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