LongReadSum supports FASTA, FASTQ, BAM, FAST5, and sequencing_summary.txt file formats for quick generation of QC data in HTML and text format.
Please refer to the conda environment.yml file for all required packages.
First, install Anaconda.
Next, create a new environment. This installation has been tested with Python 3.10:
conda create -n py10 python=3.10
conda activate py10
LongReadSum can then be installed using the following command:
conda install -c bioconda -c wglab longreadsum=1.3.1
First, install Docker. Pull the latest image from Docker hub:
docker pull genomicslab/longreadsum
On Unix/Linux:
docker run -v C:/Users/.../DataDirectory:/mnt/ -it genomicslab/longreadsum bam -i /mnt/input.bam -o /mnt/output
Note that the -v command is required for Docker to find the input file. Use a directory under C:/Users/ to ensure volume files are mounted correctly. In the above example, the local directory C:/Users/.../DataDirectory containing the input file input.bam is mapped to a directory /mnt/ in the Docker container. Thus, the input file and output directory arguments are relative to the /mnt/ directory, but the output files will also be saved locally in C:/Users/.../DataDirectory under the specified subdirectory output.
First install Anaconda. Then follow the instructions below to install LongReadSum and its dependencies:
git clone https://github.com/WGLab/LongReadSum
cd LongReadSum
conda env create -f environment.yml
export PATH=~/miniconda3/envs/lrst_py39/bin:$PATH
conda activate lrst_py39
make
If you are using FAST5 files with VBZ compression, you will need to download and install the VBZ plugin corresponding to your architecture: https://github.com/nanoporetech/vbz_compression/releases
For example:
wget https://github.com/nanoporetech/vbz_compression/releases/download/v1.0.1/ont-vbz-hdf-plugin-1.0.1-Linux-x86_64.tar.gz
tar -xf ont-vbz-hdf-plugin-1.0.1-Linux-x86_64.tar.gz
Finally, add the plugin to your path:
export HDF5_PLUGIN_PATH=/full/path/to/ont-vbz-hdf-plugin-1.0.1-Linux/usr/local/hdf5/lib/plugin
Activate the conda environment and then run with arguments:
conda activate longreadsum
python longreadsum [arguments]
Specifying input files:
usage: longreadsum [-h] {fa,fq,f5,f5s,seqtxt,bam,rrms} ...
Fast and comprehensive QC for long read sequencing data.
positional arguments:
{fa,fq,f5,seqtxt,bam}
fa FASTA file input
fq FASTQ file input
f5 FAST5 file input
f5s FAST5 file input with signal statistics output
seqtxt sequencing_summary.txt input
bam BAM file input
rrms RRMS BAM file input
optional arguments:
-h, --help show this help message and exit
Example with single inputs:
longreadsum bam -i input.bam -o output_directory -t 12
Example with multiple inputs:
longreadsum bam -I input1.bam, input2.bam -o output_directory
longreadsum bam -P *.bam -o output_directory
RRMS example:
longreadsum rrms --csv rrms_results.csv --input input.bam --output output_directory --threads 12
FAST5 signal mode example:
longreadsum f5s --input input.fast5 --output output_directory
For release history, please visit here.
Please refer to the LongReadSum issue pages for posting your issues. We will also respond your questions quickly. Your comments are criticl to improve our tool and will benefit other users.
Please cite the presentation below if you use our tool
Perdomo, J. E., M. U. Ahsan, Q. Liu, L. Fang, K. Wang. LongReadSum: A fast and flexible quality control tool for long-read sequencing data. Poster presented at: American Society of Human Genetics (ASHG) Annual Meeting; 2022 October 25-29; Los Angeles Convention Center, Los Angeles, CA.