ta_analyser

This project hosts script to calculate mean FPBM (fragments per base per million) values for TA repeats using samtools bedcov output For detailed description on method to calculate the FPBM values please refer Nature article.

samtools bedcov analyse_ta/data/liftover_broken_ta_sorted_fai.bed.gz test.bam >test_br.bedcov

samtools bedcov analyse_ta/data/liftover_non_broken_ta_sorted_fai.bed.gz test.bam >test_nbr.bedcov

• Design
• Tools
  • analyse_ta
  • inputFormat
  • outputFormat
• INSTALL
  • Package Dependencies
• Development environment
  • Development Dependencies
    • Setup VirtualEnv
• Cutting a release
  • Install via .whl (wheel)
• Reference

Design

Uses pandas>=1.3.1

Tools

analyse_ta has multiple command line options, listed with analyse_ta --help.

analyse_ta

Takes samtools bed coverage as input file for broken and non-broken ta repeat intervals and optional sample_name parameter.

Various exceptions can occur for malformed input files.

inputFormat

• test_br.bedcov bed coverage file for broken TA repeats generated using samtools
• test_nbr.bedcov bed coverage file for non-broken TA repeats generated using samtools
• test_sample sample name to be printed with results

outputFormat

• test_sample 6.12 15.8 command line output <sample_name> <mean_fpbm_broken> <mean_fpbm_non_broken>.

outputFormat with dnovo flag set

• Applicable only to Long Read sequencing data Use of dnovo flag will classify the TA repeat regions into broken and non-broken based on the user defined dnovo_cutoff parmater. This is applicable for long read data where average length of TA repeat is known for each interval
• br:broken
• nbr:non_broken
• test_sample 6.12 15.8 command line output <sample_name> <mean_fpbm_br> <mean_fpbm_nbr> <ref_br> <ref_nbr> <mean_fpbm_dnovo_br> <mean_fpbm_dnovo_br> <dnovo_br> <dnovo_nbr> <dnovo_in_ref_br> <dnovo_in_ref_nbr> <cumulative_fpbm_br> <cumulative_fpbm_nbr> <jaccard_br> <jaccard_nbr>.

INSTALL

Installing via pip install. Simply execute with the path to the compiled 'whl' found on the release page:

python3 setup.py sdist bdist_wheel
pip install analyse_ta.X.X.X-py3-none-any.whl

Release .whl files are generated as part of the release process and can be found on the release page

Development environment

This project uses git pre-commit hooks. As these will execute on your system it is entirely up to you if you activate them.

If you want tests, coverage reports and lint-ing to automatically execute before a commit you can activate them by running:

git config core.hooksPath git-hooks

Only a test failure will block a commit, lint-ing is not enforced (but please consider following the guidance).

You can run the same checks manually without a commit by executing the following in the base of the clone:

./run_tests.sh

Development Dependencies

pytest radon pytest-cov

Setup VirtualEnv

cd $PROJECTROOT
hash virtualenv || pip3 install virtualenv
virtualenv -p python3 env
source env/bin/activate
python setup.py develop # so bin scripts can find module

For testing/coverage (./run_tests.sh)

source env/bin/activate # if not already in env
pip install pytest
pip install radon
pip install pytest-cov

Also see Package Dependancies

Cutting a release

Make sure the version is incremented in ./setup.py

Install via .whl (wheel)

Generate .whl

source env/bin/activate # if not already
python setup.py bdist_wheel -d dist

Install .whl

# this creates an wheel archive which can be copied to a deployment location, e.g.
scp dist/analyse_ta.X.X.X-py3-none-any.whl user@host:~/wheels
# on host
pip install --find-links=~/wheels analyse_ta

Reference