Introduction

Food contamination with pathogens is a major burden on our society. In the year 2019, foodborne pathogens caused 137 hospitalisations in Germany (BVL 2019). Globally, they affect an estimated 600 million people a year and impact socioeconomic development at different levels. These outbreaks are mainly due to Salmonella spp. followed by Campylobacter spp. and Noroviruses, as studied by the Food safety - World Health Organization (WHO).

During the investigation of a foodborne outbreak, a microbiological analysis of the potentially responsible food vehicle is performed in order to detect the responsible pathogens and identify the contamination source. By default, the European Regulation (EC) follows ISO standards to detect bacterial pathogens in food: pathogens are detected and identified by stepwise cultures on selective media and/or targeting specific genes with real-time PCRs. The current gold standard is Pulsed-field Gel Electrophoresis (PFGE) or Multiple-Locus Variable Number Tandem Repeat Analysis (MLVA) to characterize the detected strains. These techniques have some disadvantages.

Whole Genome Sequencing (WGS) has been proposed as an alternative. With just one sequencing run, we can:

• detect all genes
• run phylogenetic analysis to link cases
• get information on antimicrobial resistance genes, virulence, serotype, resistance to sanitizers, root cause, and other critical factors in one assay, including historical reference to pathogen emergence.

WGS is more than a surveillance tool and was recommended by the European Centre for Disease Prevention and Control (ECDC) and the European Food Safety Authority (EFSA) for surveillance and outbreak investigation. WGS still requires isolation of the targeted pathogen, which is a time-consuming process, the execution is not always straightforward, nor the success is guaranteed. Sequencing methods without prior isolation could solve this issue.

The evolution of sequencing techniques in the last decades has made the development of shotgun metagenomic sequencing possible, i.e. the direct sequencing of all DNA present in a sample. This approach gives an overview of the genomic composition of all cells in the sample, including the food source itself, the microbial community, and any possible pathogens and their complete genetic information without the need for prior isolation. Several studies have demonstrated the potential of shotgun metagenomics to identify and characterize pathogens and their functional characteristics (e.g. virulence genes) in naturally contaminated or purposefully spiked food samples.

The currently available studies used Illumina sequencing, generating short reads. Longer read lengths, generated by third-generation sequencing platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), make it easier and more practical to identify strains with fewer reads. MinION (from Oxford Nanopore) is a portable, real-time device for ONT sequencing. Several proof-of-principle studies have shown the utility of ONT long-read sequencing from metagenomic samples for pathogen identification (Ciuffreda et al. 2021).

Comment: Nanopore sequencing

Nanopore sequencing has several properties that make it well-suited for our purposes

1. Long-read sequencing technology offers simplified and less ambiguous genome assembly
2. Long-read sequencing gives the ability to span repetitive genomic regions
3. Long-read sequencing makes it possible to identify large structural variations

When using Oxford Nanopore Technologies (ONT) sequencing, the change in electrical current is measured over the membrane of a flow cell. When nucleotides pass the pores in the flow cell the current change is translated (basecalled) to nucleotides by a basecaller. A schematic overview is given in the picture above.

When sequencing using a MinIT or MinION Mk1C, the basecalling software is present on the devices. With basecalling the electrical signals are translated to bases (A,T,G,C) with a quality score per base. The sequenced DNA strand will be basecalled and this will form one read. Multiple reads will be stored in a fastq file.

To identify and track foodborne pathogens using long-read metagenomic sequencing, different samples of potentially contaminated food (at different time points or different locations) are prepared, DNA is extracted and sequenced using MinION (ONT). The generated sequencing data then need to be processed using bioinformatics tools.

In this tutorial, we will be presenting a series of Galaxy workflows whose main goals are to:

1. agnostically detect pathogens (What exactly is this pathogen and what virulence factors does it carry?) from data extracted directly (without prior cultivation) from a potentially contaminated sample (e.g. food like chicken, beef, etc.) and sequenced using Nanopore
2. compare different samples to trace the possible source of contamination

To illustrate how to process such data, we will use datasets generated by Biolytix with the following approach:

Food samples, here chicken, are spiked with known pathogens, here:

• Salmonella enterica subsp. enterica in the sample named Barcode 10 Spike 2
• Salmonella enterica subsp. houtenae in the sample named Barcode 11 Spike 2b

DNA in the samples is extracted, analyzed with qPCR, and sequenced via Nanopore. We start the tutorial from raw data generated by Nanopore.

Agenda

In this tutorial, we will deal with:

1. Introduction
2. Prepare Galaxy and data
3. Pre-Processing
   1. Quality Control and preprocessing
   2. Host read filtering
4. Taxonomy Profiling
5. Gene based pathogenic identification
   1. Assembly
   2. Multilocus Sequence Typing
   3. Antimicrobial Resistance Genes
   4. Virulence Factor identification
6. SNP based pathogenic identification
   1. Variant Calling or SNP Calling
   2. Consensus Genome Building
7. Pathogen Tracking among all samples
   1. Heatmap
   2. Phylogenetic Tree building
8. Conclusion

Prepare Galaxy and data

Any analysis should get its own Galaxy history. So let’s start by creating a new one:

Hands-on: Data upload

1. Create a new history for this analysis

   Tip: Creating a new history

   Click the new-history icon at the top of the history panel.

   If the new-history is missing:

   1. Click on the galaxy-gear icon (History options) on the top of the history panel
   2. Select the option Create New from the menu

2. Rename the history

   Tip: Renaming a history

   1. Click on galaxy-pencil (Edit) next to the history name (which by default is “Unnamed history”)
   2. Type the new name
   3. Click on Save

   If you do not have the galaxy-pencil (Edit) next to the history name:

   1. Click on Unnamed history (or the current name of the history) (Click to rename history) at the top of your history panel
   2. Type the new name
   3. Press Enter

Before we can begin any Galaxy analysis, we need to upload the input data: FASTQ files with the sequenced samples.

Hands-on: Import datasets

1. Import the following samples via link from Zenodo or Galaxy shared data libraries:

   https://zenodo.org/record/7593928/files/Barcode10_Spike2.fastq.gz
   https://zenodo.org/record/7593928/files/Barcode11_Spike2b.fastq.gz
   

   Tip: Importing via links

   • Copy the link location

   • Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

   • Select Paste/Fetch Data

   • Paste the link(s) into the text field

   • Press Start

   • Close the window

   Tip: Importing data from a data library

   As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:

   1. Go into Shared data (top panel) then Data libraries
   2. Navigate to the correct folder as indicated by your instructor
   3. Select the desired files
   4. Click on Add to History galaxy-dropdown near the top and select as Datasets from the dropdown menu

   5. In the pop-up window, choose

      • “Select history”: the history you want to import the data to (or create a new one)
   6. Click on Import

2. Add a tag to each dataset (one with #Barcode10 and the other #Barcode11)

   Tip: Adding a tag

   • Click on the dataset
   • Click on galaxy-tags Add Tags

   • Add a tag starting with #

     Tags starting with # will be automatically propagated to the outputs of tools using this dataset.

   • Check that the tag is appearing below the dataset name

In this tutorial, we can offer 2 versions:

• A short version, running prebuilt workflows
• A long version, going step-by-step

Hands-on: Choose Your Own Tutorial

This is a "Choose Your Own Tutorial" section, where you can select between multiple paths. Click one of the buttons below to select how you want to follow the tutorial

Short Version Long Version

Pre-Processing

Before starting any analysis, it is always a good idea to assess the quality of your input data and to discard poor quality base content by trimming and filtering reads.

Quality Control and preprocessing

During sequencing, errors are introduced, such as incorrect nucleotides being called. These are due to the technical limitations of each sequencing platform. Sequencing errors might bias the analysis and can lead to a misinterpretation of the data. Sequence quality control is therefore an essential first step in your analysis.

In this tutorial we use similar tools as described in the tutorial “Quality control”, but more specific to Nanopore data:

• Quality control with
  • FastQC generates a web report that will aid you in assessing the quality of your data
  • NanoPlot plotting tool for long read sequencing data and alignments

  Hands-on: Initial quality assessment

  1. FastQC Tool: toolshed.g2.bx.psu.edu/repos/devteam/fastqc/fastqc/0.73+galaxy0 with the following parameters:

     • param-files “Raw read data from your current history”: both FastQ file

       Tip: Select multiple datasets

       1. Click on param-files Multiple datasets
       2. Select several files by keeping the Ctrl (or COMMAND) key pressed and clicking on the files of interest

  2. NanoPlot Tool: toolshed.g2.bx.psu.edu/repos/iuc/nanoplot/nanoplot/1.28.2+galaxy1 with the following parameters:
     • “Select multifile mode”: batch
       • “Type of the file(s) to work on”: fastq
         • param-files “Data input files”: both FastQ file

     Comment

     This step, as it does not require the results of FastQC to run, can be launched even if FastQC is not ready

• Read trimming and filtering with Porechop and Fastp (Chen et al. 2018)

  Hands-on: Read trimming and filtering

  1. Porechop Tool: toolshed.g2.bx.psu.edu/repos/iuc/porechop/porechop/0.2.4 with the following parameters:
     • param-files “Input FASTA/FASTQ”: both FastQ file
     • “Output format for the reads”: fastq.gz
  2. fastp Tool: toolshed.g2.bx.psu.edu/repos/iuc/fastp/fastp/0.20.1+galaxy0 with the following parameters:
     • “Single-end or paired reads”: Single-end
       • param-files “Input 1”: outputs of Porechop tool
     • In Output Options
       • “Output JSON report”: Yes

     Comment

     This step can be launched even if Porechop is not done. It will be scheduled and wait until Porechop is done to start.

• Quality recheck after read trimming and filtering with FastQC and Nanoplot and report aggregation with MultiQC

  Hands-on: Final quality checks

  1. FastQC Tool: toolshed.g2.bx.psu.edu/repos/devteam/fastqc/fastqc/0.73+galaxy0 with the following parameters:
     • param-files “Raw read data from your current history”: outputs of fastp tool
  2. NanoPlot Tool: toolshed.g2.bx.psu.edu/repos/iuc/nanoplot/nanoplot/1.28.2+galaxy1 with the following parameters:
     • “Select multifile mode”: batch
       • “Type of the file(s) to work on”: fastq
         • param-files “files”: outputs of fastp tool
  3. MultiQC Tool: toolshed.g2.bx.psu.edu/repos/iuc/multiqc/multiqc/1.11+galaxy0 with the following parameters:
     • In “Results”:
       • param-repeat “Insert Results”
         • “Which tool was used generate logs?”: FastQC
           • In “FastQC output”:
             • param-repeat “Insert FastQC output”
               • “Type of FastQC output?”: Raw data
               • param-files “FastQC output”: 4 Raw data outputs of FastQC tool
       • param-repeat “Insert Results”
         • “Which tool was used generate logs?”: fastp
           • param-files “Output of fastp”: JSON report outputs of fastp tool

Question

Inspect the HTML output of MultiQC for Barcode10

1. How many sequences does Barcode10 contain before and after trimming?
2. What is the quality score over the reads before and after trimming? And the mean score?
3. What is the importance of FastQC?

Solution

1. Before trimming the file has 114,986 sequences and After trimming the file has 91,434 sequences

2. The “Per base sequence quality” is globally medium: the quality score stays above 20 over the entire length of reads after trimming, while quality below 20 could be seen before trimming specially at the beginning and the end of the reads.

   

3. After checking what is wrong, e.g. before trimming, we should think about the errors reported by FastQC: they may come from the type of sequencing or what we sequenced (check the “Quality control” training: FastQC for more details). However, despite these challenges, we can already see sequences getting slightly better after the trimming and filtering, so now we can proceed with our analyses.

Comment

For more information about how to interpret the plots generated by FastQC and MultiQC, please see our dedicated “Quality control” Tutorial.

Host read filtering

Generally, we are not interested in the food (host) sequences, rather only those originating from the pathogen itself. It is an important to get rid of all host sequences and to only retain sequences that might include a pathogen, both in order to speed up further steps and to avoid host sequences compromising the analysis.

In this tutorial, we know the samples come from chicken meat spiked with Salmonella so we already know what will we get as the host and the main pathogen.

In this tutorial we use:

1. Assign reads to taxa using Kraken2 (Wood and Salzberg 2014) and Kalamari, a database of completed assemblies for metagenomics-related tasks used widely in contamination and host filtering

   Hands-on: Read taxonomic classification for host filtering

   1. Kraken2 Tool: toolshed.g2.bx.psu.edu/repos/iuc/kraken2/kraken2/2.1.1+galaxy1 with the following parameters:
      • “Single or paired reads”: Single
        • param-files “Input sequences”: outputs of fastp tool
      • “Print scientific names instead of just taxids”: Yes
      • In “Create Report”:
        • “Print a report with aggregrate counts/clade to file”: Yes
        • “Format report output like Kraken 1’s kraken-mpa-report”: Yes
        • “Report counts for ALL taxa, even if counts are zero”: Yes
        • “Report minimizer data”: Yes
      • “Select a Kraken2 database”: kalamari

   Question

   Inspect the report of Kraken2 for Barcode10

   1. What is the species of the host?
   2. How many sequences of this host was found?

   Solution

   1. Gallus gallus (taxid 9031), which is chicken
   2. 836

2. Filter host assigned reads based on Kraken2 assignments

   1. Manipulate Kraken2 classification to extract the sequence ids of all hosts sequences identified with Kraken2
   2. Filter the FASTQ files to get 1 ouput with the host-assigned sequences and 1 output without the host-assigned reads

   Hands-on: Host read filtering

   1. Filter Tabular Tool: toolshed.g2.bx.psu.edu/repos/iuc/filter_tabular/filter_tabular/2.0.0 with the following parameters:
      • param-files “Tabular Dataset to filter”: Classification outputs of Kraken2 tool
      • In “Filter Tabular Input Lines”:
        • param-repeat “Insert Filter Tabular Input Lines”
          • “Filter By”: by regex expression matching

            • “regex pattern”: ^.*Gallus|Homo|Bos.*$

              We specify here the taxonomic names of the hosts so we can filter reads assigned to these hosts. To be generic, we remove here:

              • Human (Homo)
              • Chicken (Gallus)
              • Beef (Bos)

              If the contaminated food comes from and may include other animals, you can change the value here.

            • “action for regex match”: include line if pattern found

   2. Filter sequences by ID Tool: toolshed.g2.bx.psu.edu/repos/peterjc/seq_filter_by_id/seq_filter_by_id/0.2.7 with the following parameters:
      • param-files “Sequence file to be filtered”: outputs of fastp tool
      • “Filter using the ID list from”: tabular file
        • param-files “Tabular file containing sequence identifiers”: outputs of Filter Tabular tool
        • “Column(s) containing sequence identifiers”: Column: 2
      • “Output positive matches, negative matches, or both?”: Both positive matches (ID on list) and negative matches (ID not on list), as two files

Comment

We will need the outputs from this section in the next one. If yours is still running or you get an error you can go on and upload it so you can start the next workflow, the next hands-on is optional.

Hands-on: Optional Data upload

1. Import the quality processed samples fastqsanger files via link from Zenodo or the Shared Data library:

   https://zenodo.org/record/7593928/files/Nanopore_processed_sequenced_reads_Barcode10_Spike2.fastqsanger
   https://zenodo.org/record/7593928/files/Nanopore_processed_sequenced_reads_Barcode11_Spike2b.fastqsanger
   

2. Add tags to the datasets

Taxonomy Profiling

In this section we would like to identify the different organisms found in our samples by assigning taxonomy levels to the reads starting from the kingdom level down to the species level and visualize the result. It’s important to check what might be the species of a possible pathogen to be found, it gets us closer to the investigation as well as discovering possible multiple food infections if any existed.

Details: What is Taxonomy?

Taxonomy is the method used to naming, defining (circumscribing) and classifying groups of biological organisms based on shared characteristics such as morphological characteristics, phylogenetic characteristics, DNA data, etc. It is founded on the concept that the similarities descend from a common evolutionary ancestor.

Defined groups of organisms are known as taxa. Taxa are given a taxonomic rank and are aggregated into super groups of higher rank to create a taxonomic hierarchy. The taxonomic hierarchy includes eight levels: Domain, Kingdom, Phylum, Class, Order, Family, Genus and Species.

The classification system begins with 3 domains that encompass all living and extinct forms of life

• The Bacteria and Archae are mostly microscopic, but quite widespread.
• Domain Eukarya contains more complex organisms

When new species are found, they are assigned into taxa in the taxonomic hierarchy. For example for the cat:

Level	Classification
Domain	Eukaryota
Kingdom	Animalia
Phylum	Chordata
Class	Mammalia
Order	Carnivora
Family	Felidae
Genus	Felis
Species	F. catus

From this classification, one can generate a tree of life, also known as a phylogenetic tree. It is a rooted tree that describes the relationship of all life on earth. At the root sits the “last universal common ancestor” and the three main branches (in taxonomy also called domains) are bacteria, archaea and eukaryotes. Most important for this is the idea that all life on earth is derived from a common ancestor and therefore when comparing two species, you will -sooner or later- find a common ancestor for all of them.

Let’s explore taxonomy in the Tree of Life, using Lifemap

In the previous section we ran Kraken2 along with the Kalamari database, which is also a kind of taxonomy profiling but the database used is designed to include all possible host sequences. In the following part, we run Kraken2 again; but this time with one of its built-in databases, Standard PlusPF, which can give us more insight into pathogen candidate species than Kalamari. You can test this yourself by comparing reports of both Kraken2 runs.

Details: Kraken2 and the k-mer approach for taxonomy classification

In the \(k\)-mer approach for taxonomy classification, we use a database containing DNA sequences of genomes whose taxonomy we already know. On a computer, the genome sequences are broken into short pieces of length \(k\) (called \(k\)-mers), usually 30bp.

Kraken examines the \(k\)-mers within the query sequence, searches for them in the database, looks for where these are placed within the taxonomy tree inside the database, makes the classification with the most probable position, then maps \(k\)-mers to the lowest common ancestor (LCA) of all genomes known to contain the given \(k\)-mer.

Kraken2 uses a compact hash table, a probabilistic data structure that allows for faster queries and lower memory requirements. It applies a spaced seed mask of s spaces to the minimizer and calculates a compact hash code, which is then used as a search query in its compact hash table; the lowest common ancestor (LCA) taxon associated with the compact hash code is then assigned to the k-mer.

You can find more information about the Kraken2 algorithm in the paper Improved metagenomic analysis with Kraken 2.

To assign reads to taxons, we use Kraken2 with Standard PlusPF database.

Question

Inspect the Kraken2 report for Barcode10 tag

1. What is the most commonly found species?
2. What is the second most commonly found species?
3. How many sequences are classified and how many are unclassified?
4. What are the differences between Kraken2 tool’s report with Kalamari database and Kraken2 tool’s report with Standard PlusPF database regarding the previous 3 questions?

Solution

1. Escherichia coli with 10,243 sequences
2. Salmonella enterica with 7,458 sequences
3. 40,143 sequences are classified and 50,455 are unclassified
4. With Kalamari database the most found species is Escherichia coli with 12,577 sequences and the second most found species is Salmonella enterica with 10,632 sequences. The number of classified sequences are 32,020 sequences and the unclassified sequences are 59,414. In conclusion, both databases are able to show the same results of the most common species. However, the number of the classified sequences with Standard PlusPF database is higher than Kalamari database and it would be even higher since all chicken sequences were removed before testing the Standard PlusPF database.

In order to view the taxonomy profiling produced by Kraken2 tool, there are a lot of tools to be used afterwards such as Krona pie chart, however too many species were detected to be shown by this tool. For that reason, we have chosen the Phinch visualization interactive tool as it contains multiple visualization plots, it is interactive alowing you to choose between different parameters, you can visualize each taxonomic level on its own, you can have the metadata of the samples represented along with the taxonomic visualization, download all plots for publications and a lot of other benefits. For later, you can check out Pavian tool as well it can replace Phinch visualization with similar outputs.

Phinch visualization needs a BIOM file format as an input, and for that we need the Kraken-Biom tool to convert the Kraken2 tabular output into a Biom file.

Once the BIOM file has been generated, we launch the interactive visualisation tool called Phinch visualization:

Now let’s now explore the Phinch visulization tool running for Barcode11 tag

Hands-on: Explore data interactively

1. Open Phinch interactive tool

   Tip: Open interactive tool

   1. Go to User > Active InteractiveTools
   2. Wait for the Phinch visualization to be running (Job Info)
   3. Click on Phinch visualization

2. Click on Proceed to Gallery button on the top right of the opened webpage to see all the plots

Question

1. What is the most commonly found species?
2. What is the second most commonly found species?
3. What’s your favorite visualization plot?

Solution

1. Salmonella enterica with 17,309 sequences
2. Pseudomonas lundensis with 13,227 sequences

3. All of them are good visualization of the data but for us to answer these questions, we used the Taxonomy Bar Chart

   

Comment

While these steps are running, you can move on to the next section Gene based pathogenic identification and run the steps there, as well. Both analyses can execute in parallel.

Gene based pathogenic identification

With taxonomy profiling, we identified some bacterial species. But we want to be sure they are pathogenic, by looking for genes known to be linked to pathogenicity or to the pathogenecity character of the organim:

• Virulence Factor (VF): gene products, usually proteins, involved in pathogenicity. By identifiying them we can call a pathogen and its severity level

• Antimicrobial Resistance genes (AMR).

  These type of genes have three fundamental mechanisms of antimicrobial resistance that are enzymatic degradation of antibacterial drugs, alteration of bacterial proteins that are antimicrobial targets, and changes in membrane permeability to antibiotics, which will lead to not altering the target site and spread throughput the pathogenic bacteria decreasing the overall fitness of the host.

To look for these genes and determine the strain of the bacteria we are testing for pathogenicity we use Multilocus Sequence Typing approach and dedicated pubMLST datases database:

1. Genome assembly to get contigs, i.e. longer sequences, using metaflye (Kolmogorov et al. 2020) then assembly polishing using medaka consensus pipeline and visualizing the assembly graph using Bandage Image (Wick et al. 2015)
2. Generate an MLST report with MLST tool that scans genomes against PubMLST schemes
3. Generate reports with AMR genes and VF using ABRicate

As outputs, we will get our FASTA and Tabular files to track genes and visualize our pathogenic identification. For that we will need one more file to create a report and we can upload it directly:

Hands-on: Data upload

1. Import a tabular file via link from Zenodo or shared data libraries

   https://zenodo.org/record/7593928/files/MLST_Report_Header.tabular
   

2. Check that the datatype is Tabular

Assembly

To identify VF or AMR genes, it is better to assemble reads into longer seuqences or contigs, that can be then used to search databases for the presence of any pathogenic gene:

• Assembly of long-read metagenomic data using metaflye or Flye.

  Hands-on: Assembly with Flye

  1. Flye Tool: toolshed.g2.bx.psu.edu/repos/bgruening/flye/flye/2.9+galaxy0 with the following parameters:

     • param-file “Input reads”: Outputs without matched ID of Filter sequences by ID tool for Barcode10

       Comment

       We need to run Flye individually on each sample otherwise Flye runs by default a co-assembly mode, i.e. it combines read of both samples together before running the assembly.

     • “Mode”: Nanopore HQ (--nano-hq)
     • “Perform metagenomic assembly”: Yes
     • “Reduced contig assembly coverage”: Disable reduced coverage for initial disjointing assembly
  2. Flye Tool: toolshed.g2.bx.psu.edu/repos/bgruening/flye/flye/2.9+galaxy0 with the following parameters:
     • param-file “Input reads”: Outputs without matched ID of Filter sequences by ID tool for Barcode11
     • “Mode”: Nanopore HQ (--nano-hq)
     • “Perform metagenomic assembly”: Yes
     • “Reduced contig assembly coverage”: Disable reduced coverage for initial disjointing assembly

• For the visualization of the assembly graph output from Flye we have chosen Bandage Image.

  Hands-on: Visualization of the assembly grap

  1. Bandage Image Tool: toolshed.g2.bx.psu.edu/repos/iuc/bandage/bandage_image/0.8.1+galaxy2 with the following parameters:
     • param-files “Graphical Fragment Assembly”: Assembly graph outputs of Flye tool

• Contig polishing using medaka to correct the long, error-prone Nanopore reads

  Hands-on: Contig polishing

  1. medaka consensus pipeline Tool: toolshed.g2.bx.psu.edu/repos/iuc/medaka_consensus_pipeline/medaka_consensus_pipeline/1.7.2+galaxy0 with the following parameters:
     • param-files “Select basecalls”: Outputs without matched ID of Filter sequences by ID tool
     • param-files “Select assembly”: Output of Flye tool
     • “Select model”: r941_min_hac_g507
     • “Select output file(s)”: select all

  To keep information about the provenance of the contigs, we can rename them to add contig information to them.

  Hands-on: Contig renaming to add sample names

  1. FASTA-to-Tabular Tool: toolshed.g2.bx.psu.edu/repos/devteam/fasta_to_tabular/fasta2tab/1.1.1 with the following parameters:
     • param-files “Convert these sequences”: output of medaka consensus pipeline tool
  2. Replace Tool: toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4 with the following parameters:
     • param-file “File to process”: output of FASTA-to-Tabular tool for Barcode10
     • In “Find and Replace”:
       • param-repeat “Insert Find and Replace”
         • “Find pattern”: ^(.+)$
         • “Replace with”: Barcode10_$1
         • “Find-Pattern is a regular expression”: Yes
         • “Replace all occurences of the pattern”: Yes
         • “Find and Replace text in”: specific column
           • “in column”: Column: 1

  3. Rename the output to Contigs Table - Barcode10

     Tip: Renaming a dataset

     • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
     • In the central panel, change the Name field
     • Click the Save button

  4. Replace Tool: toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_find_and_replace/1.1.4 with the following parameters:
     • param-file “File to process”: output of FASTA-to-Tabular tool for Barcode11
     • In “Find and Replace”:
       • param-repeat “Insert Find and Replace”
         • “Find pattern”: ^(.+)$
         • “Replace with”: Barcode11_$1
         • “Find-Pattern is a regular expression”: Yes
         • “Replace all occurences of the pattern”: Yes
         • “Find and Replace text in”: specific column
           • “in column”: Column: 1

  5. Rename the output to Contigs Table - Barcode11

     Tip: Renaming a dataset

     • Click on the galaxy-pencil pencil icon for the dataset to edit its attributes
     • In the central panel, change the Name field
     • Click the Save button

  6. Tabular-to-FASTA Tool: toolshed.g2.bx.psu.edu/repos/devteam/tabular_to_fasta/tab2fasta/1.1.1 with the following parameters:
     • param-files “Tab-delimited file”: outputs of Replace tool
     • “Title column(s)”: Column: 1
     • “Sequence column”: Column: 2
  7. Rename the outputs to Contigs - Barcode10 and Contigs - Barcode11

Question

Inspect Flye and Medaka consensus pipeline output results for Barcode10

1. How many different contigs did you get after Flye?
2. How many were left after Medaka consensus pipeline, and what does that mean?
3. What is the result of your Bandage Image?

Solution

1. After Flye we have got 137 contigs
2. After Medaka consensus pipeline all 137 contigs were kept, which means that the quality of the Flye run was high, and as a result the polishing did not remove any of the contigs.

3. The graph looks like:

   

Multilocus Sequence Typing

MLST tool is used to scan the pubMLST database against PubMLST typing schemes. It’s one of the analyses that you can perform on your dataset to determine the allele IDs, you can also detect novel alleles. This step is not essential to identify pathogens and track them in the remainder of this tutorial, however we wanted to show some of the analysis that one can use Galaxy in to understand more about the dataset, as well as identifying the strain that might be a pathogen or not.

The output file of the MLST tool is a tab-separated output file which contains:

• the filename
• the closest PubMLST scheme name
• the ST (sequence type)
• the allele IDs

Question

Inspect MLST tool results

1. What is the the closest PubMLST typing scheme name detected by the tool for Barcode11?

Solution

1. senterica_achtman_2 (“Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica” 2012)

Antimicrobial Resistance Genes

Now, to search AMR genes among our samples’ contigs, we run ABRicate and choose the NCBI Bacterial Antimicrobial Resistance Gene Database (AMRFinderPlus) from the advanced options of the tool. The tool checks if there is an AMR found or not, if found then in which contig it is, its location on the contig, what the name of the exact product is, what substance it provides resistance against and a lot of other information regarding the found AMR.

The outputs of ABRicate is a tabular file with different columns:

1. FILE: The filename this hit came from
2. SEQUENCE: The sequence in the filename
3. START: Start coordinate in the sequence
4. END: End coordinate
5. STRAND: AMR gene
6. GENE: AMR gene
7. COVERAGE: What proportion of the gene is in our sequence
8. COVERAGE_MA: A visual represenation
9. GAPS: Was there any gaps in the alignment - possible pseudogene?
10. %COVERAGE: Proportion of gene covered
11. %IDENTITY: Proportion of exact nucleotide matches
12. DATABASE: The database this sequence comes from
13. ACCESSION: The genomic source of the sequence
14. PRODUCT
15. RESISTANCE

Question

Inspect ABRicate output files from Barcode10and Barcode11 tags

1. How many AMR genes found in Barcode10 sample, what are they? Give more details about them.
2. How many AMR genes found in Barcode11 sample, what are they? Give more details about them.

Solution

1. 5 AMR genes were found:
   1. Tet(C), which resists TETRACYCLINE. It was found in contig 158 from the position 1633 till 2808, with 100% coverage, so 100% of gene is covered in this contig.
   2. 2 genes with sulfonamide-resistant dihydropteroate synthase Sul1 products
   3. 2 genes with oxacillin-hydrolyzing class D beta-lactamase OXA-2 products
2. No AMR genes were found by the database in Barcode11_Spike2b sample.

Virulence Factor identification

In this step we return back to the main goal of the tutorial where we want to identify the pathogens: identify if the bacteria found in our samples are pathogenic bacteria or not. One of the ways to do that is to identify if the sequences include genes with a Virulence Facor or not, such that if the samples include gene(s) with a Virulence Factor then it for sure is a pathogen.

Comment: Definitions

Bacterial Pathogen: A bacterial pathogen is usually defined as any bacterium that has the capacity to cause disease. Its ability to cause disease is called pathogenicity.

Virulence: Virulence provides a quantitative measure of the pathogenicity or the likelihood of causing a disease.

Virulence Factors: Virulence factors refer to the properties (i.e., gene products) that enable a microorganism to establish itself on or within a host of a particular species and enhance its potential to cause disease. Virulence factors include bacterial toxins, cell surface proteins that mediate bacterial attachment, cell surface carbohydrates and proteins that protect a bacterium, and hydrolytic enzymes that may contribute to the pathogenicity of the bacterium.

To identifly VFs, we use again ABRicate but this time with the VFDB from the advanced options of the tool.

Question

Inspect VFs of genes Identified by VFDB output file fromBarcode10and Barcode11 tags

1. How many different VFs gene products were found in Barcode10_Spike2 sample?
2. How many different VFs gene products were found in Barcode11_Spike2b sample?

Solution

1. 123
2. 97

SNP based pathogenic identification

Now we would like to identify pathogens with a third approach based on variant and single nucleotide polymorphisms (SNPs) calling: comparison of reads to a targeted reference genome, and call the differences between sample reads and reference genomes to identify variants.

For example, if we want to check whether our samples include Campylobacter pathogenic strains or not, we will map our samples against the reference genome of the Campylobacter species. Variants in specific positions on the genome are queried to judge if these variations would indicate a pathogen or not.

This approach also allows identification of novel alleles and possible new variants of the pathogen.

Using this approach, we also build the consensus genome of each sample, so we can later build a phylogenetic tree of all samples’ full genomes and have an insight into events that occurred during the evolution of same or different species in the samples.

In this training, we are testing Salmonella enterica, with different strains of which our samples were spiked. So we will now upload to our history the reference genome of S. enterica we originally obtained from the National Center for Biotechnology Information (NCBI) database.

Hands-on: Data upload

1. Import a reference genome FASTA file via link from Zenodo or Galaxy shared data libraries

   https://zenodo.org/record/7593928/files/Salmonella_Ref_genome.fna.gz
   

Variant Calling or SNP Calling

To identify variants, we

1. Map reads to the reference genome of the species of the pathogen we want to test our samples against using Minimap2 (Li 2017) as our datasets are from a Nanopore:

   Hands-on: Mapping to reference genome

   1. Map with minimap2 Tool: toolshed.g2.bx.psu.edu/repos/iuc/minimap2/minimap2/2.24+galaxy0 with the following parameters:
      • “Will you select a reference genome from your history or use a built-in index?”: Use a genome from history and build index
        • param-file “Use the following dataset as the reference sequence”: Salmonella_Ref_genome.fna.gz
      • “Single or Paired-end reads”: Single
        • param-files “Select fastq dataset”: Outputs without matched ID of Filter sequences by ID tool for Barcode10 and Barcode11

2. Identify variants and single nucleotide variants using Clair3 (Zheng et al. 2021), which is designed specifically for Nanopore datasets and giving better results than other variant calling tools, as well as being new and up-to-date.

   Comment

   Medaka consensus tool and medaka variant tool can be also used instead of Clair3, they give similar results but they are much slower then Clair3 and offer fewer options.

   Hands-on: Variant or SNP Calling

   1. Clair3 Tool: toolshed.g2.bx.psu.edu/repos/iuc/clair3/clair3/0.1.12+galaxy0 with the following parameters:
      • param-files “BAM/CRAM file input”: Outputs of Map with minimap2 tool
      • “Reference genome source”: History
        • param-file “Reference genome”: Salmonella_Ref_genome.fna.gz
      • “Clair3 model”: Built-in
        • “Built-in model”: r941_prom_hac_g360+g422
      • In “Advanced parameters”:
        • “Call with the following ploidy model”: haploid precise (--haploid_precise)

3. Left-align and normalize indels using bcftools norm (Li et al. 2009)

   This step:

   • checks REF alleles in the output match the reference;
   • splits multiallelic sites into multiple rows;
   • recovers multiallelics from multiple rows

   Hands-on: Left-align and normalize indels

   1. bcftools norm Tool: toolshed.g2.bx.psu.edu/repos/iuc/bcftools_norm/bcftools_norm/1.9+galaxy1 with the following parameters:
      • param-files “VCF/BCF Data”: Outputs of Clair3 tool
      • “Choose the source for the reference genome”: Use a genome from the history
        • param-file “Reference genome”: Salmonella_Ref_genome.fna.gz
      • “output_type”: uncompressed VCF

4. Filter variants to keep only the pass and good quality variants using SnpSift Filter (Cingolani et al. 2012)

   Comment

   LoFreq filter can be also used instead, both tools performs equal and fast results.

   Hands-on: Filter variants

   1. SnpSift Filter Tool: toolshed.g2.bx.psu.edu/repos/iuc/snpsift/snpSift_filter/4.3+t.galaxy1 with the following parameters:
      • param-files “Input variant list in VCF format”: Outputs of bcftools norm tool
      • “Type of filter expression”: Simple expression
        • “Filter criteria”: (QUAL > 2)
      • “Filter mode”: Retain selected variants, remove others

   The output is a VCF file. VCF is the standard file format for storing variation data, with different columns:

   • #CHROM: Chromosome
   • POS: Co-ordinate - The start coordinate of the variant.
   • ID: Identifier
   • REF: Reference allele - The reference allele is whatever is found in the reference genome. It is not necessarily the major allele.
   • ALT: Alternative allele - The alternative allele is the allele found in the sample you are studying.
   • QUAL: Score - Quality score out of 100.
   • FILTER: Pass/fail - If it passed quality filters.
   • INFO: Further information - Allows you to provide further information on the variants. Keys in the INFO field can be defined in header lines above thetable.
   • FORMAT: Information about the following columns - The GT in the FORMAT column tells us to expect genotypes in the following columns.
   • Individual identifier (optional) - The previous column told us to expect to see genotypes here. The genotype is in the form “0|1”, where 0 indicates the reference allele and 1 indicates the alternative allele, i.e it is heterozygous. The vertical pipe “|” indicates that the genotype is phased, and is used to indicate which chromosome the alleles are on. If this is a slash (/) rather than a vertical pipe, it means we don’t know which chromosome they are on.

5. Extract a tabular report with Chromosome, Position, Identifier, Reference allele, Alternative allele and Filter from the VCF files using SnpSift Extract Fields

   Hands-on: Extract a tabular report

   1. SnpSift Extract Fields Tool: toolshed.g2.bx.psu.edu/repos/iuc/snpsift/snpSift_extractFields/4.3+t.galaxy0 with the following parameters:
      • param-files “Variant input file in VCF format”: Outputs of SnpSift Filter
      • “Fields to extract”: CHROM POS ID REF ALT FILTER

Question

Now let’s inspect the outputs for Barcode10 tag:

1. How many variants were found by Clair3?
2. How many variants were found after quality filtering?
3. What is the Strain identified by the NCBI for the sample?

Solution

1. Before filtering: 2,652
2. After filtering 2,489
3. Strain LT2, can be inferred searching the Chroms. NCBI Reference Sequence: NC_003197.2

Consensus Genome Building

For further anaylsis we have included one more step in this section, where we build the full genome of our samples.

This consensus genome can be used later to compare and relate samples together based on their full genome. In cases such as SARS-CoV2, it is important to do so in order to discover new outbreaks. In this example of the training, it is not really important to draw a tree of the full genomes of the samples as Salmonella does not have such a speedy outbreak as SARS-CoV2 does. However, we decided to include it in the workflow for any further analysis of the users, if needed.

For this step we run bcftools consensus (Li et al. 2009).

Question

Inspect the bcftools consensus output for Barcode11 tag

1. How many sequences did we get for the sample? What are they?
2. Why?

Solution

1. We got 2 sequences: the complete genome and the complete plasmid genome.
2. The tool uses the reference genome and the variants found to build the consensus genome of the sample, and the reference genome FASTA file we use includes two sequences a complete one and a plasmid complete one. So the tool constructed both sequences for us to choose from, based on our further analysis.

Pathogen Tracking among all samples

Comment

If you did not get your Gene based pathogenic identification section output files needed yet or you got an error for some reason, you can go on and download them all or the ones missing from Zenodo so you can start this workflow, please don’t forget to create the collections for them as explained in the pervious hands-on.

Hands-on: Optional Data upload

1. Import all tabular and FASTA files needed for this section via link from Zenodo to the new created history:

   https://zenodo.org/record/7593928/files/VFs_Barcode10.tabular
   https://zenodo.org/record/7593928/files/VFs_Barcode11.tabular
   https://zenodo.org/record/7593928/files/VFs_accessions_with_SampleID_Barcode10.tabular
   https://zenodo.org/record/7593928/files/VFs_accessions_with_SampleID_Barcode11.tabular
   https://zenodo.org/record/7593928/files/VFs_accessions_Barcode10.tabular
   https://zenodo.org/record/7593928/files/VFs_accessions_Barcode11.tabular
   https://zenodo.org/record/7593928/files/Contigs_Barcode10.fasta
   https://zenodo.org/record/7593928/files/Contigs_Barcode11.fasta
   

   Tip: Importing via links

   • Copy the link location

   • Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)

   • Select Paste/Fetch Data

   • Paste the link(s) into the text field

   • Press Start

   • Close the window

In this last section, we would like to show how to aggregate results and use the results to help tracking pathogenes among samples by:

1. Drawing a presence-absence heatmap of the identified VF genes within all samples to visualize in which samples these genes can be found.
2. Drawing a phylogenetic tree for each pathogenic gene detected, where we will relate the contigs of the samples together where this gene is found.

With these two types of visualizations we can have an overview of all samples and the genes, but also how samples are related to each other, which common pathogenic genes they share. Given the time of the sampling and the location one can easily identify using these graphs, where and when the contamination has occurred among the different samples.

Hands-on: Organize data

1. Create a collection named VFs with VFs files for both tags

   Tip: Creating a dataset collection

   • Click on galaxy-selector Select Items at the top of the history panel
   • Check all the datasets in your history you would like to include

   • Click n of N selected and choose Build Dataset List

     

   • Enter a name for your collection
   • Click Create List to build your collection
   • Click on the checkmark icon at the top of your history again

2. Create a collection named VFs accessions with VFs accessions files for both tags

3. Create a collection named VFs accessions with SampleID with VFs accessions with SampleID files

4. Create a collection named Contigs with Contigs files

Heatmap

A heatmap is one of the visualization techniques that can give you a complete overview of all the samples together and whether or not a certain value exists. In this tutorial, we use the heatmap to visualize all samples aside and check which common bacteria pathogen genes are found in samples and which is only found in one of them.

We use Heatmap w ggplot tool along with other tabular manipulating tools to prepare the tabular files.

Question

Now let’s see how your heatmap looks like, you can zoom-in and out in your Galaxy history.

1. Mention three of the common bacterial pathogen genes found in both samples.
2. How can the differences in the found VF bacteria pathogen genes between the two samples be interpreted?

Solution

1. A lot of bacteria pathogen VF gene products identified by the VFDB are common in both samples, three of them are with the following accession number: NP_461810, NP_461809 and NP_459541
2. AAG03023 is only found in Barcode10 sample and NP_460360 is only found in Barcode11 sample

3. Both samples were spiked with the same pathogen species, S. enterica, but not the same strain:

   • Barcode10_Spike2 sample is spiked with S. enterica subsp. enterica strain
   • Barcode11_Spike2b sample is spiked with S. enterica subsp. houtenae strain.

   This can be the main cause of the big similarities and the few differences of the bacteria pathogen VF gene products found between both of the two samples. Other factors such as the time and location of the sampling may cause other differences. By knowing the metadata of the samples inputted for the workflows in real life we can understand what actually happened. We can have samples with no pathogen found then we start detecting genes from the 7th or 8th sample, then we can identify where and when the pathogen entered the host, and stop the cause of that

Phylogenetic Tree building

Phylogenetic trees are nice ways to track all the bacterial pathogen genes one by one in all samples. So we will have a tree for every found gene, this tree will indicate which contigs the gene is found in all samples and relating these contigs together based on their topology. With such trees we can identify on which location of the sequences from the sample genome the pathogentic gene is found and whether it is at the same or a different location as the other samples. We can also see how many samples have these genes in common and accordingly track our contamination.

For the phylogenetic trees, for each bacteria pathogen gene found in the samples we use ClustalW (Larkin et al. 2007) for Multiple Sequence Alignment (MSA) needed before constructing a phylogenetic tree, for the tree itself we use FASTTREE and Newick Display (Dress et al. 2008) to visualize it.

Question

Now let’s see how your trees for the bacteria pathogen gene with accession IDs: AAF98391, NP_249099 and NP_460111 look like. To access that go to the output of Newick

1. In which samples and contigs is gene BAA94855 found?
2. In which samples and contigs is gene NP_249099 found?
3. In which samples and contigs is gene NP_461819 found?

Solution

1. In the Barcode10 sample only: Contigs 109 and 66

   

2. In the Barcode11 sample only: Contig 2

   

3. In both samples; Contig 121 of Barcode10 and Contig 1 of Barcode11

   

Conclusion

In this tutorial, we have tried the workflow designed to detect and track pathogens in our food and drinks. Through out the full workflow we used our Nanopore sequenced datasets from Biolytix and analyzed it, found the pathogens and tracked it. This approach can be summarized with the following scheme:

Frequently Asked Questions

Useful literature

Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.

References

1. Larkin, M. A., G. Blackshields, N. P. Brown, R. Chenna, P. A. McGettigan et al., 2007 Clustal W and Clustal X version 2.0. Bioinformatics 23: 2947–2948. 10.1093/bioinformatics/btm404
2. Dress, A. W. M., C. Flamm, G. Fritzsch, S. Gr\~ A1\textfractionsolidus4newald, M. Kruspe et al., 2008 Noisy: Identification of problematic columns in multiple sequence alignments. Algorithms for Molecular Biology 3: 7. 10.1186/1748-7188-3-7
3. Li, H., B. Handsaker, A. Wysoker, T. Fennell, J. Ruan et al., 2009 The Sequence Alignment/Map format and SAMtools. Bioinformatics 25: 2078–2079. 10.1093/bioinformatics/btp352
4. Multilocus Sequence Typing as a Replacement for Serotyping in Salmonella enterica, 2012 PLOS Pathogens 8: e1002776. https://journals.plos.org/plospathogens/article/file?id=10.1371/journal.ppat.1002776&type=printable. 10.1371/journal.ppat.1002776 https://app.dimensions.ai/details/publication/pub.1037561963
5. Cingolani, P., V. M. Patel, M. Coon, T. Nguyen, S. J. Land et al., 2012 Using Drosophila melanogaster as a Model for Genotoxic Chemical Mutational Studies with a New Program, SnpSift. Frontiers in Genetics 3: 10.3389/fgene.2012.00035
6. Cock, P. J. A., B. A. Grüning, K. Paszkiewicz, and L. Pritchard, 2013 Galaxy tools and workflows for sequence analysis with applications in molecular plant pathology. PeerJ 1: e167. 10.7717/peerj.167
7. Wood, D. E., and S. L. Salzberg, 2014 Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biology 15: R46. 10.1186/gb-2014-15-3-r46
8. Wick, R. R., M. B. Schultz, J. Zobel, and K. E. Holt, 2015 Bandage: interactive visualization ofde novogenome assemblies: Fig. 1. Bioinformatics 31: 3350–3352. 10.1093/bioinformatics/btv383
9. Ewels, P., M. Magnusson, M. Käller, and S. Lundin, 2016 MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32: 3047–3048. 10.1093/bioinformatics/btw354
10. Li, H., 2017 Minimap2: fast pairwise alignment for long nucleotide sequences. https://arxiv.org/abs/1708.01492. 10.1093/bioinformatics/bty191
11. Chen, C.-H., T. Xiao, H. Xu, P. Jiang, C. A. Meyer et al., 2018 Improved design and analysis of CRISPR knockout screens (J. Wren, Ed.). Bioinformatics 34: 4095–4101. 10.1093/bioinformatics/bty450
12. Johnson, J. E., P. Kumar, C. Easterly, M. Esler, S. Mehta et al., 2018 Improve your Galaxy text life: The Query Tabular Tool. F1000Research 7: 1604. 10.12688/f1000research.16450.2
13. Kolmogorov, M., D. Bickhart, B. Behsaz, A. Gurevich, M. Rayko et al., 2020 metaFlye: scalable long-read metagenome assembly using repeat graphs. Nature Methods 17: 1–8. 10.1038/s41592-020-00971-x
14. Ciuffreda, L., H. Rodríguez-Pérez, and C. Flores, 2021 Nanopore sequencing and its application to the study of microbial communities. Computational and Structural Biotechnology Journal 19: 1497–1511. https://doi.org/10.1016/j.csbj.2021.02.020 https://www.sciencedirect.com/science/article/pii/S2001037021000714
15. Zheng, Z., S. Li, J. Su, A. W.-S. Leung, T.-W. Lam et al., 2021 Symphonizing pileup and full-alignment for deep learning-based long-read variant calling. 10.1038/s43588-022-00387-x

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Citing this Tutorial

1. Bérénice Batut, Engy Nasr, Pathogen detection from (direct Nanopore) sequencing data using Galaxy - Foodborne Edition (Galaxy Training Materials). http://0.0.0.0:4000/training-material/topics/metagenomics/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.html Online; accessed TODAY
2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{metagenomics-pathogen-detection-from-nanopore-foodborne-data,
author = "Bérénice Batut and Engy Nasr",
title = "Pathogen detection from (direct Nanopore) sequencing data using Galaxy - Foodborne Edition (Galaxy Training Materials)",
year = "",
month = "",
day = ""
url = "\url{http://0.0.0.0:4000/training-material/topics/metagenomics/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
	doi = {10.1371/journal.pcbi.1010752},
	url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
	year = 2023,
	month = {jan},
	publisher = {Public Library of Science ({PLoS})},
	volume = {19},
	number = {1},
	pages = {e1010752},
	author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
	editor = {Francis Ouellette},
	title = {Galaxy Training: A powerful framework for teaching!},
	journal = {PLoS Comput Biol} Computational Biology}
}

                   

Galaxy Administrators: Install the missing tools

You can use Ephemeris's shed-tools install command to install the tools used in this tutorial.

shed-tools install [-g GALAXY] [-a API_KEY] -t <(curl http://localhost:4000//training-material//api/topics/metagenomics/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

--- {}