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Sample identification with Kraken

To identify a sample from sequencing reads, we can use the tool “Kraken”. This tool can also be used to identify members in a mixed set of reads, for metagenomics.

  • e.g. reads from one sample → Kraken → 95% Staphylococcus aureus.

  • e.g. mixed reads → Kraken → 50% Staphylococcus aureus, 40% Campylobacter concisus, 10% unclassified.

In this tutorial we will use Kraken to confirm the identify of reads from a bacterial isolate.

Get data

In Galaxy, go to Shared Data in the top panel.

  • Click on Data Libraries
  • Click on Kraken data
  • Tick the box next to each of these files
  • Click the to History button
  • Under create new type Kraken

  • Click Import

  • Your current history should now contain four files.

galaxy history

Run Kraken

We have a sample that should be Staphylococcus aureus. The paired-end FASTQ read files are:

  • staph_R1.fq and staph_R2.fq.

(We will look at the other set of files later on in the tutorial).

  • Go to Tools → NGS Analysis → Metagenomic analyses → Kraken, assign taxonomic labels to sequencing reads

  • Set the following parameters:

    • Single or paired reads: Paired
    • Forward strand: staph_R1.fq
    • Reverse strand: staph_R2.fq
    • Select a Kraken database: choose minikraken_20171013_4GB
    • leave other settings as they are
    • Click Execute

Examine the output

The output is a file called Kraken on data x and x: Classification. This will be at the top of your history pane.

Click Refresh if the file hasn’t yet turned green.

refresh

When the file is green, click on the eye icon to view.

  • We will turn this output into something easier to read in the next step.
  • Column 2 is the sequence ID.
  • Column 3 is the taxon ID (from NCBI).
  • Column 5 is a summary of all the taxon IDs that each k-mer in the sequence matched to (taxon ID:number of k-mers).
  • Your file may look a little different to this example image.

classification

Kraken report

Go to Tools → NGS Analysis → Metagenomic analyses → Kraken-report

  • Set the following parameters:

    • Kraken output: Kraken on data x and x: Classification
    • Select a Kraken database: choose minikraken_20171013_4GB
    • Click Execute

The output file is called Kraken-report on data x.

  • Click on the eye icon to view.
  • Column 1: percentage of reads in the clade/taxon in Column 6
  • Column 2: number of reads in the clade.
  • Column 3: number of reads in the clade but not further classified.
  • Column 4: code indicating the rank of the classification: (U)nclassified, (D)omain, (K)ingdom, (P)hylum, (C)lass, (O)rder, (F)amily, (G)enus, (S)pecies).
  • Column 5: NCBI taxonomy ID.

kraken report

Approximately 88% of reads were classified as Staphylococcus aureus, confirming the correct identity of our bacterial sample.

The remaining reads within the S. aureus clade were classified into various taxa.

  • Scroll down column 3 to see the number of reads assigned directly to the taxon in column 6.
  • These are all very low and can be disregarded.

Next

Re-run Kraken with another sample. This sample should be Enterococcus faecalis.

  • Use the files ent_R1.fq and ent_R2.fq.
  • Run Kraken with these files. These are paired-end reads.
  • With the Classification file from Kraken, run Kraken-report.
  • Cick on the eye icon to view the Kraken-report file.

output 1

  • 63% are classified to the genus Enterococcus, and most of these to E. faecalis.

  • (Our results are probably slightly different to the example image.)

  • However, if we scroll down the table of results, we see that 31% are classified to the genus Mycobacterium, mostly M. abscessus. These are not in the same phylum as Enterococcus.

output2

  • This sample is probably contaminated.

Kraken paper

Kraken software