Sequencing (determining of DNA/RNA nucleotide sequence) is used all over the world for all kinds of analysis. The product of these sequencers are reads, which are sequences of detected nucleotides.

When sequencing bacterial isolates, it might be good to check if the expected species or strains can be identified in the data or if there is any contamination.

To illustrate the process, we take quality-controlled data of a bacterial genome (KUN1163 sample) from sequencing data produced in “Complete Genome Sequences of Eight Methicillin-Resistant Staphylococcus aureus Strains Isolated from Patients in Japan” (Hikichi et al. 2019).

Methicillin-resistant Staphylococcus aureus (MRSA) is a major pathogen causing nosocomial infections, and the clinical manifestations of MRSA range from asymptomatic colonization of the nasal mucosa to soft tissue infection to fulminant invasive disease. Here, we report the complete genome sequences of eight MRSA strains isolated from patients in Japan.

Agenda

In this tutorial, we will cover:

1. Galaxy and data preparation
2. Taxonomic profiling
3. Species extraction
4. Contamination identification
5. Conclusion

Galaxy and data preparation

Any analysis should get its own Galaxy history. So let’s start by creating a new one and get the data (forward and reverse quality-controlled sequences) into it.

Hands-on: Prepare Galaxy and data

1. Create a new history for this analysis

   Tip: Creating a new history

   To create a new history simply click the new-history icon at the top of the history panel:

   

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
   4. To cancel renaming, click the galaxy-undo “Cancel” button

   If you do not have the galaxy-pencil (Edit) next to the history name (which can be the case if you are using an older version of Galaxy) do the following:

   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

3. Import the quality-controlled sequences from Zenodo or from Galaxy shared data libraries:

   https://zenodo.org/record/10572227/files/DRR187559_after_fastp_1.fastq.gz
   https://zenodo.org/record/10572227/files/DRR187559_after_fastp_2.fastq.gz
   

   Tip: Importing via links

   • Copy the link location

   • Click galaxy-upload Upload Data at the top of the tool panel

   • Select galaxy-wf-edit 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 Data (top panel) then Data libraries
   2. Navigate to the correct folder as indicated by your instructor.
      • On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
   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

Taxonomic profiling

To find out which microorganisms are present, we will compare the reads of the sample to a reference database, i.e. sequences of known microorganisms stored in a database, using Kraken2 (Wood et al. 2019).

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.

For this tutorial, we will use the PlusPF database which contains the Standard (archaea, bacteria, viral, plasmid, human, UniVec_Core), protozoa and fungi data.

Hands-on: Assign taxonomic labels with Kraken2

1. Kraken2 ( Galaxy version 2.1.1+galaxy1) with the following parameters:
   • “Single or paired reads”: Paired
     • param-file “Forward strand”: DRR187559_after_fastp_1
     • param-file “Reverse strand”: DRR187559_after_fastp_2
   • “Minimum Base Quality”: 10
   • In “Create Report”:
     • “Print a report with aggregrate counts/clade to file”: Yes
   • “Select a Kraken2 database”: PlusPF-16

• Classification: tabular files with one line for each sequence classified by Kraken and 5 columns:

  1. C/U: a one letter indicating if the sequence classified or unclassified
  2. Sequence ID as in the input file
  3. NCBI taxonomy ID assigned to the sequence, or 0 if unclassified
  4. Length of sequence in bp (read1|read2 for paired reads)

  5. A space-delimited list indicating the lowest common ancestor (LCA) mapping of each k-mer in the sequence

     For example, 562:13 561:4 A:31 0:1 562:3 would indicate that:

     1. The first 13 k-mers mapped to taxonomy ID #562
     2. The next 4 k-mers mapped to taxonomy ID #561
     3. The next 31 k-mers contained an ambiguous nucleotide
     4. The next k-mer was not in the database
     5. The last 3 k-mers mapped to taxonomy ID #562

     |:| indicates end of first read, start of second read for paired reads

   Column 1	Column 2	Column 3	Column 4	Column 5
   C 	DRR187559.1 	1280 	164|85 	0:1 1279:1 0:41 1279:10 0:5 1280:5 1279:1 0:1 1279:1 0:7 1279:5 0:2 1279:6 0:12 1279:5 0:19 1279:2 0:6 |:| 0:39 1279:2 0:6 1279:3 0:1
   C 	DRR187559.2 	1280 	70|198 	0:2 1279:5 0:29 |:| 0:52 1279:5 0:13 1279:3 0:23 1279:2 0:45 1280:1 0:9 1280:3 A:8
   C 	DRR187559.3 	1279 	106|73 	0:4 1279:3 0:36 1279:4 0:10 1279:5 0:3 1279:5 0:2 |:| 0:39
   C 	DRR187559.4 	1279 	121|189 	1279:6 0:17 1279:4 0:28 1279:2 0:30 |:| 0:7 1279:5 0:19 1279:5 0:20 1279:1 0:8 1279:1 0:1 1279:6 0:25 1279:2 0:44 A:11
   C 	DRR187559.5 	1279 	68|150 	1279:2 0:20 1279:3 0:9 |:| 0:10 1279:3 0:24 1279:2 0:9 1279:5 0:21 1279:5 0:20 1279:5 0:9 1279:1 0:2
   C 	DRR187559.6 	1280 	137|246 	0:2 1280:5 0:28 1279:1 0:28 1279:2 0:8 1279:2 0:23 1279:1 0:3 |:| 1279:1 0:2 1279:3 0:61 1279:2 0:14 1279:2 0:97 A:30 
  

  Question

  1. Is the first sequence in the file classified or unclassified?
  2. What is the taxonomy ID assigned to the first classified sequence?
  3. What is the corresponding taxon?

  Solution

  1. classified
  2. 1280
  3. 1280 corresponds to Staphylococcus aureus .

• Report: tabular files with one line per taxon and 6 columns or fields

  1. Percentage of fragments covered by the clade rooted at this taxon
  2. Number of fragments covered by the clade rooted at this taxon
  3. Number of fragments assigned directly to this taxon
  4. A rank code, indicating
     • (U)nclassified
     • (R)oot
     • (D)omain
     • (K)ingdom
     • (P)hylum
     • (C)lass
     • (O)rder
     • (F)amily
     • (G)enus, or
     • (S)pecies

     Taxa that are not at any of these 10 ranks have a rank code that is formed by using the rank code of the closest ancestor rank with a number indicating the distance from that rank. E.g., G2 is a rank code indicating a taxon is between genus and species and the grandparent taxon is at the genus rank.

  5. NCBI taxonomic ID number
  6. Indented scientific name

   Column 1	Column 2	Column 3	Column 4	Column 5	Column 6
   0.24 	1065 	1065 	U 	0 	unclassified
   99.76 	450716 	14873 	R 	1 	root
   96.44 	435695 	2 	R1 	131567 	cellular organisms
   96.43 	435675 	3889 	D 	2 	Bacteria
   95.56 	431709 	78 	D1 	1783272 	Terrabacteria group
   95.53 	431578 	163 	P 	1239 	Firmicutes
   95.49 	431390 	4625 	C 	91061 	Bacilli
   94.38 	426383 	1436 	O 	1385 	Bacillales
   94.04 	424874 	2689 	F 	90964 	Staphylococcaceae
   93.44 	422124 	234829 	G 	1279 	Staphylococcus 
  

  Question

  1. How many taxa have been found?
  2. What are the percentage on unclassified?
  3. What are the domains found?

  Solution

  1. 621, as the number of lines
  2. 0.24%
  3. Only Bacteria

Species extraction

In Kraken output, there are quite a lot of identified taxa with different levels. To extract the species level, we will use Bracken.

Bracken (Bayesian Reestimation of Abundance after Classification with Kraken) is a “simple and worthwile addition to Kraken for better abundance estimates” (Ye et al. 2019). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: “Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species” (Lu et al. 2017).

Hands-on: Extract species with Bracken

1. Bracken ( Galaxy version 2.9+galaxy0) with the following parameters:
   • param-collection “Kraken report file”: Report output of Kraken

   • “Select a kmer distribution”: PlusPF, same as for Kraken

     It is important to choose the same database that you also chose for Kraken2

   • “Level”: Species

Bracken generates one output as a table with 7 columns:

• Taxon name
• Taxonomy ID
• Level ID (S=Species, G=Genus, O=Order, F=Family, P=Phylum, K=Kingdom)
• Kraken assigned reads
• Added reads with abundance re-estimation
• Total reads after abundance re-estimation
• Fraction of total reads

Question

1. How many species have been found?
2. Which the species has been the most identified? And in which proportion?
3. What are the other species?

Solution

1. 51 (52 lines including 1 line with header)
2. Staphylococcus aureus with 95.5% of the reads.
3. Most of the other species are from Staphylococcus genus, so same as Staphylococcus aureus. The other species in really low proportion.

As expected Staphylococcus aureus represents most of the reads in the data.

Contamination identification

To explore Kraken report and specially to detect more reliably minority organisms or contamination, we will use Recentrifuge (Martı́ Jose Manuel 2019).

Hands-on: Identify contamination

1. Recentrifuge ( Galaxy version 1.12.1+galaxy0) with the following parameters:
   • param-file “Select taxonomy file tabular formated”: report of Kraken2 tool
   • “Type of input file (Centrifuge, CLARK, Generic, Kraken, LMAT)”: Kraken
   • In “Database type”:
     • “Cached database whith taxa ID”: latest
   • In “Output options”:
     • “Type of extra output to be generated (default on CSV)”: TSV
     • “Remove the log file”: Yes
   • In ” Fine tuning of algorithm parameters”:
     • “Strain level instead of species as the resolution limit for the robust contamination removal algorithm; use with caution, this is an experimental feature”: Yes

Recentrifuge generates 3 outputs:

• A statistic table with general statistics about assignations

  Question

  1. How many sequences have been used?
  2. How many sequences have been classified?

  Solution

  1. 451,780
  2. 450,715

• A data table with a report for each taxa

  Question

  1. How many taxa have been kept?
  2. What is the lowest level in the data?

  Solution

  1. 185 (189 lines including 3 header lines)
  2. The lowest level is strain.

• A HTML report with a Krona chart

  Question

  1. What is the percentage of classified sequences?
  2. When clicking on Staphylococcus aureus, what can we say about the strains?
  3. Is there any contamination?

  Solution

  1. 99.8%
  2. 99% of sequences assigned to Staphylococcus aureus are not assigned to any strains, probably because they are too similar to several strains. Staphylococcus aureus subsp. aureus JKD6159 is the strain with the most classified sequences, but only 0.3% of the sequences assigned to Staphylococcus aureus.
  3. There is no sign of a possible contamination. Most sequences are classified to taxon on the Staphylococcus aureus taxonomy. Only 3% of the sequences are not classified to Staphylococcus.

Conclusion

In this tutorial, we checked bacterial isolate sequencing data for expected species and potential contamination.

You've Finished the Tutorial

Please also consider filling out the Feedback Form as well!

Key points

• Kraken assigns taxons to sequences

• Bracken extracts species from Kraken assignations

• Recentrifuge extracts stats and creates visualization from Kraken report

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. 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
2. Lu, J., F. P. Breitwieser, P. Thielen, and S. L. Salzberg, 2017 Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science 3: e104. 10.7717/peerj-cs.104
3. Hikichi, M., M. Nagao, K. Murase, C. Aikawa, T. Nozawa et al., 2019 Complete Genome Sequences of Eight Methicillin-Resistant Staphylococcus aureus Strains Isolated from Patients in Japan (I. L. G. Newton, Ed.). Microbiology Resource Announcements 8: 10.1128/mra.01212-19
4. Martı́ Jose Manuel, 2019 Recentrifuge: Robust comparative analysis and contamination removal for metagenomics. PLoS computational biology 15: e1006967. 10.1371/journal.pcbi.100696
5. Wood, D. E., J. Lu, and B. Langmead, 2019 Improved metagenomic analysis with Kraken 2. Genome biology 20: 1–13. 10.1186/s13059-019-1891-0
6. Ye, S. H., K. J. Siddle, D. J. Park, and P. C. Sabeti, 2019 Benchmarking Metagenomics Tools for Taxonomic Classification. Cell 178: 779–794. 10.1016/j.cell.2019.07.010

Feedback

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Citing this Tutorial

1. Bérénice Batut, Checking expected species and contamination in bacterial isolate (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/ecology/tutorials/bacterial-isolate-species-contamination-checking/tutorial.html Online; accessed TODAY
2. Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
3. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012

BibTeX

@misc{ecology-bacterial-isolate-species-contamination-checking,
author = "Bérénice Batut",
	title = "Checking expected species and contamination in bacterial isolate (Galaxy Training Materials)",
	year = "",
	month = "",
	day = ""
	url = "\url{https://training.galaxyproject.org/training-material/topics/ecology/tutorials/bacterial-isolate-species-contamination-checking/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}
}

                   

Funding

These individuals or organisations provided funding support for the development of this resource

ABRomics

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 https://training.galaxyproject.org/training-material/api/topics/ecology/tutorials/bacterial-isolate-species-contamination-checking/tutorial.json | jq .admin_install_yaml -r)

Alternatively you can copy and paste the following YAML

---
install_tool_dependencies: true
install_repository_dependencies: true
install_resolver_dependencies: true
tools:
- name: bracken
  owner: iuc
  revisions: 1d4bd12f01cf
  tool_panel_section_label: Metagenomic Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: kraken2
  owner: iuc
  revisions: 20e2f64aa1fe
  tool_panel_section_label: Metagenomic Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/
- name: recentrifuge
  owner: iuc
  revisions: b5407cc2bf51
  tool_panel_section_label: Metagenomic Analysis
  tool_shed_url: https://toolshed.g2.bx.psu.edu/

Feedback

5 stars	1

March 2024

• 5 stars: Liked: I found the tutorial very instructive, but special kudos to the contamination identification section with the Recentrifuge interactive plot. Many thanks! Disliked: When clicking on the Recentrifuge reference pointing to the paper, I see the DOI appears incomplete: it should be 10.1371/journal.pcbi.1006967 (the last number is missing on the tutorial).