TIN score calculation

Given a set of BAM files and a gene annotation BED file, calculates the Transcript Integrity Number (TIN) for each transcript.

Main usage

python calculate-tin.py [-h] [options]

Parameters

  --version             show program's version number and exit
  -h, --help            show this help message and exit
  -i INPUT_FILES, --input=INPUT_FILES
                        Input BAM file(s). "-i" takes these input: 1) a single
                        BAM file. 2) "," separated BAM files (no spaces
                        allowed). 3) directory containing one or more bam
                        files. 4) plain text file containing the path of one
                        or more bam files (Each row is a BAM file path). All
                        BAM files should be sorted and indexed using samtools.
                        [required]
  -r REF_GENE_MODEL, --refgene=REF_GENE_MODEL
                        Reference gene model in BED format. Must be strandard
                        12-column BED file. [required]
  -c MINIMUM_COVERAGE, --minCov=MINIMUM_COVERAGE
                        Minimum number of read mapped to a transcript.
                        default=10
  -n SAMPLE_SIZE, --sample-size=SAMPLE_SIZE
                        Number of equal-spaced nucleotide positions picked
                        from mRNA. Note: if this number is larger than the
                        length of mRNA (L), it will be halved until it's
                        smaller than L. default=100
  --names=SAMPLE_NAMES  sample names, comma separated (no spaces allowed);
                        number must match the number of provided bam_files
  -s, --subtract-background
                        Subtract background noise (estimated from intronic
                        reads). Only use this option if there are substantial
                        intronic reads.
  -p NRPROCESSES, --processes=NRPROCESSES
                        Number of child processes for the parallelization.
                        Default: 1

File formats

• BAM
• BED

Sample output (TSV):

transcript         sample_name
ENST00000303113    80.6328743265
ENST00000427445    0
ENST00000430792    59.7324017312
ENST00000647504    84.8860204563
ENST00000398647    64.4764470574
ENST00000400202    69.6331415873
ENST00000455813    85.3605191157
ENST00000397854    92.3965306733
ENST00000630077    72.8829044591

Information

The tool was forked off the script tin.py (v2.6.4) of the RSeQC package to achieve some speed-up.

This program calculates transcript integrity number (TIN) for each transcript (or gene) in BED file. TIN is conceptually similar to RIN (RNA integrity number) but provides transcript level measurement of RNA quality and is more sensitive to measure low quality RNA samples:

1. TIN score of a transcript is used to measure the RNA integrity of the transcript.
2. Median TIN score across all transcripts can be used to measure RNA integrity of that “RNA sample”.
3. TIN ranges from 0 (the worst) to 100 (the best). TIN = 60 means: 60% of the transcript has been covered if the reads coverage were uniform.
4. TIN will be assigned to 0 if the transcript has no coverage or covered reads is fewer than cutoff.

Extended usage

Additionaly, this repository has been updated with three simple Python scripts.

TIN score merging

Merge TIN score tables for multiple samples.

python merge-tin.py [-h] [options]

Parameters

  -h, --help            show this help message and exit
  -v {DEBUG,INFO,WARN,ERROR,CRITICAL}, --verbosity {DEBUG,INFO,WARN,ERROR,CRITICAL}
                        Verbosity/Log level. Defaults to ERROR
  -l LOGFILE, --logfile LOGFILE
                        Store log to this file.
  --input-files INFILES
                        Space-separated paths to the input tables.
  --output-file OUTFILE
                        Path for the outfile with merged TIN scores

Output file is formatted in a TSV table as well.

TIN score plotting

Create per-sample boxplots of TIN scores.

python plot-tin.py [-h] [options]

Parameters

  -h, --help            show this help message and exit
  -v {DEBUG,INFO,WARN,ERROR,CRITICAL}, --verbosity {DEBUG,INFO,WARN,ERROR,CRITICAL}
                        Verbosity/Log level. Defaults to ERROR
  -l LOGFILE, --logfile LOGFILE
                        Store log to this file.
  --input-file INFILE   Path to the table with merged TIN scores
  --output-file-prefix OUTFILE_PREFIX
                        Prefix for the path to the TIN boxplots.

The boxplots are generated in PDF and PNG formats under output-file-prefix+.pdf and output-file-prefix+.png.

TIN score summary

Calculate simple summary statistics for the per-sample TIN scores.

python summarize-tin.py [-h] [options]

Parameters

  -h, --help            show this help message and exit
  -v {DEBUG,INFO,WARN,ERROR,CRITICAL}, --verbosity {DEBUG,INFO,WARN,ERROR,CRITICAL}
                        Verbosity/Log level. Defaults to ERROR
  -l LOGFILE, --logfile LOGFILE
                        Store log to this file.
  --input-file INFILE   Path to the table with merged TIN scores
  --output-file OUTFILE
                        Path for the output table with TIN statistics.

Output file is formatted in a TSV table as well.

Run locally

In order to use the scripts you will need to clone this repository and install the dependencies:

git clone https://github.com/zavolanlab/tin-score-calculation
cd tin-score-calculation
pip install .

Alternatively you can install it via pypi by:

pip install tin-score-calculation

Alternatively you can install it via conda by:

conda install -c bioconda -c conda-forge tin-score-calculation

NOTES:

• You may want to install dependencies inside a virtual environment, e.g., using virtualenv. Alternatively, if you use conda we provide an environment recipe too - in such case just run conda env create.
• Some of the dependencies require specific system libraries to be installed, this however should be taken care of by the package manager.

You can then find the scripts in directory scripts/ and run it as described in the Main usage and Extended usage sections. To run the tool with minimum test files, try:

calculate-tin.py \
-i .test/calculate-tin/sample.bam \
-r .test/calculate-tin/transcripts.bed \
--names "sample_name" \
1> .test/calculate-tin/test.tsv

merge-tin.py \
--input-files .test/merge-tin/sample_1.tsv .test/merge-tin/sample_2.tsv \
--output-file .test/merge-tin/test.tsv

plot-tin.py \
--input-file .test/plot-tin/merged.tsv \
--output-file-prefix .test/plot-tin/test

summarize-tin.py \
--input-file .test/summarize-tin/merged.tsv \
--output-file .test/summarize-tin/test.tsv

Run inside container

If you have Docker installed, you can also pull the Docker image:

docker pull quay.io/biocontainers/tin-score-calculation:0.6--pyh5e36f6f_0

You can execute the scripts as following:

docker run -it quay.io/biocontainers/tin-score-calculation:0.6--pyh5e36f6f_0 calculate-tin.py --help
docker run -it quay.io/biocontainers/tin-score-calculation:0.6--pyh5e36f6f_0 merge-tin.py --help
docker run -it quay.io/biocontainers/tin-score-calculation:0.6--pyh5e36f6f_0 plot-tin.py --help
docker run -it quay.io/biocontainers/tin-score-calculation:0.6--pyh5e36f6f_0 summarize-tin.py --help

NOTE: To run the tool on your own data in that manner, you will probably need to mount a volume to allow the container read input files and write persistent output from/to the host file system.

Version

0.6.3

Contact

Please see the list of contributors for contact information.