TIN & Gene Body Coverage Outputs

RustQC includes two complementary RNA integrity analyses:

• TIN (Transcript Integrity Number) — reimplementation of RSeQC’s tin.py, measuring transcript-level coverage uniformity via Shannon entropy
• Gene body coverage — Qualimap-compatible output showing coverage distribution along normalized gene body positions

Both run automatically as part of rustqc rna in the same single-pass BAM scan.

TIN (Transcript Integrity Number)

What is TIN?

TIN measures the uniformity of read coverage across a transcript. A TIN score of 100 means perfectly uniform coverage; lower scores indicate degradation or bias. TIN is computed using Shannon entropy of read-start coverage at sampled positions along each transcript’s exonic regions.

TIN is particularly useful for:

• Detecting RNA degradation — degraded samples show low TIN scores across most transcripts
• Sample QC — median TIN provides a single-number summary of RNA integrity, complementing RIN (RNA Integrity Number) from Bioanalyzer/TapeStation
• Identifying problematic samples — samples with unusually low median TIN should be flagged for potential exclusion

Output files

TIN output files use the BAM file stem as a prefix and are written to a rseqc/tin/ subdirectory under the output directory.

• Directoryrseqc/

  • Directorytin/

    • sample.tin.xls Per-transcript TIN scores
    • sample.summary.txt Summary statistics (mean, median, stdev)

TIN scores

File: <sample>.tin.xls

A tab-separated file with one row per transcript (gene), reporting the TIN score for each:

Column	Description
geneID	Gene identifier from the annotation
chrom	Chromosome
tx_start	Transcript start position
tx_end	Transcript end position
TIN	Transcript Integrity Number (0-100)

Transcripts below the minimum coverage threshold are excluded from the output. TIN values are formatted to 2 decimal places.

Example:

geneID  chrom  tx_start  tx_end  TIN
ENSG00000227232.6  chr1  14695  24886  74.86
ENSG00000268903.1  chr1  135140  135895  60.32
ENSG00000269981.1  chr1  137681  137965  85.89

Summary

File: <sample>.summary.txt

A single-row tab-separated summary of TIN statistics across all transcripts:

Column	Description
Bam_file	Path to the input BAM file
TIN(mean)	Mean TIN across all transcripts
TIN(median)	Median TIN
TIN(stdev)	Standard deviation of TIN

Example:

Bam_file  TIN(mean)  TIN(median)  TIN(stdev)
sample.bam  72.55  83.89  26.14

Interpreting TIN scores

Median TIN	Interpretation
> 70	Good RNA integrity
50-70	Moderate degradation
< 50	Significant degradation — consider excluding
< 30	Severe degradation

A bimodal distribution of per-transcript TIN scores (some very high, some very low) can indicate selective degradation of certain transcript classes rather than global degradation.

Configuration

tin:
  enabled: true
  sample_size: 100    # Equally-spaced positions to sample per transcript
  min_coverage: 10    # Minimum read-start count to compute TIN

TIN uses the shared --mapq / -q flag for mapping quality filtering.

Gene body coverage

What is gene body coverage?

Gene body coverage profiles show the distribution of read coverage along normalized transcript positions from 5’ to 3’. Each transcript is divided into 100 equal percentile bins, and coverage depth is accumulated across all expressed genes. This reveals systematic biases such as:

• 3’ bias — typical of degraded RNA or oligo-dT primed libraries
• 5’ bias — can indicate incomplete reverse transcription
• Uniform coverage — ideal result, expected from high-quality libraries

Output files

Gene body coverage output is written to a qualimap/ subdirectory in a format compatible with Qualimap rnaseq output, which MultiQC can parse directly.

• Directoryqualimap/

  • coverage_profile_along_genes_(total).txt Coverage profile (100 bins)
  • rnaseq_qc_results.txt Qualimap-compatible QC summary

Coverage profile

File: coverage_profile_along_genes_(total).txt

A tab-separated file with 100 rows, one per percentile bin (0-99), showing the cumulative coverage depth at each normalized position along the gene body:

Column	Description
Position	Percentile position along gene body (0.0 to 99.0)
Coverage	Cumulative read depth at this position across all genes

QC results

File: rnaseq_qc_results.txt

A Qualimap rnaseq-compatible text file with four sections:

• Input — BAM file name
• Reads alignment — total reads, alignments, secondary, aligned to genes, ambiguous, no feature assigned
• Reads genomic origin — exonic, intronic, and intergenic base counts with percentages; overlapping exon count
• Transcript coverage profile — 5’ bias, 3’ bias, and 5’-3’ bias ratios

Example:

>>>>>>> Input
     bam file = sample

>>>>>>> Reads alignment
     reads aligned = 175,097,721
     total alignments = 185,718,543
     secondary alignments = 10,620,822

>>>>>>> Reads genomic origin
     exonic = 744,941,713 (8.29%)
     intronic = 837,462,849 (9.32%)
     intergenic = 7,406,659,875 (82.40%)

>>>>>>> Transcript coverage profile
     5' bias = 1.20
     3' bias = 0.92
     5'-3' bias = 1.24

Configuration

genebody_coverage:
  enabled: true    # Whether to produce gene body coverage output

Gene body coverage requires GTF annotation (--gtf mode) — it is not available in BED-only mode because it requires transcript-level exon structure to normalize positions along the gene body.

Compatibility

TIN

RustQC’s TIN output uses the same file format and column names as RSeQC’s tin.py. The key difference is that RustQC reports one row per gene (using the longest transcript as representative), while RSeQC reports one row per transcript. TIN scores are computed using the same Shannon entropy formula.

Gene body coverage

The Qualimap-compatible output files are designed for direct parsing by MultiQC’s Qualimap rnaseq module. The coverage profile format and QC results sections match Qualimap’s output structure.

References

• TIN: Wang L, Nie J, Sicotte H, et al. Measure transcript integrity using RNA-seq data. BMC Bioinformatics. 2016;17:58.
• RSeQC: Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2012;28(16):2184-2185. RSeQC website
• Qualimap: Garcia-Alcalde F, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28(20):2678-2679. Qualimap website