Hantavirus Assembly Pipeline

This repository contains a standalone Python pipeline for assembling segmented Hantavirus genomes from Nanopore sequencing data. The pipeline performs quality control, mapping, primer masking, consensus sequence generation, and report creation. The pipeline should be able to assemble any viral genome if the required files are provided.

Overview

The pipeline is designed specifically for Nanopore data and supports segmented genomes. For each segment (e.g., L, M, S), the tool:

• Runs quality control (QC) using nanoQC and porechop.
• Maps reads to the provided segment-specific reference using minimap2 and sorts the output with samtools.
• Performs primer masking using iVar with an user-provided bed file specifying the sequence and position of the primers.
• Generates a consensus sequence with samtools mpileup piped to ivar consensus.
• Creates a CSV mapping report summarizing read mapping statistics.

Requirements

External Tools

Ensure the following command-line tools are installed and available in your PATH:

• nanoQC – Quality control for raw reads.
• porechop – Adapter trimming for Nanopore reads.
• minimap2 – Read mapping.
• samtools – BAM file manipulation and read statistics.
• ivar – Primer masking and consensus calling.

You can install these using Conda from the conda-forge and bioconda channels:

#If installing on an ARM mac (M processor), create an OSX-64 environment first
CONDA_SUBDIR=osx-64 conda create -n SNVler python=3.11
#Activate the environment
conda activate SNVler
#Install packages
conda install -c conda-forge -c bioconda nanoQC porechop minimap2 samtools ivar pandas

Python Dependencies

The pipeline requires Python 3.6+ (tested with Pytho 3.11.8) and the following libraries

• Biopython
• cyvcf2
• pandas

Installation

1. Clone the repository

git clone https://github.com/viralemergence/SNVler.git
cd hantavirus-assembly

2. Install the required external tools and Python dependencies as described in the Requirements section.

Basic Usage

You can declare each sample individually, or you can use an Excel/CSV file to automatically assemble the genomes from all the samples in a directory

python3 assemble_hantavirus.py \
  --input sample1.fastq sample2.fastq \
  --references Hanta_L.fasta Hanta_M.fasta Hanta_S.fasta \
  --primer_bed /path/to/primer.bed \
  --mask_script /path/to/mask_vcf.py \
  --maskfile /path/to/maskfile.txt \
  --maskvcf /path/to/mask.vcf \
  --output /path/to/output \
  --stringent #optional

Command-Line arguments

• --input: One or more input FASTQ (or FAST5) files.
• --references: One or more reference FASTA files for each genome segment.
• --primer_bed: (Optional) Primer BED file for trimming/mapping.
• --skip_qc: (Optional) Flag to skip the quality control steps.
• --skip_masking: (Optional) Flag to skip the masking step.
• --output: Output directory where results will be stored.
• --stringent: Performs consensus calling using iVar with more stringent parameters. Specifically, a minimum depth of 10X and a nucleotide frequency of 0.75. Use it if you want to avoid ambiguous bases (IUPAC) or validate the presence of indels.

Pipeline Workflow

1. Quality Control (QC):

• Runs nanoQC on each input.
• Trims reads using porechop
• Optional: Skip QC using the --skip-qc flag.

2. Mapping:

• Maps reads against each provided segment reference using minimap2.
• Converts and sorts the mapping output with samtools to produce sorted BAM files.

3. *Primer Masking

• If masking parameters are provided and the --skip_masking flag is not set, the pipeline will run a iVar to trim primer reads before calling the consensus sequence.

4. Consensus Calling:

• Generates consensus sequences from each sorted BAM using samtools mpileup piped to ivar consensus.

5. Mapping Report:

• Creates a CSV report that summarizes the number and percentage of mapped reads for each sample.