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cronology

cronology is an automated workflow for Cronobacter whole genome sequence assembly, subtyping and traceback based on NCBI Pathogen Detection Project for Cronobacter. It uses fastp for read quality control, shovill and polypolish for de novo assembly and genome polishing, prokka for gene prediction and annotation, and quast.py for assembly quality metrics. User(s) can choose a gold standard reference genome as a model during gene prediction step with prokka. By default, GCF_003516125 (Cronobacter sakazakii) is used.

In parallel, for each isolate, whole genome based (genome distances) traceback analysis is performed using mash and mashtree and the results are saved as a phylogenetic tree in newick format. Accompanying metadata generated can be uploaded to iTOL for tree visualization.

User(s) can also run pangenome analysis using pirate but this will considerably increase the run time of the pipeline if the input has more than ~50 samples.


 


 

Minimum Requirements

  1. Nextflow version 23.04.3.

    • Make the nextflow binary executable (chmod 755 nextflow) and also make sure that it is made available in your $PATH.
    • If your existing JAVA install does not support the newest Nextflow version, you can try Amazon's JAVA (OpenJDK): Corretto.

  2. Either of micromamba (version 1.0.0) or docker or singularity installed and made available in your $PATH.

    • Running the workflow via micromamba software provisioning is preferred as it does not require any sudo or admin privileges or any other configurations with respect to the various container providers.

    • To install micromamba for your system type, please follow these installation steps and make sure that the micromamba binary is made available in your $PATH.

    • Just the curl step is sufficient to download the binary as far as running the workflows are concerned.

    • Once you have finished the installation, it is important that you downgrade micromamba to version 1.0.0.

      micromamba self-update --version 1.0.0

  3. Minimum of 10 CPU cores and about 60 GBs for main workflow steps. More memory may be required if your FASTQ files are big.


 

CFSAN GalaxyTrakr

The cronology pipeline is also available for use on the Galaxy instance supported by CFSAN, FDA. If you wish to run the analysis using Galaxy, please register for an account, after which you can run the workflow by selecting cronology under Metagenomics:CPIPES tool section.

Please note that the pipeline on CFSAN GalaxyTrakr in most cases may be a version older than the one on GitHub due to testing prioritization.


 

Usage and Examples

Clone or download this repository and then call cpipes.

cpipes --pipeline cronology [options]

Alternatively, you can use nextflow to directly pull and run the pipeline.

nextflow pull CFSAN-Biostatistics/cronology
nextflow list
nextflow info CFSAN-Biostatistics/cronology
nextflow run CFSAN-Biostatistics/cronology --pipeline cronology_db --help
nextflow run CFSAN-Biostatistics/cronology --pipeline cronology --help


 

Example: Run the default cronology pipeline in single-end mode.

cd /data/scratch/$USER
mkdir nf-cpipes
cd nf-cpipes
cpipes
      --pipeline cronology \
      --input /path/to/illumina/fastq/dir \
      --output /path/to/output \
      --cronology_root_dbdir /data/Kranti_Konganti/cronology_db/PDG000000043.213 \
      --fq_single_end true


 

Example: Run the cronology pipeline in paired-end mode.

cd /data/scratch/$USER
mkdir nf-cpipes
cd nf-cpipes
cpipes \
      --pipeline cronology \
      --input /path/to/illumina/fastq/dir \
      --output /path/to/output \
      --cronology_root_dbdir /data/Kranti_Konganti/cronology_db/PDG000000043.213 \
      --fq_single_end false


 

Database

Although users can choose to run the cronology_db pipeline, it requires access to HPC Cluster or a similar cloud setting. Since GUNC and CheckM2 tools are used to filter out low quality assemblies, which require its own databases, the runtime is longer than usual. Therefore, the pre-formatted databases will be provided for download.


 

Input

The input to the workflow is a folder containing compressed (.gz) FASTQ files. Please note that the sample grouping happens automatically by the file name of the FASTQ file. If for example, a single sample is sequenced across multiple sequencing lanes, you can choose to group those FASTQ files into one sample by using the --fq_filename_delim and --fq_filename_delim_idx options. By default, --fq_filename_delim is set to _ (underscore) and --fq_filename_delim_idx is set to 1.

For example, if the directory contains FASTQ files as shown below:

  • KB-01_apple_L001_R1.fastq.gz
  • KB-01_apple_L001_R2.fastq.gz
  • KB-01_apple_L002_R1.fastq.gz
  • KB-01_apple_L002_R2.fastq.gz
  • KB-02_mango_L001_R1.fastq.gz
  • KB-02_mango_L001_R2.fastq.gz
  • KB-02_mango_L002_R1.fastq.gz
  • KB-02_mango_L002_R2.fastq.gz

Then, to create 2 sample groups, apple and mango, we split the file name by the delimitor (underscore in the case, which is default) and group by the first 2 words (--fq_filename_delim_idx 2).

This goes without saying that all the FASTQ files should have uniform naming patterns so that --fq_filename_delim and --fq_filename_delim_idx options do not have any adverse effect in collecting and creating a sample metadata sheet.


 

Output

All the outputs for each step are stored inside the folder mentioned with the --output option. A multiqc_report.html file inside the cronology-multiqc folder can be opened in any browser on your local workstation which contains a consolidated brief report. The tree metadata which can be uploaded to iTOL for visualization will be located in the cat_unique folder.


 

Computational resources

The workflow cronology requires at least a minimum of 60 GBs of memory to successfully finish the workflow. By default, cronology uses 10 CPU cores where possible. You can change this behavior and adjust the CPU cores with --max_cpus option.


 

Example:

cpipes \
    --pipeline cronology \
    --input /path/to/cronology_sim_reads \
    --output /path/to/cronology_sim_reads_output \
    --cronology_root_dbdir /path/to/PDG000000043.213
    --max_cpus 5 \
    -profile stdkondagac \
    -resume


 

Runtime profiles

You can use different run time profiles that suit your specific compute environments i.e., you can run the workflow locally on your machine or in a grid computing infrastructure.


 

Example:

cd /data/scratch/$USER
mkdir nf-cpipes
cd nf-cpipes
cpipes \
    --pipeline cronology \
    --input /path/to/fastq_pass_dir \
    --output /path/to/where/output/should/go \
    -profile your_institution

The above command would run the pipeline and store the output at the location per the --output flag and the NEXTFLOW reports are always stored in the current working directory from where cpipes is run. For example, for the above command, a directory called CPIPES-cronology would hold all the NEXTFLOW related logs, reports and trace files.


 

your_institution.config

In the above example, we can see that we have mentioned the run time profile as your_institution. For this to work, add the following lines at the end of computeinfra.config file which should be located inside the conf folder. For example, if your institution uses SGE or UNIVA for grid computing instead of SLURM and has a job queue named normal.q, then add these lines:


 

your_institution {
    process.executor = 'sge'
    process.queue = 'normal.q'
    singularity.enabled = false
    singularity.autoMounts = true
    docker.enabled = false
    params.enable_conda = true
    conda.enabled = true
    conda.useMicromamba = true
    params.enable_module = false
}

In the above example, by default, all the software provisioning choices are disabled except conda. You can also choose to remove the process.queue line altogether and the cronology workflow will request the appropriate memory and number of CPU cores automatically, which ranges from 1 CPU, 1 GB and 1 hour for job completion up to 10 CPU cores, 1 TB and 120 hours for job completion.


 

Cloud computing

You can run the workflow in the cloud (works only with proper set up of AWS resources). Add new run time profiles with required parameters per Nextflow docs:


 

Example:

my_aws_batch {
    executor = 'awsbatch'
    queue = 'my-batch-queue'
    aws.batch.cliPath = '/home/ec2-user/miniconda/bin/aws'
    aws.batch.region = 'us-east-1'
    singularity.enabled = false
    singularity.autoMounts = true
    docker.enabled = true
    params.conda_enabled = false
    params.enable_module = false
}


 

Example data

cronology was tested on multiple internal sequencing runs and also on publicly available WGS run data. Please make sure that you have all the minimum requirements to run the workflow.

  • Download public SRA data for Cronobacter: SRR List. You can download a minimized set of sequencing runs for testing purposes.

  • Download pre-formatted full database for NCBI Pathogens release: PDG000000043.213 (~500 MB).

  • After succesful run of the workflow, your MultiQC report should look something like this.

  • It is always a best practice to use absolute UNIX paths and real destinations of symbolic links during pipeline execution. For example, find out the real path(s) of your absolute UNIX path(s) and use that for the --input and --output options of the pipeline.

    realpath /hpc/scratch/user/input

Now, run the workflow:


 

cpipes \
    --pipeline cronology \
    --input /path/to/sra_reads \
    --output /path/to/sra_reads_output \
    --cronology_root_dbdir /path/to/PDG000000043.213 \
    --fq_single_end false \
    --fq_suffix '_1.fastq.gz' --fq2_suffix '_2.fastq.gz' \
    -profile stdkondagac \
    -resume

Please note that the run time profile stdkondagac will run jobs locally using micromamba for software provisioning. The first time you run the command, a new folder called kondagac_cache will be created and subsequent runs should use this conda cache.


 

cronology CLI Help

[Kranti_Konganti@my-unix-box ]$ cpipes --pipeline cronology --help
N E X T F L O W  ~  version 23.04.3
Launching `./cronology/cpipes` [jovial_colden] DSL2 - revision: 79ea031fad
================================================================================
             (o)                  
  ___  _ __   _  _ __    ___  ___ 
 / __|| '_ \ | || '_ \  / _ \/ __|
| (__ | |_) || || |_) ||  __/\__ \
 \___|| .__/ |_|| .__/  \___||___/
      | |       | |               
      |_|       |_|
--------------------------------------------------------------------------------
A collection of modular pipelines at CFSAN, FDA.
--------------------------------------------------------------------------------
Name                            : CPIPES
Author                          : Kranti.Konganti@fda.hhs.gov
Version                         : 0.7.0
Center                          : CFSAN, FDA.
================================================================================


--------------------------------------------------------------------------------
Show configurable CLI options for each tool within cronology
--------------------------------------------------------------------------------
Ex: cpipes --pipeline cronology --help
Ex: cpipes --pipeline cronology --help fastp
Ex: cpipes --pipeline cronology --help fastp,polypolish
--------------------------------------------------------------------------------
--help dpubmlstpy               : Show dl_pubmlst_profiles_and_schemes.py CLI
                                  options CLI options
--help fastp                    : Show fastp CLI options
--help spades                   : Show spades CLI options
--help shovill                  : Show shovill CLI options
--help polypolish               : Show polypolish CLI options
--help quast                    : Show quast.py CLI options
--help prodigal                 : Show prodigal CLI options
--help prokka                   : Show prokka CLI options
--help pirate                   : Show priate CLI options
--help mlst                     : Show mlst CLI options
--help mash                     : Show mash `screen` CLI options
--help tree                     : Show mashtree CLI options
--help abricate                 : Show abricate CLI options