Installation

It is recommended to execute hashFrag in a conda or virtualenv environment with Python version 3.10.

pip install

We recommend installing hashFrag using the following pip command:

pip install hashFrag

Clone repository

Clone the repository using the following command:

git clone https://github.com/de-Boer-Lab/hashFrag.git

Export the source directory to your PATH:

export PATH="$PATH:./hashFrag/src"

(Optional) To avoid running the above command every time you open a terminal, add it to your shell configuration file (e.g., ~/.bashrc) with the following command:

echo 'export PATH="$PATH:./hashFrag/src"' >> ~/.bashrc

Installing dependencies

If you are managing your virtual environment with Anaconda or Miniconda, you can directly install dependencies upon creation of the conda environment using the command:

conda env create -n hashFrag -f environment.yml

This creates a conda environment named “hashFrag”

Alternatively, you can install dependencies located in the requirements.txt file with the folowing pip command:

pip install -r requirements.txt

BLAST+ download

To install the suite of BLAST applications, follow the instructions found at the NCBI BLAST Command Line Applications User Manual here.

Directly download the BLAST+ package executables for different operating systems at the following FTP page:

https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/

Follow the instructions to extract the downloaded file and export binaries to your PATH.

Verify that blastn and makeblastdb commands were installed succesfully:

blastn -version
makeblastdb -version

A minor note on the use of BLAST alignment scores in hashFrag

The lightning (default) mode of hashFrag uses pairwise local alignment scores derived from the BLAST algorithm. Rather than directly using the provided alignment scores, however, a corrected version of the alignment score is calculated.

Gap scoring is designed to reflect the biological occurrence of insertions and deletions in sequences. Typically, opening a gap incurs a larger penalty (gapopen), while subsequent extension of the same gap incurs smaller penalties (gapextend). Upon encountering a gap opening event, the BLAST algorithm applies the gapextend penalty in addition to a gapopen penalty. To conform to exact local alignment scoring conventions, we adjust the BLAST scores such that only the gapopen penalty is applied to gap opening events.