TARGETSEQ

A nanopore pipeline based on the artic-ncov2019 pipeline

Requirements

This pipeline wrapper requires python v3.6 or higher (Pyhton 3.7 is preferred)

Several dependencies are noted in the requirements.yml file and are installed automatically when creating the conda environment. A specific commit of Jvarkit was taken from (http://lindenb.github.io/jvarkit/SAM4WebLogo.html, https://github.com/lindenb/jvarkit.git) and permanently added to the repo

This pipeline also requires a "sample_names.csv" to indicate which barcodes correspond to which samples for the demultiplexing step. A template of this file is included in the repo.

This pipeline assumes that the primer scheme bed files are of a specific format: tab seperated ".bed" file

with the column headings: "genome", "start", "end", "Primer_ID" and "number" start is the start position of the primer, relative to the reference genome, using a zero based index (pos 1 = 0) Primer names need to include "LEFT" or "RIGHT", using "_" as a delimeter to refer to Fwd and Rev primers

If you need to use a primer scheme that is not included here, please create an issue on this github page and I will add it to the repo for you

This pipeline will call the artic-ncov2019 pipeline which needs to be cloned seperately with its own environment

Step 1

Download and install the 64-bit Python 3.7 version of Miniconda/Anaconda

Step 2 clone the pipeline repo

git clone --recursive https://github.com/PhilliVanilli/target.git

change into the repo directory

cd target

Step 3 create the conda environment

conda env create -f requirements_v5.yml

if pip ssl issues, create a HOME/.pip/pip.conf file with following text

[global]
trusted-host = pypi.python.org
pypi.org
files.pythonhosted.org

Step 3.1 activate the conda env

activate the environment

conda activate target

Step 3.2

install sam4weblogo from jvarkit folder, this tool converts a bam/sam file to a multi sequence alignment

cd jvarkit ./gradlew sam4weblogo

go back to base environment and root folder cd .. cd .. conda deactivate

Step 4 clone the artic-ncov2019 pipeline

git clone --recursive https://github.com/artic-network/artic-ncov2019.git

change into the repo directory cd artic-ncov2019 ]

Step 5 create the conda environment for artic-ncov2019

conda env create -n artic-ncov2019 conda activate artic-ncov2019 conda config --set channel_priority false conda install artic-network::rampart=1.2.0 conda install snakemake-minimal=5.8.1 conda install -c bioconda -c conda-forge artic

Step 6 replace the primer scheme directory under artic-ncov2019

rm -r primer_schemes cd ..

copy the primer_schemes_artic-ncov2019 folder from target folder to the artic-ncov2019 folder and rename as primer_schemes cp -r ./target/primer_schemes_artic-ncov2019 ./artic-ncov2019/primer_schemes

Running the pipeline:

activate the environment

conda activate target

process the raw data to sample consensus sequences

python target/target.py --help
usage: target.py [-h] -in PROJECT_PATH -r
{ChikAsian_V1_400,ChikECSA_V1_800,ZikaAsian_V1_400,SARS2_V1_800,SARS2_V1_400,RSVA_V1_3000,RSVB_V1_3000,DENV1_V1_400,DENV2_V1_400}
[-rs REFERENCE_START] [-re REFERENCE_END] [-mi MIN_LEN]
[-ma MAX_LEN] [-d MIN_DEPTH] [--run_step RUN_STEP] [--run_step_only] [-b {0,1}] [-m] [-a] [-c {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}] [-g GPU_THREADS]
[-gb GPU_BUFFERS] [--use_gaps] [--use_bwa] -p GUPPY_PATH
[-rt]

Process raw nanopore reads to fasta consensus sequences

optional arguments: -h, --help show this help message and exit -in PROJECT_PATH, --project_path PROJECT_PATH The path to the directory containing the 'fast5' and 'fastq' folders -r {ChikAsian_V1_400,ChikECSA_V1_800,ZikaAsian_V1_400,SARS2_V1_800,SARS2_V1_400,RSVA_V1_3000,RSVB_V1_3000,DENV1_V1_400,DENV2_V1_400}, --reference {ChikAsian_V1_400,ChikECSA_V1_800,ZikaAsian_V1_400,SARS2_V1_800,SARS2_V1_400,RSVA_V1_3000,RSVB_V1_3000,DENV1_V1_400,DENV2_V1_400} The reference genome and primer scheme to use (default: None) -rs REFERENCE_START, --reference_start REFERENCE_START The start coordinate of the reference sequence for read mapping (default: 1) -re REFERENCE_END, --reference_end REFERENCE_END The end coordinate of the reference sequence for read mapping. Default = full length (default: False) -mi MIN_LEN, --min_len MIN_LEN The minimum read length allowed: = 300 for 400bp amplicon design = 700 for 800bp amplicon design (default: 700) -ma MAX_LEN, --max_len MAX_LEN The maximum read length allowed: = 500 for 400bp amplicon design = 900 for 800bp amplicon design (default: 900) -d MIN_DEPTH, --min_depth MIN_DEPTH The minimum coverage to call a position in the MSA to consensus (default: 100) --run_step RUN_STEP Run the pipeline starting at this step: --run_step 0 = basecall reads with Guppy --run_step 1 = demultiplex with Guppy --run_step 2 = size filer and rename demultiplexed fastq file --run_step 3 = concatenate demultiplexed files into sample files --run_step 4 = run read mapping and all the variant calling steps on each sample (default: 0) --run_step_only Only run the step specified in 'run_step' (default: False) -b {0,1}, --basecall_mode {0,1} 0 = basecall in r10.4.1 kit14 mode 1 = basecall in r9.4.1 mode (default: 1) -m, --msa Generate consensus from MSA (default: False) -a, --art Generate consensus with Artic pipeline (default: False) -c {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15}, --cpu_threads {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15} The number of cpu threads to use for bwa, nanopolish etc... (default: 16) -g GPU_THREADS, --gpu_threads GPU_THREADS The number of gpu threads to use ... (default: 8) -gb GPU_BUFFERS, --gpu_buffers GPU_BUFFERS The number of gpu buffers to use for demultiplexing (default: 15) --use_gaps use gap characters when making the consensus sequences (default: ) --use_bwa use bwa instead of minimap2 to map reads to reference (default: ) -p GUPPY_PATH, --guppy_path GUPPY_PATH The path to the guppy executables eg: '.../ont-guppy/bin/' -rt, --real_time start basecalling fast5 files in batches during sequencing (default: False)