v1.02.7

• Added GPL License
• Added DOI through Zenodo

1.02.6

• Module trim_reads_and_QC now saves the fastqc.zip file as well, useful for multiQC
• Added functions to integrate the variant calls of different runs
• Conda installation is more stepwise now
• The bcftools env now includes libopenblas=0.3.20

v1.02.5

Minor fixes to improve usability:

• More verbose VEP output for better debugging.
• VEP now does not throw an error if there are >10% of unannotated variants, but a warning. This can be useful if you have chromosomes with no annotations.
• The align_reads and trim_reads_and_QC module now make sure that the reads are in .gz format.
• The call_CNVs module now checks that all chromosomes have at least 2 windows to do CNV calling. This is necessary to not get errors with CONY and HMMcopy.
• Better documentation for the call_CNVs module
• Improved output of the environmental activation of conda.
• Prints a warning if the simulated coverage is different than the original one

v1.02.4

• Added modular perSVade
• perSVade align_reads checks that the input read pairs are not equal
• Added the test_installation_modules.py, which is the default way of testing perSVade

1.02.3

• Fixed gff and environment checking

1.02

• The samtools view cmd ‘broken pipe’ warning is fixed in get_read_length
• I added an option to execute modules
• Changed all the arround to around in the production perSVade in the scripts perSVade.py and sv_functions.py. Note that the folder testing/ contains scripts based on v1.0, which still uses the arround instead of around. You should run the scripts in testing/ with v1.0.
• Added --downsampled_coverage option.
• Fixed a bug in the definition of the log_file_all_cmds, introduced in v1.01.
• Tested that the docker image works
• Added docker folder

1.01

There was a problem with the mtDNA chromosomes which is now fixed
I also added mtDNA as a mandatory argument
I created a file that saves all the cmd calls

v1.0

First version of perSVade.

v0.10

Fixed bugs in per gene CNV calling.

v0.9

Improved errors in SV_CNV file generation and annotation
Implemented functions for the integration of SVs
Tested scalability up to human datasets