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Introduction

Schematic of the main steps in the workflow

ONT-Seq-analysis is a pipeline that inputs ONT sequencing data of Mpox isolates and performs a reference-based assembly followed by a variant table analysis relative to the reference used. It takes a samplesheet and FASTQ files as input, performs quality control (QC), trimming, alignment, nextclade run to identify viral genetic variants, and produces an extensive QC report.

Main steps of the workflow:

1. Read QC (FastQC).
2. Sequencing Toolkit (SEQTK) Trim to remove primers.
3. Trimming of raw reads to specific length using (Trimmomatic).
4. Maps raw reads to reference and generate a refined consensus using (Minimap2) and (IVAR Consensus).
5. (Samtools) to manage alignment files and obtain depth of coverage.
6. Polish consensus using (MEDAKA).
7. Generate a variant table using (IVAR Variants).
8. (Nextclade) for clade assignment, mutation calling, phylogenetic placement, and quality checks for Mpox (Monkeypox).
9. Present QC for raw reads (MultiQC).

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data containing single-end ONT fastq files:

samplesheet.csv:

csv sample,fastq_1,fastq_2 SAMPLE_NAME_1,RANDOM_NAME_S1_L002_R1_001.fastq.gz, A script is available to concatenate barcoded FASTQ files in a directory and generate a samplesheet in the required input format. You can find this script in /assets/ont_fastq_concat_and_samplesheet_create.sh. Ensure you’re in the working directory where you’d like the files merged and saved, as the script will automatically create a directory to store the resulting files, placing the samplesheet file in the same directory. Make sure to enter the path to the directory with the FASTQ files, ending with a "/" symbol.

If your FASTQ files are already concatenated by barcode, you can generate only the samplesheet by running /assets/create_samplesheet_only.sh. Enter the path to the directory with concatenated FASTQ files, ending with a "/", and ensure you are in the working directory where you want to save the samplesheet.

[!WARNING] Avoid using special characters (parentheses, commas, asterisks, hashes, etc.) in FASTQ file names. Only use letters, numbers, underscores (_), and hyphens (-) for better compatibility with the workflow and to avoid unexpected crashes of the runs.

[!NOTE] If you are starting with raw Oxford Nanopore fast5 data, you can use the optional bash script /assets/repository/dorado_pipeline.sh to perform basecalling and demultiplexing. This script will convert fast5 files to pod5, basecall them with Dorado (utilizing GPU acceleration), demultiplex barcoded reads, and generate pseudo-paired-end FASTQ files. The output FASTQ files from this script can then be used as input for the samplesheet.csv required by this Nextflow pipeline. If you use this method, you will need to edit the bash script prior to running it with input and output directories as appropriate. Once this is completed, you can generate a samplesheet as described above.

Repository needs to be cloned using git clone git clone https://github.com/CDCgov/ONT-Seq-analysis Now, you can run the pipeline using:

bash nextflow run mpox_ont_seq_analysis \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --outdir <OUTDIR> \ -resume <#if applicable> \ --fasta <fasta_path> \ --bed_file <bed_path> \ --fai_file <fai_path> \ --gff_file <gff_path> \ --mmi_file <mmi_path> \ --nextclade_dataset_name 'nextstrain/mpox/all-clades' *Reference files for Mpox [NC063383] have been provided in `/assets/NC063383mpox/`_

The module Ivar_Variants is turned off by default. To include it on the run, simply add --run_ivar_variants true to the nextflow run command (shown above).

Nextclade output files can be modified to remove unnecesary columns. To do so, run the filtering script (assets/nextclade_tsv_column_filter.sh) inside the Nextclade output directory. The resulting TSV file will contain the following colums: 'index', 'seqName', 'clade', 'lineage', 'outbreak', 'qc.overallScore', 'qc.overallStatus', 'totalSubstitutions', 'totalDeletions', 'totalInsertions', 'totalFrameShifts', 'totalMissing', 'totalNonACGTNs', 'failedCdses', 'warnings', 'errors'. Feel free to modify as needed.

[!WARNING] Note that, for historical reasons, the developers use semicolon (;) as the column separator in CSV files because they have comma (,) as list separators within table cells. In early versions of Nextclade, their CSV writer code was imperfect, making this an easy solution. They recommend using TSV format instead of CSV format. However, if using CSV format, it is important to configure spreadsheet software or parsers to use semicolons (;) as column delimiters.

We have also included a python script to extract mutations from each nextclade output file and for each specimen, returning a table highlighting key mutations that could indicate a designation of Clade I or Clade II, including the different lineages. Run the python script (assets/pythonX_nextclade_parser.py) inside the Nextclade output directory. It will generate a directory named parser that will contain a tsv file for each specimen with the following format:

| seqname | Mutationsfoundonsequence | FoundinCladeIorCladeII | Clade_designation | |----------|-----------------------------|------------------------------|--------------------| | sample1 | G01234A | yes | Clade Ia | | sample1 | G56789A | yes | Clade IIb |

For more information visit the Nextclade CLI homepage.

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters: see docs.

Credits

ONT-Seq-analysis was originally written by Luis Antonio Haddock, PhD MSc (CDC).

We thank the following people for their extensive assistance in the development of this pipeline:

1. Crystal Gigante, PhD (CDC)
2. Daisy McGrath (CDC)
3. Christopher Gulvik, PhD (CDC)
4. Jiusheng Deng, PhD (CDC)

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.

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Related documents

• Open Practices
• Rules of Behavior
• Thanks and Acknowledgements
• Disclaimer
• Contribution Notice
• Code of Conduct

Overview

This NextFlow pipeline was designed to generate and analyze consensus sequences of two targetted amplicon regions. Each region correlates to primers designed to differentiate mpox clade designations. This analysis gives an alternate to whole genome sequencing while maintaining a high level of confidence to efficient clade assignment.

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