 40% |
| Mean TIN | > 70 | 50-70 | < 50 |
| 3' bias | < 1.5 | 1.5-2 | > 2 |
Related Skills
- quality-reports - General FastQC
- fastp-workflow - Read trimming
- alignment-files/alignment-validation - General BAM QC
- rna-quantification/featurecounts-counting - Quantification after QC
