Checking expected species and contamination in bacterial isolate

Confirmation of the expected bacterial species and detection of possible contamination in isolate sequencing data.

When sequencing a genome, contamination from foreign organisms may be inadvertently mixed with the genomic material of the species of interest.
For this reason, when working with bacterial isolates, it is crucial to verify that the expected species or strains are present in the data and to identify any potential contamination.

Confirming species identity and detecting contamination are essential to ensure the accuracy and reliability of the analysis, and to prevent misleading results that could affect downstream interpretations.

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Galaxy and data preparation

To illustrate the process of checking the expected species and potential contamination in a bacterial isolate,
we will use the sequencing reads filtered with fastp in the assembly tutorial.

Before starting the analysis, prepare your Galaxy workspace as follows:

1. Create a new Galaxy history and give it a meaningful name.
2. Import the paired-end reads filtered with fastp by dragging and dropping them from the previous history (see here for instructions on managing and copying datasets between histories).

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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

k-mer based taxonomic assignment

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.

1. examines the k-mers within the query sequence,

2. searches for them in the database,

3. looks for where these are placed within the taxonomy tree inside the database,

4. makes the classification with the most probable position,

5. then maps k-mers to the lowest common ancestor (LCA) of all genomes known to contain the given k-mer.

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

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Assign taxonomic labels with Kraken2

1. Kraken2 Tool with the following parameters:
   • Single or paired reads: Paired Collection
     • Collection of paired reads: Input paired collection

   • Confidence: 0.1

   • Minimum Base Quality: 10

   • In Create Report:
     • Print a report with aggregrate counts/clade to file: Yes
   • Select a Kraken2 database: Prebuilt Refseq indexes: PlusPF

A confidence score of 0.1 means that at least 10% of the k-mers should match entries in the database. This value can be reduced if a less restrictive taxonomic assignation is desired.

Kraken2 will create two outputs for each dataset: Classification and Report

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Classification

• 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

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

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Report

• 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
     • (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

Question

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

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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 (Bayesian Reestimation of Abundance after Classification with Kraken).

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

1. Bracken Tool with the following parameters:
   • Kraken report file: Report output of Kraken
   • Select a kmer distribution: PlusPF (same as for Kraken)
   • Level: Species
   • Produce Kraken-Style Bracken report: yes

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

Bracken output file format (tabular):

• 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?

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Contamination identification

To explore Kraken report and specially to detect more reliably minority organisms or contamination, we will use Recentrifuge

1. Recentrifuge with the following parameters:
   • Select taxonomy file tabular formated: classification 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:

1. A statistic table with general statistics about assignations

   

2. A data table with a report for each taxa

   

3. 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?

NOTE! There is no sign of a possible contamination. Most sequences are classified to taxon on the Staphylococcus aureus taxonomy. Only 4% of the sequences are not classified to Staphylococcus.

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Visualization of taxonomic assignment

Getting an overview of the assignation is not straightforward with the Kraken2 outputs directly. Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data. There are several tools for that:

• Krona
• Pavian

Visualisation using Krona

Krona creates an interactive HTML file allowing hierarchical data to be explored with zooming, multi-layered pie charts.

With this tool, we can easily visualize the composition of the bacterial communities and compare how the populations of microorganisms are modified according to the conditions of the environment.

Convert Kraken report file

Kraken outputs can not be given directly to Krona, they first need to be converted.

Krakentools is a suite of tools to work on Kraken outputs. It include a tool designed to translate results of the Kraken metagenomic classifier to the full representation of NCBI taxonomy. The output of this tool can be directly visualized by the Krona tool.

1. Krakentools: Convert kraken report file Tool with the following parameters:
   • Kraken report file: Report collection of Kraken
2. Inspect the generated output

Generate Krona visualisation

Let’s now run Krona

1. Krona pie chart Tool with the following parameters:
   • Type of input data: Tabular
   • Input file: output of Krakentools
2. Inspect the generated file

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Visualization using Pavian

Pavian (pathogen visualization and more) is an interactive visualization tool for metagenomic data. It was developed for the clinical metagenomic problem to find a disease-causing pathogen in a patient sample, but it is useful to analyze and visualize any kind of metagenomics data.

1. Download Report files from Report collection of Kraken
2. Upload Report files on Pavian server: https://fbreitwieser.shinyapps.io/pavian/
3. Click on Save table
4. Click on Result Overview. This page shows the summary of the classifications in the selected sample set:

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