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neissflow

neissflow is a Nextflow pipeline for Neisseria gonorrhoeae (Ng) isolate analysis. The mission of neissflow is to consolidate commonly used bioinformatics tools for Ng analysis into a parallel and scalable pipeline. Having all your tools in one place allows you to rapidly generate data and respond quickly to public health demands!

neissflow is currently undergoing validation in accordance with Next-Generation Sequencing (NGS) quality metrics and Quality Manual for Microbiological Laboratories (QMML) standards; users are advised to interpret the results with caution until official validation is complete.

Software

Below is a list of the bioinformatics tools currently integrated into neissflow.

1. fastp - a tool for all-in-one FASTQ processing, including quality filtering, adaptor-trimming, and quality-trimming, as well as quality profiling
2. Samtools stats - a tool for collecting statistics from BAM files and outputting them in a text format
3. Mash - a tool for species screening via fast genome and metagenome distance estimation using MinHash
4. Shovill - an assembly tool for illumina paired end reads
5. QUAST - a tool for evaluating assemblies through calculating and reporting quality metrics
6. Snippy - a tool for rapid haploid variant calling and core genome alignment
7. mlst - a tool for scanning contigs against PubMLST typing schemes.
8. NGMASTER - a tool for performing multi-antigen sequence typing for Neisseria gonorrhoeae (NG-MAST) and Neisseria gonorrhoeae sequence typing for antimicrobial resistance (NG-STAR)
9. BLASTn - basic local alignment search tool (BLAST) for comparing nucleotide sequences to those in a database.
10. Samtools depth - a tool for calculating the read depth at a given position from an alignment.
11. snp-dists - a tool for generating a SNP distance matrix from a FASTA core alignment
12. Gubbins - a tool for marking recombination regions and constructing a phylogeny based on mutations outside of those regions
13. RAxML - a tool for performing Maximum Likelihood based inference of large phylogenetic trees
14. Gotree - tool to manipulate phylogenetic trees and generate visualizations
15. MultiQC - tool for summarizing analysis results for multiple tools and samples in a single report
    

Running neissflow from Nextflow Tower

neissflow has been tested on Nextflow Tower and will run as any other pipeline. This version of neissflow will need to be forked and modified, due to references being included within the repository. See installation.md for further instructions

Running neissflow from the terminal

1. Example: run neissflow with test profile
   The test profile will run neissflow with a set of test samples, this can be used to check if neissflow is working correctly.

• Log into your linux terminal

• Ensure you have followed the instructions in installation.md

• Execute neissflow as instructed below. (Using whatever profile is suited to your computing environment in place of all)

$ nextflow run neissflow/main.nf -profile singularity,all,test --outdir <OUTDIR> --name <RUN NAME>

In this example, just the test sample set will be run through neissflow, and it will procede through all of the steps.

Input Requirements

FASTQ Nomenclature

To successfully run the pipeline, the FASTQ files need to be named appropriately, so that the pairs can be correctly identified. The acceptable file patterns can be found in the table below:

| Naming Pattern | Example pairs | | -------- | ----------- | | *{R1,R2}*. |

CA028S1L001R1001.fastq.gz, CA028S1L001R2001.fastq.gz

TestR1.fastq.gz, TestR2.fastq.gz

| | *{1,2}. | CAJNED011.fastq.gz, CAJNED012.fastq.gz | | *{01,02}. | Test01.fastq.gz, Test_02.fastq.gz |

FASTQ Extensions

To successfully run the pipeline all FASTQ files must be gunzipped. Below is a list of acceptable file extensions.

| Extensions | | -------- | | .fastq.gz | | .fq.gz |

### FASTQ format Additionally, all FASTQ file content needs to follow accepted formatting for FASTQ files, this does not need to be checked ahead of time. However, if the pipeline does not run and the above conditions are met, this is the likely culprit. A descriptive error message can be found in the run log files if this is the case.

### FASTA Extensions Below is a list of acceptable file extensions for input FASTA assemblies.

| Extensions | | -------- | | .fasta | | .fa |

If you choose to use contigs that have already been generated for the samples you are running through neissflow ensure that these have already passed through quality control steps as neissflow does not QC pre assembled contigs.

Options

The majority of the neissflow components can be run a la carte depending on your analysis needs. Depending on which portions of the pipeline you would like to run, or skip, there is also the option to run with just FASTQ input, just FASTA contig input, or both. For more information on running neissflow, checkout docs/usage.md.

Typical pipeline command:

nextflow run neissflow/main.nf -profile singularity,all --input samplesheet.csv --outdir <OUTDIR> --name <RUN NAME> --only_fastq

Input/output options

--input                             Path to comma-separated file containing information about the samples in the experiment. [string]
--outdir                           The output directory where the results will be saved. You have to use absolute paths to storage on Cloud infrastructure. [string]
--name                               The The name of the run, this will be in the final report filename. [default: complete] [string]

Input Type (required)
--only_fastq                   Use flag if only FASTQ input is provided (entire pipeline can run with just FASTQ input) [boolean]
--only_fasta                   Use flag if only FASTA contigs are provided (only snippy and the Phylogeny Subworkflow will run with this input) [boolean]
--fastq_w_fasta             Use flag if FASTQ and FASTA input are provided (entire pipeline can run with this input) [boolean]

Skip steps
--skip_fastq_check       Skip FASTQ format check (only skip if your FASTQs have already been QCed) [boolean]
--skip_preprocess         Skip Preprocessing Subworkflow (only do this if your reads have already been preprocessed and QCed) [boolean]
--skip_species_id         Skip SpeciesID Subworkflow (will skip Mash and FA19 coverage steps used to determine if a sample is NG) [boolean]
`--skipassembly             Skip Assembly Subworkflow (if you do this and do not provide assemblies, the AMR_Profiler Subworkflow will not run) [boolean] --skipassemblyqc       Skip Denovo Assembly QC script (do this if you are inputting non-shovill assemblies) [boolean] --skipamr`                       Skip AMRProfiler Subworkflow [boolean]
--skip_phylogeny           Skip Phylogeny Subworkflow [boolean]

Assembly Parameters
--downsample                   Downsample reads to depth specified by depth parameter with shovill for assembly [boolean]
--depth                             Depth for downsampling reads for assembly with shovill [default: 150] [integer]

Phylogeny Parameters
--reference_genome       Path to alternate reference genome [string]
--remove_ref                    Remove reference from the core alignment produced by the Phylogeny Subworkflow (reference will not appear in generated tree) [boolean]
--snp_dist                        SNP distance cutoff for direct connections used in outbreak detection algorithm [default: 20] [integer]
--max_itr                          Maximum number of iterations for Gubbins [default: 25] [integer]

QC Failure Conditions

There are two quality control (QC) checks performed in this pipeline, the first being a sequence check and the second being a species and assembly check.

The conditions in the following table are used to pass/fail isolates after they are processed by fastp, such that only high quality FASTQ files are passed for further analysis:

| Sequence QC Failure Conditions | | -------- | | Total reads before quality trimming & filtering < 352000 | | Total reads after quality trimming and filtering < 88000 | | Total bases after quality trimming & filtering < 22418200 |

The conditions in the following table are used to pass/fail isolates based on their Mash identified species, mapping to the FA19 reference, and De novo assembly quality metrics, such that only high-quality Neisseria gonorrhoeae isolates are passed for further analysis:

| Species and Assembly QC Failure Conditions | | -------- | | Mash top hit is not Neisseria gonorrhoeae AND the percent of FA19 with greater than 10x coverage < 85% | | The percent of FA19 with greater than 10x coverage < 85% | | Bases in contigs > 2500000 | | Mean coverage < 11 | | Bases in large contigs (>10000bp) < 1850000 AND Bases in contigs < 2100000 | | Mean coverage < 15 AND Fraction of contigs that are large contigs < 0.25 | | Fraction of contigs that are large contigs (>10000bp) < 0.1 |

Major Subworkflow Architectures

AMR Profiler

The AMR Profiler Subworkflow performs AMR typing on the samples with reference to sensitive NG reference, FA19, as well as identifying the presence of resistance genes, performing allele calls, and sequence typing with various sequence typing schemes (MLST, NGSTAR, NGMAST).
The following diagram illustrates what the workflow would look like when running the AMR Profiler (along with its necessary preprocessing steps upstream in neissflow).

Phylogeny

The Phylogeny Subworkflow performs core genome alignment, recombination detection, outbreak detection, and phylogenetic analysis on the samples in the set.
The following diagram illustrates what the workflow would look like when running the Phylogeny steps (along with the necessary preprocessing steps upstream in neissflow).

Output

For a detailed summary of the neissflow output, checkout docs/output.md

Support

Post an issue on our issue tracker for any needed clarification, bugs, or proposed enhancements.

Roadmap

Features that are currently being updated or will be added in the future include:

• Giving the option to run stringMLST or mlst
• Producing a better annotated phylogenetic tree visualization
• Adding more positions of interest to the AMR Profiler variant analysis step based on recent findings

A recent abstract can be found here

Authors and Acknowledgment

Authors / Contributors

• Kat Morin
• Ethan Hetrick
• Apurva Shrivastava
• Eric Tran
• Matthew Schmerer
• Sandeep Joseph

Special thanks to

• Jack Cartee
• Brandi Celia-Sanchez
• Sam Chill
• Arvon Clemons
• Kim Gernert
• Katie Hebrank
• Ellen Kersh
• Amanda Smith

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