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Error supression and variant calling pipeline for Second-Generation sequencing data

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An error supression and variant calling pipeline for Second-Generation sequencing data


See the full wiki page for more information:



You will need to install the following before installing Dellingr:

  • 3.7>python>=3.4
  • bwa>=0.7.0
  • samtools>=1.3.1

Dellingr will check these dependencies prior to running the pipeline

To install the Dellingr package run the following command:

Install using the Python Package Index (PyPI)

pip install Dellingr

Install from Github

git clone
cd Dellingr
python install

All required python dependencies will be installed during this step

Running Dellingr

The Analysis Pipeline: Very Quick Start

You can view more detailed instructions on the wiki

All parameters required to run ProDuSe can be viewed using the following:

    dellingr run_dellingr -h

Alternatively, if you wish to run Dellingr without installing it, you can run manually in a similar manner:

    /path/to/Dellingr/ -h

While these parameters can be specified individually, they can also be provided using a configuration file

To run the analysis pipeline you simply need to run the following command:

    dellingr run_dellingr
    -c /path/to/github/clone/etc/dellingr_config.ini


    -c /path/to/github/clone/etc/dellingr_config.ini

This will run the entire Dellingr pipeline on all samples specified in the sample_config.ini file, which can be found in etc/sample_config.ini

Results will be located in the following directory:

ls ./dellingr_analysis_directory

Helper Scripts

The Dellingr package is comprised of several stages to aid in the analysis of duplex sequencing data.

These stages can be be viewed by running the following:

dellingr -h

dellingr adapter_predict

If you need to confirm the expected adapter sequence of a sample you should run the following command:

dellingr adapter_predict -i input1.fastq input2.fastq

This tool will print a predicted adapter sequence based off of ACGT abundances at each position. It uses these observed abundances and finds the closest expected abundance for an IUPAC unambiguous or ambiguous base.

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