Pathogenseq

Pathogenseq is a pathogen whole genome sequence (WGS) data analysis pipeline, which inclues sequence quality checking, quality control, taxonomy assignment, assembly, assembly quality assessment, assembled contig annotation, mlst, antimicrobial resistance, virulome, plasmid, and taxonomy prediciton. Built with Nextflow,it enables portable and scalable execution across a range of computing environments. The use of Docker and Singularity containers ensures easy installation and highly reproducible results.

Pipeline summary

By default, the pipeline supports both short and long reads:

• Sequence quality check and quality control
  • Short reads
  • Short Illumina reads quality checks (FastQC)
  • Short read quality control (BBDuk | fastp)
  • Short read statistics (seqkit stats)
  • Taxonomic assignment and contamination check (Kraken2)
  • Long reads
  • Nanopore long read quality checks (NanoPlot)
  • Nanopore long read adapter trimming (Porechop)
  • Nanopore long read quality and length filter (chopper)
  • Nanopore long read statistics (seqkit stats)
• Assembly
  • Short read assembly with user choice of the assemblers (Spades | Skesa | Unicycler | megahit | shovill
  • Long read assembly is following the steps below:
  • Nanopore long read de novo assembly (Flye)
  • Circular Flye contigs are rotated to start in the center of the contig (in-house perl script)
  • Long read polishing and consensus generating (Medaka)
  • Short-read polishing while short reads are available:
    • Polypolish
    • POLCA
• Assembly quality check
  • Rapid assessment of genome assembly completeness and contamination using machine learning approach (CheckM2)
  • Rapid taxonomic identification of microbial pathogens from assemblies and also the assement of the sample relatedness (gambit)
  • Assembly depth prediciton and reproting using (Minimap2, samtools and in-house sripts)
• Genome annotation
  • Gene prediction and annotation (Bakta)
  • Identify acquired antimicrobial resistance genes in the assembled contigs (AMRFinderPlus)
  • Scan contig files against traditional PubMLST typing schemes (mlst)
  • Typing and reconstruction of plasmid sequences from assembled contigs (MOB-suite)
  • Virulome detection (abricate with VFDB)
• Tools for special organism
  • Mycobacterium tuberculosis lineage and drug resistance prediciton based on quality controlled illumina and nanopore reads using (TB-Profiler)
  • Streptococcus pneumoniae capsular typeing based on quality controlled illumina reads using (PneumoCaT)
  • Streptococcus pyogenes emm-typing based on assembled contigs using (emmtyper)
  • Streptococcus agalactiae, Group B Streptococcus, serotyping based on assembled contigs using (GBS-SBG)
• Summarize and generate the analysis report, software version control reports

Pipeline reference databases

• Bakta database
• Kraken2 database
• AMRFinderPlus database
• CheckM2 database
• gambit database
• Hostie database

Quick Start

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.

Check workflow options

You can clone or download the pathogenseq from github to local computer or you can directly run the pipeline from github. To check the pipeline command line options:

```{r df-drop-ok, class.source="bg-success"}

running directly from github without downloading or cloning

nextflow run xiaoli-dong/pathogenseq -r revision_number(e.g:8657a20) --help

```

Prepare required samplesheet input

The pathogenseq pipeline requires user to provide a csv format samplesheet, which contains the sequenence information for each sample, as input. See below for what the samplesheet looks like:

samplesheet.csv

sample,fastq_1,fastq_2,long_fastq,basecaller_mode sample1,shortreads_1.fastq.gz,shortreads_2.fastq.gz,longreads.fastq.gz,r1041_e82_400bps_hac_v4.2.0 sample2,shortreads.fastq,NA,longreads.fastq.gz,r1041_e82_400bps_sup_v4.2.0 sample3,NA,NA,longreads.fastq.gz,NA sample4,shortreads_1.fastq.gz,shortreads_2.fastq.gz,NA The csv format samplesheet has five required columns: * The first row of the csv file is the header describing the columns * Each row represents a unique sample to be processed, the first colum is the unique sample id * When the information for a particular column is missing, please fill the column with "NA" * The "fastq1" and "fastq2" columns are reserved for supplying the short sequence files * "basecallermode" is for user to provide the Nanopore basecalling model, for example: r1041e82400bpshacv4.2.0. The availble models for medakaconsensus from medaka (v1.8.0) are listed at bewlow:

medaka model, (default: r1041e82400bpssupv4.2.0). Choices: r103fastg507 r103hacg507 r103minhighg345 r103minhighg360 r103promhighg360 r103supg507 r1041e82260bpsfastg632 r1041e82260bpshacg632 r1041e82260bpshacv4.0.0 r1041e82260bpshacv4.1.0 r1041e82260bpssupg632 r1041e82260bpssupv4.0.0 r1041e82260bpssupv4.1.0 r1041e82400bpsfastg615 r1041e82400bpsfastg632 r1041e82400bpshacg615 r1041e82400bpshacg632 r1041e82400bpshacv4.0.0 r1041e82400bpshacv4.1.0 r1041e82400bpshacv4.2.0 r1041e82400bpssupg615 r1041e82400bpssupv4.0.0 r1041e82400bpssupv4.1.0 r1041e82400bpssupv4.2.0 r104e81fastg5015 r104e81hacg5015 r104e81supg5015 r104e81supg610 r10minhighg303 r10minhighg340 r941e81fastg514 r941e81hacg514 r941e81supg514 r941minfastg303 r941minfastg507 r941minhacg507 r941minhighg303 r941minhighg330 r941minhighg340rle r941minhighg344 r941minhighg351 r941minhighg360 r941minsupg507 r941promfastg303 r941promfastg507 r941promhacg507 r941promhighg303 r941promhighg330 r941promhighg344 r941promhighg360 r941promhighg4011 r941promsupg507 r941supplant_g610

Run the pipeline:

```

Runing the pipeline from remote github

nextflow run xiaoli-dong/pathogenseq \ -r sevencharactergithubrevisionnumber (e.g: 8657a20) --input samplesheet.csv \ -profile \ --outdir results \ --platform

run the pipeline with a local clone

nextflow run yourpathto/pathogenseq/main.nf \ --input samplesheet.csv \ -profile \ --outdir results \ --platform

an example command to launch the pipeline from local computer and run it with singularity configraton profile.

nextflow run yourpathto/pathogenseq/main.nf \ -profile singularity \ --input samplesheet.csv \ --outdir results_singularity \ --platform

an example commnad to launch the pipeline from a local clone and run it with conda configraton profile.

nextflow run yourpathto/pathogenseq/main.nf \ -profile conda \ --input samplesheet.csv \ --outdir results_conda \ --platform ```

• Notes: 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.

```

an example command to launch the pipeline from local computer and run it with singularity and a local configraton profile.

nextflow run yourpathto/pathogenseq/main.nf \ -profile singularity \ -c my.config \ --outdir resultssingularity \ --platform \ --illuminareads_assembler shovill

content of the my.config file

params { configprofilename = 'myconfig' configprofiledescription = 'this is a demo'

// resource configuration
max_cpus   = 8
max_memory = '36.GB'
max_time   = '12.h'
// Input data
input = './samplesheet.csv'

}

```

Pipeline output

Fig 1. Pathogenseq output top level layout

Fig 2. Pathogenseq pipeline_info directory layout

Fig 3. Pathogenseq report directory layout

From the above screenshots, you can see:

• Results of each sample included in the analysis go to its own directry.
• pipeline_info directory contains software version control information, nextflow workflow report, resource usage report, task report.
• report directory contains the summary files of each analysis task

Example pathogenseq data analysis outputs for a particalur sample

Credits

pathogenseq was originally written by Xiaoli Dong. Extensive support was provided from other co-authors on the scientific or technical input required for the pipeline: * Dr. Matthew Croxen * Dr. Tarah Lynch

Notes

with conda, fastqc 0.11.9--0 is not working. I need to change the version to 0.12.1.

mobsuite is not working with 3.0.3 in conda, I need to change the mobsuite version to 3.1.4

Reference