Duplication rate
RSeQC Outputs
RustQC reimplements seven tools from the RSeQC package. Each tool produces output files that match the format and content of the original Python implementation, plus native PNG and SVG plots generated directly — no R scripts required.
All RSeQC tools run automatically as part of the rustqc rna command and use
the input filename stem as a prefix for output files. Output files are organized
into per-tool subdirectories under rseqc/. For example,
rustqc rna sample.bam --gtf genes.gtf -o results/ produces
RSeQC output files like results/rseqc/bam_stat/sample.bam_stat.txt.
Use --flat-output to write all files directly to the output directory instead
of the nested rseqc/<tool>/ subdirectories.
bam_stat
Section titled “bam_stat”Basic alignment statistics from a single-pass BAM scan.
| File | Description |
|---|---|
{stem}.bam_stat.txt | Formatted text report with total records, QC failures, duplicates, mapping quality distribution, splice reads, proper pairs, and more |
The output format matches bam_stat.py exactly, including the same section
headings and number formatting. Key metrics include:
- Total records, QC-failed, duplicates, non-primary, unmapped
- Unique and multi-mapped read counts at the configured MAPQ cutoff
- Read 1 / Read 2 counts, forward/reverse strand counts
- Splice / non-splice read counts
- Proper pair and paired-on-different-chromosome counts
- MAPQ distribution histogram
infer_experiment
Section titled “infer_experiment”Library strandedness inference by sampling reads overlapping gene models.
| File | Description |
|---|---|
{stem}.infer_experiment.txt | Strandedness fractions: failed-to-determine, and the two strand protocols |
The output reports the fraction of reads consistent with each strand protocol:
- Fraction failed to determine — reads that could not be assigned to either protocol
- Fraction “1++,1—,2+-,2-+” (forward stranded) — reads consistent with the same-strand protocol
- Fraction “1+-,1-+,2++,2—” (reverse stranded) — reads consistent with the opposite-strand protocol
For paired-end data, the labels are PairEnd with 1++,1--,2+-,2-+ and
1+-,1-+,2++,2--. For single-end: SingleEnd with ++,-- and +-,-+.
Interpreting results:
- Both fractions near 50% = unstranded library
- First fraction near 100% = forward stranded (e.g., Ligation protocol)
- Second fraction near 100% = reverse stranded (e.g., dUTP protocol, most common)
read_duplication
Section titled “read_duplication”Position-based and sequence-based duplication rate histograms.
| File | Description |
|---|---|
{stem}.pos.DupRate.xls | Position-based duplication histogram (TSV: Occurrence, UniqReadNumber, ReadNumber) |
{stem}.seq.DupRate.xls | Sequence-based duplication histogram (TSV: Occurrence, UniqReadNumber, ReadNumber) |
{stem}.DupRate_plot.png | Duplication rate plot (PNG) |
{stem}.DupRate_plot.svg | Duplication rate plot (SVG) |
Each TSV file is a tab-separated table where each row represents a duplication level (number of times a read was seen). The columns are:
- Occurrence — the duplication count (1 = unique, 2 = seen twice, etc.)
- UniqReadNumber — number of unique read groups at this duplication level
- ReadNumber — total reads consumed (Occurrence x UniqReadNumber)
Position-based deduplication groups reads by alignment position (chromosome, start, CIGAR-derived exon blocks). Sequence-based deduplication groups reads by the actual read sequence.
Plot files
Section titled “Plot files”The duplication rate plot shows two curves: sequence-based (blue) and position-based (red) duplication. The x-axis shows the occurrence count (capped at 500) and the y-axis shows the number of reads at each duplication level. Most reads should appear at low occurrence counts; a long tail indicates high duplication.
read_distribution
Section titled “read_distribution”Classification of reads across genomic feature types.
| File | Description |
|---|---|
{stem}.read_distribution.txt | Tabular report with total reads, total tags, and per-region breakdown |
The output includes:
- Total Reads and Total Tags (CIGAR M-block midpoints)
- A table of genomic regions with columns: Group, Total_bases, Tag_count, Tags/Kb
- Regions: CDS_Exons, 5’UTR_Exons, 3’UTR_Exons, Introns, TSS_up_1kb, TSS_up_5kb, TSS_up_10kb, TES_down_1kb, TES_down_5kb, TES_down_10kb
- Tags assigned to each region (with priority: CDS > UTR > Intron > Intergenic)
junction_annotation
Section titled “junction_annotation”Splice junction classification against a reference gene model.
| File | Description |
|---|---|
{stem}.junction.xls | TSV with all observed junctions: chrom, intron_start(0-based), intron_end(1-based), read_count, annotation_status |
{stem}.junction.bed | BED12 file with color-coded junctions (red = known, green = partial novel, blue = complete novel) |
{stem}.junction_plot.r | R script for generating splice event and junction pie charts |
{stem}.splice_events.png | Splice events pie chart (PNG) |
{stem}.splice_events.svg | Splice events pie chart (SVG) |
{stem}.splice_junction.png | Splice junctions pie chart (PNG) |
{stem}.splice_junction.svg | Splice junctions pie chart (SVG) |
{stem}.junction_annotation.txt | Summary: total/known/partial novel/complete novel event and junction counts |
Junctions are classified by comparing splice sites (CIGAR N-operations) against the reference BED12 gene model:
- Known (Annotated) — both donor and acceptor sites match known introns
- Partial novel — one splice site matches, the other is novel
- Complete novel — neither splice site matches any known intron
Plot files
Section titled “Plot files”Two pie charts are generated, one for splice events (reads) and one for splice junctions (unique splice sites). Each chart shows the proportion of known (blue), partial novel (red), and complete novel (green) splicing.
Splice events
Splice junctions
junction_saturation
Section titled “junction_saturation”Splice junction discovery rate at increasing sequencing depths.
| File | Description |
|---|---|
{stem}.junctionSaturation_plot.r | R script for saturation curve plots |
{stem}.junctionSaturation_plot.png | Junction saturation plot (PNG) |
{stem}.junctionSaturation_plot.svg | Junction saturation plot (SVG) |
{stem}.junctionSaturation_summary.txt | TSV: percent_of_reads, known_junctions, novel_junctions, all_junctions |
The tool subsamples reads at configurable percentages (default: 5%, 10%, …, 100%) and counts how many unique known and novel junctions are detected at each level. This reveals whether sequencing depth is sufficient for comprehensive junction detection. A saturated library will show a plateau in the curve; an unsaturated library will show continuing growth.
Plot files
Section titled “Plot files”The junction saturation plot shows three lines: all junctions (blue), known junctions (red), and novel junctions (green). The x-axis is the percentage of total reads used and the y-axis is the number of splice junctions detected (in thousands). A plateau in the curves indicates saturation.
Junction saturation
inner_distance
Section titled “inner_distance”Fragment inner distance for paired-end RNA-seq libraries.
| File | Description |
|---|---|
{stem}.inner_distance.txt | Per-pair detail: readpair_name, inner_distance, classification |
{stem}.inner_distance_freq.txt | Histogram: lower_bound, upper_bound, count |
{stem}.inner_distance_plot.r | R script for histogram and density plot |
{stem}.inner_distance_plot.png | Inner distance histogram with density overlay (PNG) |
{stem}.inner_distance_plot.svg | Inner distance histogram with density overlay (SVG) |
{stem}.inner_distance_summary.txt | Summary counts by pair classification |
The inner distance is defined as the gap between the end of read 1 and the start of read 2 on the mRNA transcript. Negative values indicate read overlap. Pairs are classified as:
- sameTranscript=Yes, sameExon=Yes — both reads on the same exon
- sameTranscript=Yes, sameExon=No — reads on the same transcript, different exons (distance calculated on mRNA)
- sameTranscript=No — reads on different transcripts or no transcript overlap
- readPairOverlap — reads overlap each other
- nonExonic — one or both reads not on exons
- sameChrom=No — reads on different chromosomes
- unknownChromosome — chromosome not in the gene model
The histogram bins are configurable via --inner-distance-lower-bound,
--inner-distance-upper-bound, and --inner-distance-step (defaults: -250 to 250, step 5).
Plot files
Section titled “Plot files”The inner distance plot is a histogram showing the distribution of fragment inner distances, with a Gaussian density curve (red) overlaid. The title displays the mean and standard deviation. The distribution should be approximately normal for a well-prepared library.
Inner distance
tin (Transcript Integrity Number)
Section titled “tin (Transcript Integrity Number)”Measures transcript integrity via Shannon entropy of read coverage uniformity.
A reimplementation of RSeQC’s tin.py tool. For full details, see the
dedicated TIN & Gene Body Coverage documentation page.
| File | Description |
|---|---|
{stem}.tin.xls | Per-gene TIN scores (TSV: geneID, chrom, tx_start, tx_end, TIN) |
{stem}.summary.txt | Summary statistics: mean, median, and standard deviation of TIN scores |
TIN scores range from 0 (completely degraded) to 100 (perfectly uniform coverage). The tool uses the longest transcript per gene and samples equally-spaced positions within exonic regions.
Compatibility with RSeQC
Section titled “Compatibility with RSeQC”All output files are designed to be drop-in replacements for the corresponding RSeQC Python tool output. File formats, column names, and numeric precision match the Python originals to facilitate migration from RSeQC to RustQC without downstream pipeline changes.
RustQC also generates the R scripts that RSeQC produces (for junction_annotation, junction_saturation, and inner_distance), maintaining full compatibility. However, unlike RSeQC, RustQC generates the plots directly as PNG and SVG files — there is no need to run the R scripts separately.