ONT_preprocessing

ONT_preprocessing is a preprocessing pipeline for ONT data.

Getting started

Prerequisites

• Miniconda3. Tested with conda 4.8.3. which conda should return the path to the executable. If you don't have Miniconda3 installed, you could download and install it with:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod 755 Miniconda3-latest-Linux-x86_64.sh
./Miniconda3-latest-Linux-x86_64.sh

Then, after completing ONT_preprocessing installation, set the MINICONDA_DIR variable in config_ONT_preprocessing.R to the full path to miniconda3 directory.

• Guppy, the software for basecalling and demultiplexing provided by ONT. Tested with Guppy v6.4. If you don't have Guppy installed, choose an appropriate version and install it. For example, you could download and unpack the archive with:

wget /path/to/ont-guppy-cpu_version_of_interest.tar.gz
tar -xf ont-guppy-cpu_version_of_interest.tar.gz

A directory ont-guppy-cpu should have been created in your current directory. Then, after completing ONT_preprocessing installation, set the BASECALLER_DIR variable in config_ONT_preprocessing.R to the full path to ont-guppy-cpu/bin directory.

Installation

git clone https://github.com/MaestSi/ONT_preprocessing.git
cd ONT_preprocessing
chmod 755 *
./install.sh

Otherwise, you can download a docker image with:

docker pull maestsi/ont_preprocessing:latest

A conda environment named ONT_preprocessing_env is created, where seqtk, NanoFilt, pycoQC, Porechop and R with package Biostrings are installed. Then, you can open the config_ONT_pipeline.R file with a text editor and set the variables PIPELINE_DIR and MINICONDA_DIR to the value suggested by the installation step.

Usage

Launch_ONT_preprocessing.sh

Usage: Launch_ONT_preprocessing.sh <fast5_dir>

Note: modify config_ONT_preprocessing.R before running; the script runs the preprocessing pipeline from raw fast5 files to filtered reads.

Input

• <fast5_dir>: directory containing raw fast5 files

Outputs (saved in <fast5_dir>_analysis/analysis):

• <"sample_name".fastq>: filtered reads in fastq format
• <"sample_name".fasta>: filtered reads in fasta format
• logfile.txt: logfile tracking the options used and the number of filtered reads

Outputs (saved in <fast5_dir>_analysis/qc):

• Read length distributions and pycoQC report

Outputs (saved in <fast5_dir>_analysis/basecalling):

• Temporary files for basecalling

Outputs (saved in <fast5_dir>_analysis/preprocessing):

• Temporary files for demultiplexing, filtering based on read length and quality and adapters trimming

Citation

If this tool is useful for your work, please consider citing our manuscripts.

Maestri, S.; Maturo, M.G.; Cosentino, E.; Marcolungo, L.; Iadarola, B.; Fortunati, E.; Rossato, M.; Delledonne, M. A Long-Read Sequencing Approach for Direct Haplotype Phasing in Clinical Settings. Int. J. Mol. Sci. 2020, 21, 9177.

Marcolungo L, Passera A, Maestri S, Segala E, Alfano M, Gaffuri F, Marturano G, Casati P, Bianco PA, Delledonne M. Real-Time On-Site Diagnosis of Quarantine Pathogens in Plant Tissues by Nanopore-Based Sequencing. Pathogens. 2022 Feb 2;11(2):199.

Tarquini, G.; Martini, M.; Maestri, S.; Firrao, G.; Ermacora, P. The Virome of ‘Lamon Bean’: Application of MinION Sequencing to Investigate the Virus Population Associated with Symptomatic Beans in the Lamon Area, Italy. Plants 2022, 11, 779.