Running PHoeNIx

You should have already set up your config file to make sure Nextflow knows how to run the programs within PHoeNIx. If you haven't already, please review the config set up portion of the install page.

Input Parameters

The following are the possible parameters you can pass to PHoeNIx.

Inputs

PHoeNIx currently only runs on Illumina paired-end reads. Multiple samples can be run using a samplesheet.csv file

nextflow run phoenix/main.nf -profile <docker/singularity/custom> -entry PHOENIX --input samplesheet.csv

Samplesheet Input

You will need to create a samplesheet with information about the samples you would like to analyze before running the pipeline. Use the --input parameter to specify its location. It must be a comma-separated file (csv) with at least 3 columns and a header row, as shown in the example below. DO NOT HAVE ANY SPACES IN THIS FILE. Do make sure the paths are full paths and not relative. For best results use the automated samplesheet creation scripts described in the automated section below.

--input '[path to samplesheet file]'

Full Samplesheet

The samplesheet can have as many columns as you desire; however, there is a strict requirement for the first 3 columns to match those defined in the table below.

A final samplesheet file consisting of paired-end data may look something like the one below.

sample,fastq_1,fastq_2
SAMPLE_1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
SAMPLE_2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz
SAMPLE_3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz

Column	Description
sample	Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (_).
fastq_1	Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".
fastq_2	Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz".

An example samplesheet has been provided with the pipeline and can be used for testing.

Automated Samplesheet Creation

A script is available to create a samplesheet from a directory of fastq files. The script will search 1 directory deep and attempt to determine sample id names and pairing/multilane information and will automatically create a samplesheet.

- Please review the samplesheet for accuracy before using it in the pipeline.

phoenix/bin/create_samplesheet.sh <directory of fastq files> > samplesheet.csv

You can change the name of the samplesheet.csv above to anything you want.

Outputs

Output file structure

The output of PHoeNIx is structured like the following:

📦results
┣ 📂SRR17250615
┃ ┣ 📂ANI
┃ ┃ ┣ 📂fastANI
┃ ┃ ┃ ┗ 📜SRR17250615.fastANI.txt
┃ ┃ ┣ 📂mash_dist
┃ ┃ ┃ ┣ 📜SRR17250615.txt
┃ ┃ ┃ ┗ 📜SRR17250615_best_MASH_hits.txt
┃ ┃ ┗ 📜SRR17250615.ani.txt
┃ ┣ 📂Assembly
┃ ┃ ┣ 📜SRR17250615.assembly.gfa.gz
┃ ┃ ┣ 📜SRR17250615.bbmap_filtered.log
┃ ┃ ┣ 📜SRR17250615.contigs.fa.gz
┃ ┃ ┣ 📜SRR17250615.filtered.scaffolds.fa.gz
┃ ┃ ┣ 📜SRR17250615.renamed.scaffolds.fa.gz
┃ ┃ ┣ 📜SRR17250615.scaffolds.fa.gz
┃ ┃ ┗ 📜SRR17250615.spades.log
┃ ┣ 📂BUSCO*
┃ ┃ ┣ 📜SRR17250615-auto-busco.batch_summary.txt
┃ ┃ ┣ 📜short_summary.generic.bacteria_odb10.SRR17250615.filtered.scaffolds.fa.json
┃ ┃ ┣ 📜short_summary.generic.bacteria_odb10.SRR17250615.filtered.scaffolds.fa.txt
┃ ┃ ┣ 📜short_summary.specific.enterobacterales_odb10.SRR17250615.filtered.scaffolds.fa.json
┃ ┃ ┗ 📜short_summary.specific.enterobacterales_odb10.SRR17250615.filtered.scaffolds.fa.txt
┃ ┣ 📂fastp_trimd
┃ ┃ ┣ 📜SRR17250615.fastp.html
┃ ┃ ┣ 📜SRR17250615.fastp.json
┃ ┃ ┣ 📜SRR17250615.singles.fastq.gz
┃ ┃ ┣ 📜SRR17250615_1.trim.fastq.gz
┃ ┃ ┣ 📜SRR17250615_2.trim.fastq.gz
┃ ┃ ┣ 📜SRR17250615_raw_read_counts.txt
┃ ┃ ┣ 📜SRR17250615_singles.fastp.html
┃ ┃ ┣ 📜SRR17250615_singles.fastp.json
┃ ┃ ┗ 📜SRR17250615_trimmed_read_counts.txt
┃ ┣ 📂fastqc
┃ ┃ ┣ 📜SRR17250615_1_fastqc.html
┃ ┃ ┣ 📜SRR17250615_1_fastqc.zip
┃ ┃ ┣ 📜SRR17250615_2_fastqc.html
┃ ┃ ┗ 📜SRR17250615_2_fastqc.zip
┃ ┣ 📂gamma_ar
┃ ┃ ┣ 📜SRR17250615_ResGANNCBI_20210507_srst2.gamma
┃ ┃ ┗ 📜SRR17250615_ResGANNCBI_20210507_srst2.psl
┃ ┣ 📂gamma_hv
┃ ┃ ┣ 📜SRR17250615_HyperVirulence_20220414.gamma
┃ ┃ ┗ 📜SRR17250615_HyperVirulence_20220414.psl
┃ ┣ 📂gamma_pf
┃ ┃ ┣ 📜SRR17250615_PF-Replicons_20220414.gamma
┃ ┃ ┗ 📜SRR17250615_PF-Replicons_20220414.psl
┃ ┣ 📂kraken2_asmbld*
┃ ┃ ┣ 📂krona
┃ ┃ ┃ ┣ 📜SRR17250615_asmbld.html
┃ ┃ ┃ ┗ 📜SRR17250615_asmbld.krona
┃ ┃ ┣ 📜SRR17250615.asmbld_summary.txt
┃ ┃ ┣ 📜SRR17250615.classified.fastq.gz
┃ ┃ ┣ 📜SRR17250615.kraken2_asmbld.classifiedreads.txt
┃ ┃ ┣ 📜SRR17250615.kraken2_asmbld.report.txt
┃ ┃ ┣ 📜SRR17250615.mpa
┃ ┃ ┗ 📜SRR17250615.unclassified.fastq.gz
┃ ┣ 📂kraken2_asmbld_weighted
┃ ┃ ┣ 📂krona
┃ ┃ ┃ ┣ 📜SRR17250615_wtasmbld.html
┃ ┃ ┃ ┗ 📜SRR17250615_wtasmbld.krona
┃ ┃ ┣ 📜SRR17250615.kraken2_wtasmbld.report.txt
┃ ┃ ┗ 📜SRR17250615.wtasmbld_summary.txt
┃ ┣ 📂kraken2_trimd
┃ ┃ ┣ 📂krona
┃ ┃ ┃ ┣ 📜SRR17250615_trimd.html
┃ ┃ ┃ ┗ 📜SRR17250615_trimd.krona
┃ ┃ ┣ 📜SRR17250615.classified_1.fastq.gz
┃ ┃ ┣ 📜SRR17250615.classified_2.fastq.gz
┃ ┃ ┣ 📜SRR17250615.kraken2_trimd.classifiedreads.txt
┃ ┃ ┣ 📜SRR17250615.kraken2_trimd.report.txt
┃ ┃ ┣ 📜SRR17250615.mpa
┃ ┃ ┣ 📜SRR17250615.trimd_summary.txt
┃ ┃ ┣ 📜SRR17250615.unclassified_1.fastq.gz
┃ ┃ ┗ 📜SRR17250615.unclassified_2.fastq.gz
┃ ┣ 📂mlst
┃ ┃ ┗ 📜SRR17250615.tsv
┃ ┣ 📂quast
┃ ┃ ┗ 📜SRR17250615_report.tsv
┃ ┣ 📂removedAdapters
┃ ┃ ┗ 📜SRR17250615.bbduk.log
┃ ┣ 📂srst2*
┃ ┃ ┗ 📜SRR17250615__fullgenes__ResGANNCBI_20210507_srst2__results.txt
┃ ┣ 📜SRR17250615.synopsis
┃ ┣ 📜SRR17250615.tax
┃ ┣ 📜SRR17250615_Assembly_ratio_20210819.txt
┃ ┗ 📜SRR17250615_summaryline.tsv
┣ 📂multiqc
┃ ┣ 📂multiqc_data
┃ ┃ ┣ 📜mqc_fastqc_per_base_n_content_plot_1.txt
┃ ┃ ┣ 📜mqc_fastqc_per_base_sequence_quality_plot_1.txt
┃ ┃ ┣ 📜mqc_fastqc_per_sequence_gc_content_plot_Counts.txt
┃ ┃ ┣ 📜mqc_fastqc_per_sequence_gc_content_plot_Percentages.txt
┃ ┃ ┣ 📜mqc_fastqc_per_sequence_quality_scores_plot_1.txt
┃ ┃ ┣ 📜mqc_fastqc_sequence_counts_plot_1.txt
┃ ┃ ┣ 📜mqc_fastqc_sequence_duplication_levels_plot_1.txt
┃ ┃ ┣ 📜mqc_fastqc_sequence_length_distribution_plot_1.txt
┃ ┃ ┣ 📜multiqc.log
┃ ┃ ┣ 📜multiqc_data.json
┃ ┃ ┣ 📜multiqc_fastqc.txt
┃ ┃ ┣ 📜multiqc_general_stats.txt
┃ ┃ ┗ 📜multiqc_sources.txt
┃ ┣ 📂multiqc_plots
┃ ┃ ┣ 📂pdf
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_base_n_content_plot_1.pdf
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_base_sequence_quality_plot_1.pdf
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_gc_content_plot_Counts.pdf
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_gc_content_plot_Percentages.pdf
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_quality_scores_plot_1.pdf
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_counts_plot_1.pdf
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_counts_plot_1_pc.pdf
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_duplication_levels_plot_1.pdf
┃ ┃ ┃ ┗ 📜mqc_fastqc_sequence_length_distribution_plot_1.pdf
┃ ┃ ┣ 📂png
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_base_n_content_plot_1.png
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_base_sequence_quality_plot_1.png
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_gc_content_plot_Counts.png
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_gc_content_plot_Percentages.png
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_quality_scores_plot_1.png
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_counts_plot_1.png
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_counts_plot_1_pc.png
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_duplication_levels_plot_1.png
┃ ┃ ┃ ┗ 📜mqc_fastqc_sequence_length_distribution_plot_1.png
┃ ┃ ┗ 📂svg
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_base_n_content_plot_1.svg
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_base_sequence_quality_plot_1.svg
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_gc_content_plot_Counts.svg
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_gc_content_plot_Percentages.svg
┃ ┃ ┃ ┣ 📜mqc_fastqc_per_sequence_quality_scores_plot_1.svg
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_counts_plot_1.svg
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_counts_plot_1_pc.svg
┃ ┃ ┃ ┣ 📜mqc_fastqc_sequence_duplication_levels_plot_1.svg
┃ ┃ ┃ ┗ 📜mqc_fastqc_sequence_length_distribution_plot_1.svg
┃ ┗ 📜multiqc_report.html
┣ 📂pipeline_info
┃ ┣ 📜execution_report_2022-06-16_09-34-32.html
┃ ┣ 📜execution_timeline_2022-06-16_09-34-32.html
┃ ┣ 📜execution_trace_2022-06-16_09-34-32.txt
┃ ┣ 📜pipeline_dag_2022-06-16_09-34-32.svg
┃ ┣ 📜samplesheet.valid.csv
┃ ┗ 📜software_versions.yml
┗ 📜Phoenix_Output_Report.tsv

This is the file tree for running one sample. *Designates files that will only be generated when you use the -entry CDC_PHOENIX for the pipeline.

Output File Overview

The following are an explanations of the files that are output:

• ANI - Output of FastANI and Mash dist
• Assembly - Assembly output from SPADes and filtering/header renaming steps.
• BUSCO - Output from BUSCO run on scaffolds.
• fastp_trimd - Output of raw reads filtering and stats for trimmed, raw and unpaired reads.
• fastqc - Raw read QC
• GAMMA
  • gamma_ar - Output of GAMMA hits from curated AR database
  • gamma_hv - Output of GAMMA hits from hypervirulence gene database
  • gamma_pf - Output of GAMMA-S hits from plasmid finder database
• Kraken2
  • kraken2_trimd - Output of Kraken2 run on trimmed reads and Krona plots
  • kraken2_asmbld - Output of Kraken2 run on the assembly and Krona plots
  • kraken2_asmbld_weighted - Output of Kraken2 run on the assembly weighted by sequence length and Krona plots
• mlst - Output of MLST scans for assembly files against traditional PubMLST typing schemes
• quast - Assembly QC metrics
• removedAdapters - Output of BBDUK step to remove adapters
• srst2 - Output of calling AR genes using the curated database on trimmed reads.
• Sample Specific Files - Files that summarize the results for a sample
• Run Specific Files - A file that summarizes multiple samples A good first place to start
• MultiQC - Aggregate report describing results and FastQC from the whole pipeline
• Pipeline information - Report metrics generated during the workflow execution

ANI

Output files

• ANI/
  • *.ani.txt: Output of FastANI. Which shows the ANI estimate between the assembly and the top 20 closest genomes (determined via the mash distance). The remaining columns are the ANI estimate, the number of genomes that were aligned as orthologous matches, and the total sequence fragments from the assembly. For further details see the FastANI documentation.
  • ANI/fastANI
    • *.fastANI.txt: This is a reformatted version of *.ani.txt that list matches in order of ANI and includes the top match information as the first line of the file to be extracted in downstream processes for reporting.
  • ANI/mash_dist
    • *.txt: output of mash distance.F or further details see the Mash documentation
    • *_best_MASH_hits.txt: A list of the top 20 matches found via mash dist that is past to FastANI to calculate

FastANI FastANI is developed for fast alignment-free computation of whole-genome Average Nucleotide Identity (ANI). ANI is defined as mean nucleotide identity of orthologous gene pairs shared between two microbial genomes. FastANI avoids expensive sequence alignments and uses Mashmap as its MinHash based sequence mapping engine to compute the orthologous mappings and alignment identity estimates.

Assembly

Output files

• Assembly/
  • *.assembly.gfa.gz: Contains SPAdes assembly graph and scaffolds paths in GFA 1.0 format
  • *.bbmap_filtered.log: The log file of bbmap which is used to remove scaffolds that have <500bp
  • *.contigs.fa.gz: Contains contigs generated by SPAdes
  • *.filtered.scaffolds.fa.gz: Scaffolds file that has <500bp sequences remove.
  • *.renamed.scaffolds.fa.gz: Same as the *.filtered.scaffolds.fa.gz file, but headers contain the sample name.
  • *.scaffolds.fa.gz: Contains scaffolds generated by SPAdes
  • *.spades.log: SPAdes log

SPAdes – St. Petersburg genome assembler – is an assembly toolkit containing various assembly pipelines.. For further reading and documentation see the SPAdes manual. SPAdes scaffold files are used for downstream analysis.

BUSCO - only run with -entry CDC_PHOENIX

BUSCO output is based on evolutionarily-informed expectations of gene content of near-universal single-copy orthologs, thus the BUSCO metric is complementary to technical metrics like N50.

Output files

• BUSCO/
  • *-auto-busco.batch_summary.txt:
  • short_summary.generic.*.filtered.scaffolds.fa.json: Contains a summary of the results in JSON form.
  • short_summary.generic.*.filtered.scaffolds.fa.txt: Contains a plain text summary of the results in BUSCO notation.
  • short_summary.specific.*.filtered.scaffolds.fa.json: Contains a summary of the results in JSON form.
  • short_summary.specific.*.filtered.scaffolds.fa.txt: Contains a plain text summary of the results in BUSCO notation.

For further reading and documentation see the BUSCO Users Guide.

Fastp

Output files

• fastp_trimd/
  • *.fastp.html: Html output of fastp run on raw reads.
  • *.fastp.json: Same as the html output, just in json format
  • *.singles.fastq.gz: Unpaired reads that passed the QC filters when running fastp on the raw reads.
  • *_1.trim.fastq.gz: Forward reads from paired-end reads that passed the QC filters of fastp.
  • *_2.trim.fastq.gz : Reverse reads from paired-end reads that passed the QC filters of fastp.
  • *_raw_read_counts.txt: Parsed *.fastp.json on raw reads and custom stat calculations.
  • *_singles.fastp.html: Html output of fastp run on unpaired reads.
  • *_singles.fastp.json: Same as the html output, just in json format.
  • *_trimmed_read_counts.txt: Parsed *.fastp.json on trimmed reads and single reads with custom stat calculations.

FastP is a tool designed to provide fast all-in-one preprocessing for FastQ files. This tool is developed in C++ with multithreading supported to afford high performance. For further reading and documentation see the Fastp documentation.

FastQC

Output files

• fastqc/
  • *_fastqc.html: FastQC report containing quality metrics.
  • *_fastqc.zip: Zip archive containing the FastQC report, tab-delimited data file and plot images.

FastQC gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the FastQC help pages.

GAMMA

Output files

• gamma_ar/
  • *_ResGANNCBI_*.gamma: Output of GAMMA that are the best matches from the curated AR gene database.
  • *_ResGANNCBI_*.psl:
• gamma_hv/
  • *_HyperVirulence_*.gamma: Output of GAMMA that are the best matches from the hypervirulence database.
  • *_HyperVirulence_*.psl:
• gamma_pf/
  • *_PF-Replicons_*.gamma: Output of GAMMA-S that are the best matches from the plasmid finder database without translating them.
  • *_PF-Replicons_*.psl:

GAMMA (Gene Allele Mutation Microbial Assessment) is a command line tool that finds gene matches in microbial genomic data using protein coding (rather than nucleotide) identity, and then translates and annotates the match by providing the type (i.e., mutant, truncation, etc.) and a translated description (i.e., Y190S mutant, truncation at residue 110, etc.). Because microbial gene families often have multiple alleles and existing databases are rarely exhaustive, GAMMA is helpful in both identifying and explaining how unique alleles differ from their closest known matches. GAMMA-S (Gene Allele Mutation Microbial Assessment-Sequence) finds best matches from a gene database without translating them--so it will find the best match by nucleotides, rather by the translated protein sequence. For further reading and documentation see the GAMMA's github.

Kraken2

Output files

• kraken2_asmbld/
  • krona/
    • *_asmbld.html: Interactive hierarchical chart of kraken2's taxa calls on the assembly that can be viewed with any modern web browser.
    • *_asmbld.krona: Krona file used to make the *_asmbld.html file.
  • *.asmbld_summary.txt: The kraken2 best hit for the scaffolds.
  • *.classified.fastq.gz: The sequences that were able to be classified by kraken2.
  • *.kraken2_asmbld.classifiedreads.txt: Standard Kraken2 output on assembly scaffolds.
  • *.kraken2_asmbld.report.txt: Kraken2 report for assembly scaffolds.
  • *.mpa: Converted Kraken report style output to a mpa (MetaPhlAn)-style TEXT file. Used downstream to collect final stats.
  • *.unclassified.fastq.gz: The sequences that were unable to be classified by kraken2.
• kraken2_asmbld_weighted/
  • krona/
    • *_wtasmbld.html: Interactive hierarchical chart of kraken2's taxa calls on the weighted assembly that can be viewed with any modern web browser.
    • *_wtasmbld.krona: Krona file used to make the *_wtasmbld.html file.
  • *.kraken2_wtasmbld.report.txt: Kraken2 report for weighted assembly.
  • *.wtasmbld_summary.txt: The kraken2 best hit for the weighted assembly.
• kraken2_trimd/
  • krona/
    • *_trimd.html: Interactive hierarchical chart of kraken2's taxa calls on the trimmed reads that can be viewed with any modern web browser.
    • *_trimd.krona: Krona file used to make the *_trimd.html file.
  • *.classified_1.fastq.gz: The forward reads that were able to be classified by kraken2.
  • *.classified_2.fastq.gz: The reverse reads that were able to be classified by kraken2.
  • *.kraken2_trimd.classifiedreads.txt: Standard Kraken2 output on trimmed reads.
  • *.kraken2_trimd.report.txt: Kraken2 report for trimmed reads.
  • *.mpa: Converted Kraken report style output to a mpa (MetaPhlAn)-style TEXT file. Used downstream to collect final stats.
  • *.trimd_summary.txt: The kraken2 best hit for the trimmed reads.
  • *.unclassified_1.fastq.gz: The forward reads that were unable to be classified by kraken2.
  • *.unclassified_2.fastq.gz: The reverse reads that were unable to be classified by kraken2.

Kraken2 is a taxonomic classification system using exact k-mer matches to achieve high accuracy and fast classification speeds. This classifier matches each k-mer within a query sequence to the lowest common ancestor (LCA) of all genomes containing the given k-mer. For further reading and documentation see the Kraken2's github. Krona allows hierarchical data to be explored with zooming, multi-layered pie charts. The resulting interactive charts are self-contained and can be viewed with any modern web browser. For further reading and documentation see the Krona's github.

MLST

Output files

• mlst/
  • *.tsv: Output of MLST that contains the filename, matching PubMLST scheme name, ST (sequence type), and allele IDs.

MLST scans assembly files against traditional PubMLST typing schemes.

QUAST

Output files

• quast/
  • *_report.tsv: tab-separated version of the summary, suitable for spreadsheets.

QUAST QUAST evaluates genome assemblies. For further reading and documentation see QUAST's Manual.

removedAdapters

Output files

• removedAdapters/
  • *.bbduk.log: log file for the bbduk run.

BBDUK was developed to combine most common data-quality-related trimming, filtering, and masking operations into a single high-performance tool. For further reading and documentation see BBDUK's Manual.

srst2 - only run with -entry CDC_PHOENIX

Output files

• srst2/
  • *._fullgenes__*__results.txt: STs will be printed in tab-delim format to a file called [outputprefix]mlst[db]__results.txt, output is explained further here.

SRST2 Short Read Sequence Typing for Bacterial Pathogens. For further reading and documentation see SRST2's GitHub.

Sample Specific Files

Output files

• *.synopsis: This file contains a summary of stats for the sample and will provide warnings and alerts for metrics that don't meet quality standards. This is an example output:

----------Checking SRR17250615 for successful completion on ----------
Summarized                    : SUCCESS  : Mon Jun 20 00:33:05 EDT 2022
FASTQs                        : SUCCESS  : R1: 172799474bps R2: 172962956bps
RAW_READ_COUNTS               : SUCCESS  : 1445648 individual reads found in sample (722824 paired reads)
RAW_Q30_R1%                   : SUCCESS  : Q30_R1% at 93% (Threshold is 90)
RAW_Q30_R2%                   : SUCCESS  : Q30_R2% at 84% (Threshold is 70)
TRIMMED_BPS                   : SUCCESS  : R1: 136957271bps R2: 124338571bps Unpaired: 16137177bps
TRIMMED_READ_COUNTS           : SUCCESS  : 1292222 individual reads found in sample (603199 paired reads, 85824 singled reads)
TRIMMED_Q30_R1%               : SUCCESS  : Q30_R1% at 98% (Threshold is 90)
TRIMMED_Q30_R2%               : SUCCESS  : Q30_R2% at 96% (Threshold is 70)
KRAKEN2_CLASSIFY_READS        : SUCCESS  : 8.77% Paraliobacillus lotor with 9.15% unclassified reads
KRAKEN2_READS_CONTAM          : WARNING  : No species have been found above the 25% threshold
ASSEMBLY                      : SUCCESS  : 394 scaffolds found
SCAFFOLD_TRIM                 : WARNING  : 272 scaffolds remain which is high. 122 were removed due to shortness
KRAKEN2_CLASSIFY_ASMBLD       : SUCCESS  :  Paraliobacillus(67.28%) lotor(6.99%) with 0% unclassified contigs
KRAKEN2_ASMBLD_CONTAM         : ALERT    : No species have been found above 25% abundance
KRAKEN2_CLASSIFY_WEIGHTED     : SUCCESS  : Paraliobacillus(1783100.00%) lotor(49050.00%) with 0% unclassified contigs
KRAKEN2_WEIGHTED_CONTAM       : WARNING  : No species have been found above the 25% threshold
QUAST                         : SUCCESS  : #-272 length-5222859 n50-63306 %GC-50.75
TAXA-ANI_REFSEQ               : SUCCESS  : Escherichia coli
ASSEMBLY_RATIO(SD)            : SUCCESS  : 1.0192x(.2825-SD) against E.coli
COVERAGE                      : SUCCESS  : 53.11x coverage based on trimmed reads
BUSCO_ENTEROBACTERALES_ODB10  : SUCCESS  : 100% (440/440)
FASTANI_REFSEQ                : SUCCESS  : 99.03%ID-85.16%COV-Escherichia coli(Escherichia_coli_GCF_016780285.1_ASM1678028v1_genomic.fna.gz)
MLST-ECOLI                    : SUCCESS  : ST541
GAMMA_AR                      : SUCCESS  : 10 AR gene(s) found from ResGANNCBI_20210507
SRST2                         : SUCCESS  : 13 gene(s) found from ResGANNCBI_20210507
PLASMID_REPLICONS             : SUCCESS  : 7 replicon(s) found from SRR17250615_PF-Replicons
HYPERVIRULENCE                : SUCCESS  : No hypervirulence genes were found from SRR17250615_HyperVirulence
---------- SRR17250615 completed as WARNING ----------

• *.tax: This file contains the best taxa id. This is an example output:

(ANI_REFSEQ)-99.83%ID-93.46%COV-SRR13183128.fastANI.txt
D:	Bacteria
P:	Proteobacteria
C:	Gammaproteobacteria
O:	Enterobacterales
F:	Enterobacteriaceae
G:	Enterobacter
s:	cloacae

• *_Assembly_ratio_20210819.txt: This file contains information on the assembly ratio and standard dev for the sample. This is an example output:

Tax: Enterobacter cloacae
NCBI_TAXID: 550
Species_St.Dev: 506000
Isolate_St.Devs: .2716
Actual_length: 5117973
Expected_length: 4980531
Ratio: 1.0276

• *_summaryline.tsv: This is a one line summary that contains the columns: ID, Coverage, Genome_Length, Assembly_Ratio_(STDev), number_of_Contigs, Species, MLST_Scheme, MLST, GC_%, Beta_Lactam_Resistance_Genes, Other_AR_Genes, Hypervirulence_Genes. This will be combined together later for a full summary.

Run Specific Files

Output files

• Phoenix_Output_Report.tsv: A file that is a combination of all *_summaryline.tsv files that is a good overview of the entire run.

MultiQC

Output files

• multiqc/
  • multiqc_report.html: a standalone HTML file that can be viewed in your web browser.
  • multiqc_data/: directory containing parsed statistics from the different tools used in the pipeline.
  • multiqc_plots/: directory containing static images from the report in various formats.

MultiQC is a visualization tool that generates a single HTML report summarizing all samples in your project. Most of the pipeline QC results are visualized in the report and further statistics are available in the report data directory.

Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see http://multiqc.info.

Pipeline information

Output files

• pipeline_info/
  • Reports generated by Nextflow: execution_report.html, execution_timeline.html, execution_trace.txt and pipeline_dag.dot/pipeline_dag.svg.
  • Reports generated by the pipeline: pipeline_report.html, pipeline_report.txt and software_versions.yml. The pipeline_report* files will only be present if the --email / --email_on_fail parameter's are used when running the pipeline.
  • Reformatted samplesheet files used as input to the pipeline: samplesheet.valid.csv.

Nextflow provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage.

Running on a High-Performance Computing (HPC) Cluster

To run PHoeNIx on an HPC and submit jobs to a cluster you will need to make config file for your executor. We provide a template in the conf folder to edit. Then run the program by running:

nextflow run CDCgov/phoenix -profile singularity,custom_HPC --input samplesheet.csv